Datasets:

ftopal
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huggingface-models-embeddings

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train · 241k rows

sha null	last_modified null	library_name stringclasses 154 values	text stringlengths 1 900k	metadata stringlengths 2 348k	pipeline_tag stringclasses 45 values	id stringlengths 5 122	tags sequencelengths 1 1.84k	created_at stringlengths 25 25	arxiv sequencelengths 0 201	languages sequencelengths 0 1.83k	tags_str stringlengths 17 9.34k	text_str stringlengths 0 389k	text_lists sequencelengths 0 722	processed_texts sequencelengths 1 723	tokens_length sequencelengths 1 723	input_texts sequencelengths 1 61	embeddings sequencelengths 768 768
null	null	null	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-1.8B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-1.8B-Chat](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/second-state/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-1.8B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-1.8B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Qwen1.5-1.8B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q2_K.gguf) \| Q2_K \| 2 \| 863 MB\| smallest, significant quality loss - not recommended for most purposes \| \| [Qwen1.5-1.8B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 1.06 GB\| small, substantial quality loss \| \| [Qwen1.5-1.8B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 1.02 GB\| very small, high quality loss \| \| [Qwen1.5-1.8B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 970 MB\| very small, high quality loss \| \| [Qwen1.5-1.8B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q4_0.gguf) \| Q4_0 \| 4 \| 1.12 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Qwen1.5-1.8B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 1.22 GB\| medium, balanced quality - recommended \| \| [Qwen1.5-1.8B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 1.16 GB\| small, greater quality loss \| \| [Qwen1.5-1.8B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q5_0.gguf) \| Q5_0 \| 5 \| 1.31 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Qwen1.5-1.8B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 1.38 GB\| large, very low quality loss - recommended \| \| [Qwen1.5-1.8B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 1.33 GB\| large, low quality loss - recommended \| \| [Qwen1.5-1.8B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q6_K.gguf) \| Q6_K \| 6 \| 1.58 GB\| very large, extremely low quality loss \| \| [Qwen1.5-1.8B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q8_0.gguf) \| Q8_0 \| 8 \| 1.96 GB\| very large, extremely low quality loss - not recommended \|	{"language": ["en"], "license": "other", "tags": ["chat"], "model_name": "Qwen1.5 1.8B Chat", "base_model": "Qwen/Qwen1.5-1.8B-Chat", "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/blob/main/LICENSE", "model_creator": "Qwen", "quantized_by": "Second State Inc.", "pipeline_tag": "text-generation"}	text-generation	second-state/Qwen1.5-1.8B-Chat-GGUF	[ "gguf", "chat", "text-generation", "en", "base_model:Qwen/Qwen1.5-1.8B-Chat", "license:other", "region:us" ]	2024-02-06T04:33:23+00:00	[]	[ "en" ]	TAGS #gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-1.8B-Chat #license-other #region-us	![](URL style=) --- Qwen1.5-1.8B-Chat-GGUF ====================== Original Model -------------- Qwen/Qwen1.5-1.8B-Chat Run with LlamaEdge ------------------ * LlamaEdge version: v0.2.15 and above * Prompt template + Prompt type: 'chatml' + Prompt string * Run as LlamaEdge service * Run as LlamaEdge command app Quantized GGUF Models ---------------------	[]	[ "TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-1.8B-Chat #license-other #region-us \n" ]	[ 39 ]	[ "passage: TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-1.8B-Chat #license-other #region-us \n" ]	[ -0.012159162200987339, -0.002384505234658718, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-bn-adapter-3.17M-snli-model1 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7500 - Accuracy: 0.736 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 83 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.412 \| 1.0 \| 8584 \| 0.3365 \| 0.8770 \| \| 0.3706 \| 2.0 \| 17168 \| 0.3072 \| 0.8872 \| \| 0.3597 \| 3.0 \| 25752 \| 0.2988 \| 0.8888 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-large-cased", "model-index": [{"name": "bert-large-cased-bn-adapter-3.17M-snli-model1", "results": []}]}	null	varun-v-rao/bert-large-cased-bn-adapter-3.17M-snli-model1	[ "tensorboard", "generated_from_trainer", "base_model:bert-large-cased", "license:apache-2.0", "region:us" ]	2024-02-06T04:34:08+00:00	[]	[]	TAGS #tensorboard #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #region-us	bert-large-cased-bn-adapter-3.17M-snli-model1 ============================================= This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7500 * Accuracy: 0.736 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 83 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 83\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#tensorboard #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 83\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 37, 98, 4, 33 ]	[ "passage: TAGS\n#tensorboard #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 83\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.09240599721670151, 0.008305436931550503, -0.0009757168008945882, 0.10874024778604507, 0.1635274738073349, 0.03409658372402191, 0.14058178663253784, 0.0902060866355896, -0.12397259473800659, 0.019144149497151375, 0.10259444266557693, 0.10818060487508774, 0.009139712899923325, 0.07315446436405182, -0.022586921229958534, -0.2367015928030014, -0.011283166706562042, 0.03786800801753998, -0.12947320938110352, 0.11912231892347336, 0.0885123759508133, -0.1360803246498108, 0.07304488867521286, 0.0074118925258517265, -0.25938722491264343, 0.04532434791326523, 0.01284931693226099, -0.05351780727505684, 0.13299542665481567, -0.0026367197278887033, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-bn-adapter-895K-snli-model1 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7623 - Accuracy: 0.728 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 61 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.4254 \| 1.0 \| 8584 \| 0.3365 \| 0.8722 \| \| 0.4021 \| 2.0 \| 17168 \| 0.3165 \| 0.8790 \| \| 0.3806 \| 3.0 \| 25752 \| 0.3115 \| 0.8817 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-bn-adapter-895K-snli-model1", "results": []}]}	null	varun-v-rao/roberta-base-bn-adapter-895K-snli-model1	[ "tensorboard", "generated_from_trainer", "base_model:roberta-base", "license:mit", "region:us" ]	2024-02-06T04:35:02+00:00	[]	[]	TAGS #tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us	roberta-base-bn-adapter-895K-snli-model1 ======================================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7623 * Accuracy: 0.728 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 61 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 61\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 61\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 31, 98, 4, 33 ]	[ "passage: TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 61\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.07504717260599136, -0.02196611650288105, -0.0010848628589883447, 0.11662556231021881, 0.19939157366752625, 0.03200864791870117, 0.1486678421497345, 0.06581471115350723, -0.129006028175354, 0.013607422821223736, 0.10531160980463028, 0.1320256143808365, -0.01142910122871399, 0.055550456047058105, -0.021502450108528137, -0.26036733388900757, -0.030642813071608543, 0.03408229723572731, -0.14363013207912445, 0.12668326497077942, 0.08404766023159027, -0.14420217275619507, 0.07034140825271606, -0.0012516307178884745, -0.3223230242729187, 0.053184445947408676, 0.0346849150955677, -0.041734255850315094, 0.14334385097026825, -0.008378831669688225, 0.1929733157157898, 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null	null	diffusers	# LoRA DreamBooth - nick1221/outputs These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a photo of robot dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. ![img_0](./image_0.png) ![img_1](./image_1.png) ![img_2](./image_2.png) ![img_3](./image_3.png) LoRA for the text encoder was enabled: False.	{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "lora"], "base_model": "stabilityai/stable-diffusion-2-1", "instance_prompt": "a photo of robot dog", "inference": true}	text-to-image	nick1221/outputs	[ "diffusers", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "lora", "base_model:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "region:us" ]	2024-02-06T04:35:35+00:00	[]	[]	TAGS #diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #region-us	# LoRA DreamBooth - nick1221/outputs These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a photo of robot dog using DreamBooth. You can find some example images in the following. !img_0 !img_1 !img_2 !img_3 LoRA for the text encoder was enabled: False.	[ "# LoRA DreamBooth - nick1221/outputs\n\nThese are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a photo of robot dog using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ "TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #region-us \n", "# LoRA DreamBooth - nick1221/outputs\n\nThese are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a photo of robot dog using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ 66, 95 ]	[ "passage: TAGS\n#diffusers #stable-diffusion #stable-diffusion-diffusers #text-to-image #lora #base_model-stabilityai/stable-diffusion-2-1 #license-creativeml-openrail-m #region-us \n# LoRA DreamBooth - nick1221/outputs\n\nThese are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were trained on a photo of robot dog using DreamBooth. You can find some example images in the following. \n\n!img_0\n!img_1\n!img_2\n!img_3\n\n\nLoRA for the text encoder was enabled: False." ]	[ -0.011676653288304806, 0.006012909114360809, -0.0020474332850426435, 0.02847246453166008, 0.13854143023490906, -0.0036198622547090054, 0.22681288421154022, 0.08492279797792435, 0.045761313289403915, 0.07900574058294296, 0.0826135203242302, 0.06975211948156357, -0.005930464714765549, 0.14287935197353363, -0.004946372006088495, -0.1460188925266266, 0.06048252806067467, -0.08777972310781479, -0.056101031601428986, 0.011286484077572823, 0.050094395875930786, -0.02348863147199154, 0.0921449065208435, -0.025634948164224625, -0.14646904170513153, 0.036266278475522995, -0.004645939450711012, -0.042643506079912186, 0.05524274706840515, 0.06611350178718567, 0.01831171289086342, 0.056524284183979034, 0.05857698991894722, -0.16211619973182678, 0.025475680828094482, 0.027221065014600754, -0.03503834083676338, 0.04598848894238472, -0.054308272898197174, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-large-bn-adapter-3.17M-snli-model1 This model is a fine-tuned version of [roberta-large](https://huggingface.co/roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6213 - Accuracy: 0.804 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 15 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.3105 \| 1.0 \| 8584 \| 0.2418 \| 0.9176 \| \| 0.2877 \| 2.0 \| 17168 \| 0.2294 \| 0.9223 \| \| 0.2768 \| 3.0 \| 25752 \| 0.2243 \| 0.9240 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-large", "model-index": [{"name": "roberta-large-bn-adapter-3.17M-snli-model1", "results": []}]}	null	varun-v-rao/roberta-large-bn-adapter-3.17M-snli-model1	[ "tensorboard", "generated_from_trainer", "base_model:roberta-large", "license:mit", "region:us" ]	2024-02-06T04:35:56+00:00	[]	[]	TAGS #tensorboard #generated_from_trainer #base_model-roberta-large #license-mit #region-us	roberta-large-bn-adapter-3.17M-snli-model1 ========================================== This model is a fine-tuned version of roberta-large on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.6213 * Accuracy: 0.804 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 15 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 15\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-large #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 15\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 32, 98, 4, 33 ]	[ "passage: TAGS\n#tensorboard #generated_from_trainer #base_model-roberta-large #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 15\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.0771183967590332, -0.02338598109781742, -0.0009387187310494483, 0.11565724015235901, 0.19629453122615814, 0.03765023872256279, 0.15827122330665588, 0.0669541135430336, -0.1202378049492836, 0.016902709379792213, 0.1015181913971901, 0.12441790103912354, -0.006471141707152128, 0.05422975867986679, -0.02366012893617153, -0.2654603123664856, -0.03600674867630005, 0.042314834892749786, -0.13900874555110931, 0.12058179825544357, 0.0820549950003624, -0.14838092029094696, 0.06784864515066147, 0.007021482102572918, -0.3299749195575714, 0.05311701446771622, 0.040150344371795654, -0.04650535061955452, 0.13689495623111725, -0.005959800910204649, 0.1912897527217865, 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null	null	diffusers	# ae-real-nigi3d API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/14542202021707193215.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "ae-real-nigi3d" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/ae-real-nigi3d) Model link: [View model](https://modelslab.com/models/ae-real-nigi3d) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "ae-real-nigi3d", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN	{"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true}	text-to-image	stablediffusionapi/ae-real-nigi3d	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	2024-02-06T04:40:10+00:00	[]	[]	TAGS #diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us	# ae-real-nigi3d API Inference !generated from URL ## Get API Key Get API key from ModelsLab API, No Payment needed. Replace Key in below code, change model_id to "ae-real-nigi3d" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs Try model for free: Generate Images Model link: View model View all models: View Models import requests import json url = "URL payload = URL({ "key": "your_api_key", "model_id": "ae-real-nigi3d", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(URL) > Use this coupon code to get 25% off DMGG0RBN	[ "# ae-real-nigi3d API Inference\n\n!generated from URL", "## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"ae-real-nigi3d\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"ae-real-nigi3d\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN" ]	[ "TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# ae-real-nigi3d API Inference\n\n!generated from URL", "## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"ae-real-nigi3d\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"ae-real-nigi3d\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN" ]	[ 70, 19, 556 ]	[ "passage: TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# ae-real-nigi3d API Inference\n\n!generated from URL" ]	[ -0.052609775215387344, 0.052299387753009796, -0.003810620168223977, 0.0510832741856575, 0.10892955213785172, -0.03014417178928852, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_article_model2 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8307 - Rouge1: 0.002 - Rouge2: 0.0011 - Rougel: 0.002 - Rougelsum: 0.002 - Gen Len: 0.095 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 50 \| 2.2217 \| 0.0401 \| 0.017 \| 0.0369 \| 0.0372 \| 2.09 \| \| No log \| 2.0 \| 100 \| 1.8971 \| 0.001 \| 0.0005 \| 0.001 \| 0.001 \| 0.095 \| \| No log \| 3.0 \| 150 \| 1.8432 \| 0.001 \| 0.0005 \| 0.001 \| 0.001 \| 0.095 \| \| No log \| 4.0 \| 200 \| 1.8307 \| 0.002 \| 0.0011 \| 0.002 \| 0.002 \| 0.095 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "my_article_model2", "results": []}]}	text2text-generation	hussainBurhan/my_article_model2	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:t5-small", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T04:42:14+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	my\_article\_model2 =================== This model is a fine-tuned version of t5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.8307 * Rouge1: 0.002 * Rouge2: 0.0011 * Rougel: 0.002 * Rougelsum: 0.002 * Gen Len: 0.095 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 4 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 77, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.09460937976837158, 0.06795691698789597, -0.0019046663073822856, 0.10757317394018173, 0.12441359460353851, -0.0025696696247905493, 0.16910600662231445, 0.12435691803693771, -0.06783522665500641, 0.0454556979238987, 0.1358487606048584, 0.11368320882320404, 0.031879741698503494, 0.14064987003803253, -0.06671234220266342, -0.20283597707748413, 0.013972509652376175, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	dvilasuero/DistilabelOpenHermes-2.5-mistral-7b-mix2	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T04:42:30+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04571164771914482, 0.1637648642063141, -0.005522117950022221, 0.017756497487425804, 0.09821303188800812, 0.01318030059337616, 0.06541220843791962, 0.1127115860581398, -0.017605241388082504, 0.1127321794629097, 0.030432263389229774, 0.09820804744958878, 0.1134178638458252, 0.14702944457530975, -0.003594378475099802, -0.22472713887691498, 0.052083637565374374, -0.12124937027692795, -0.03241228312253952, 0.1181139275431633, 0.14941681921482086, -0.09871039539575577, 0.07234785705804825, -0.030714161694049835, -0.01334790326654911, -0.03167412802577019, -0.05947697162628174, -0.045681875199079514, 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null	null	diffusers	# DreamBooth - NK2306/FineTunedModelSD This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on ku Tenh using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False.	{"license": "creativeml-openrail-m", "tags": ["stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers", "dreambooth"], "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "ku Tenh", "inference": true}	text-to-image	NK2306/FineTunedModelSD	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "dreambooth", "base_model:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	2024-02-06T04:43:23+00:00	[]	[]	TAGS #diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us	# DreamBooth - NK2306/FineTunedModelSD This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on ku Tenh using DreamBooth. You can find some example images in the following. DreamBooth for the text encoder was enabled: False.	[ "# DreamBooth - NK2306/FineTunedModelSD\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on ku Tenh using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]	[ "TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "# DreamBooth - NK2306/FineTunedModelSD\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on ku Tenh using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]	[ 97, 79 ]	[ "passage: TAGS\n#diffusers #tensorboard #safetensors #stable-diffusion #stable-diffusion-diffusers #text-to-image #dreambooth #base_model-CompVis/stable-diffusion-v1-4 #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n# DreamBooth - NK2306/FineTunedModelSD\n\nThis is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on ku Tenh using DreamBooth.\nYou can find some example images in the following. \n\n\n\nDreamBooth for the text encoder was enabled: False." ]	[ -0.042480554431676865, 0.06309319287538528, -0.0009013091330416501, 0.0314345583319664, 0.11230699717998505, -0.0009034625254571438, 0.20968398451805115, 0.028753286227583885, 0.058545880019664764, 0.0639677569270134, 0.10546842217445374, -0.02398792840540409, 0.023897044360637665, 0.11465507000684738, 0.026531951501965523, -0.16817082464694977, 0.026981176808476448, 0.009436839260160923, -0.009138382039964199, 0.042563654482364655, 0.02708243578672409, -0.10223852843046188, 0.09533882141113281, -0.03601051867008209, -0.17115333676338196, 0.01369374431669712, -0.013472416438162327, -0.04549907147884369, 0.06241225823760033, 0.019309690222144127, 0.12685707211494446, 0.08440246433019638, 0.01707381196320057, -0.1518261432647705, 0.031494803726673126, 0.04360023885965347, -0.002341795712709427, 0.024157097563147545, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-classification	dylansheils0241/Quantum-Balanced-GPT2-Experimental-Theoretical-Classifier-Arxiv	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T04:52:14+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 57, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.056688208132982254, 0.16275818645954132, -0.004995849449187517, 0.02452681213617325, 0.09786820411682129, 0.012234015390276909, 0.0665494054555893, 0.11446622014045715, -0.019654832780361176, 0.11005201935768127, 0.03224760666489601, 0.09871180355548859, 0.11353186517953873, 0.16168983280658722, 0.0032966197468340397, -0.23542851209640503, 0.0468909777700901, -0.12772662937641144, -0.03536919504404068, 0.1168198511004448, 0.1453043669462204, -0.10465475916862488, 0.07777879387140274, -0.030737513676285744, -0.00966490525752306, -0.031576599925756454, -0.06401672959327698, -0.05254754796624184, 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null	null	null	These are some rvc models I trained. I will unpack the game and extract the voice data after training and put it here.	{"language": ["zh"], "license": "mit", "datasets": ["mozi1924/sounds"], "pipeline_tag": "audio-to-audio"}	audio-to-audio	mozi1924/my_rvc_model	[ "audio-to-audio", "zh", "dataset:mozi1924/sounds", "license:mit", "region:us" ]	2024-02-06T04:53:12+00:00	[]	[ "zh" ]	TAGS #audio-to-audio #zh #dataset-mozi1924/sounds #license-mit #region-us	These are some rvc models I trained. I will unpack the game and extract the voice data after training and put it here.	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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_1_3136_files This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.2746 - Accuracy: 0.9331 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6934 \| 0.96 \| 19 \| 0.6904 \| 0.5334 \| \| 0.6794 \| 1.97 \| 39 \| 0.6392 \| 0.6672 \| \| 0.5873 \| 2.99 \| 59 \| 0.5179 \| 0.7548 \| \| 0.4796 \| 4.0 \| 79 \| 0.3624 \| 0.8710 \| \| 0.3158 \| 4.96 \| 98 \| 0.3010 \| 0.8933 \| \| 0.2043 \| 5.97 \| 118 \| 0.5355 \| 0.8201 \| \| 0.1699 \| 6.99 \| 138 \| 0.2456 \| 0.9220 \| \| 0.1443 \| 8.0 \| 158 \| 0.2069 \| 0.9268 \| \| 0.1258 \| 8.96 \| 177 \| 0.2480 \| 0.9172 \| \| 0.1057 \| 9.97 \| 197 \| 0.2855 \| 0.9188 \| \| 0.0796 \| 10.99 \| 217 \| 0.3025 \| 0.9108 \| \| 0.0582 \| 12.0 \| 237 \| 0.3201 \| 0.9061 \| \| 0.0505 \| 12.96 \| 256 \| 0.2977 \| 0.9188 \| \| 0.0802 \| 13.97 \| 276 \| 0.1667 \| 0.9490 \| \| 0.0377 \| 14.99 \| 296 \| 0.2239 \| 0.9411 \| \| 0.0346 \| 16.0 \| 316 \| 0.2396 \| 0.9315 \| \| 0.0372 \| 16.96 \| 335 \| 0.2970 \| 0.9204 \| \| 0.0222 \| 17.97 \| 355 \| 0.3063 \| 0.9204 \| \| 0.0214 \| 18.99 \| 375 \| 0.2761 \| 0.9331 \| \| 0.0192 \| 19.24 \| 380 \| 0.2746 \| 0.9331 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "test_1_3136_files", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9331210191082803, "name": "Accuracy"}]}]}]}	audio-classification	PatricioMN/test_1_3136_files	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:audiofolder", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-06T04:54:37+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us	test\_1\_3136\_files ==================== This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set: * Loss: 0.2746 * Accuracy: 0.9331 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 78, 144, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 20### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-lora-592K-snli-model2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9156 - Accuracy: 0.6375 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 98 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6215 \| 1.0 \| 2146 \| 0.5300 \| 0.7996 \| \| 0.5619 \| 2.0 \| 4292 \| 0.4853 \| 0.8164 \| \| 0.5446 \| 3.0 \| 6438 \| 0.4782 \| 0.8188 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-lora-592K-snli-model2", "results": []}]}	text-classification	varun-v-rao/bert-base-cased-lora-592K-snli-model2	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T04:58:03+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	bert-base-cased-lora-592K-snli-model2 ===================================== This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.9156 * Accuracy: 0.6375 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 256 * eval\_batch\_size: 256 * seed: 98 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 98\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 98\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 67, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 98\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.08894699811935425, 0.108758844435215, -0.002147892490029335, 0.10633673518896103, 0.1493767946958542, 0.021484412252902985, 0.15700002014636993, 0.10932836681604385, -0.07722398638725281, 0.04654308035969734, 0.1250632107257843, 0.12847165763378143, 0.015215017832815647, 0.1305249035358429, -0.061375945806503296, -0.22570475935935974, 0.009530636481940746, 0.03258287161588669, -0.06905625760555267, 0.1170974150300026, 0.09107226133346558, 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null	null	ml-agents	# ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: r0in/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀	{"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]}	reinforcement-learning	r0in/ppo-SnowballTarget	[ "ml-agents", "tensorboard", "onnx", "SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget", "region:us" ]	2024-02-06T04:59:11+00:00	[]	[]	TAGS #ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us	# ppo Agent playing SnowballTarget This is a trained model of a ppo agent playing SnowballTarget using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A longer tutorial to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: r0in/ppo-SnowballTarget 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play	[ "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: r0in/ppo-SnowballTarget\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ "TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n", "# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: r0in/ppo-SnowballTarget\n 3. Step 2: Select your .nn /.onnx file\n 4. Click on Watch the agent play" ]	[ 50, 206 ]	[ "passage: TAGS\n#ml-agents #tensorboard #onnx #SnowballTarget #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SnowballTarget #region-us \n# ppo Agent playing SnowballTarget\n This is a trained model of a ppo agent playing SnowballTarget\n using the Unity ML-Agents Library.\n\n ## Usage (with ML-Agents)\n The Documentation: URL\n\n We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:\n - A short tutorial where you teach Huggy the Dog to fetch the stick and then play with him directly in your\n browser: URL\n - A longer tutorial to understand how works ML-Agents:\n URL\n\n ### Resume the training\n \n\n ### Watch your Agent play\n You can watch your agent playing directly in your browser\n\n 1. If the environment is part of ML-Agents official environments, go to URL\n 2. Step 1: Find your model_id: r0in/ppo-SnowballTarget\n 3. Step 2: Select your .nn /.onnx file\n 4. 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-classification	dylansheils0241/Quantum-Balanced-GPT2-Experimental-Theoretical-Classifier-Arxiv-V2	[ "transformers", "safetensors", "gpt2", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T05:01:01+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 57, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.056688208132982254, 0.16275818645954132, -0.004995849449187517, 0.02452681213617325, 0.09786820411682129, 0.012234015390276909, 0.0665494054555893, 0.11446622014045715, -0.019654832780361176, 0.11005201935768127, 0.03224760666489601, 0.09871180355548859, 0.11353186517953873, 0.16168983280658722, 0.0032966197468340397, -0.23542851209640503, 0.0468909777700901, -0.12772662937641144, -0.03536919504404068, 0.1168198511004448, 0.1453043669462204, -0.10465475916862488, 0.07777879387140274, -0.030737513676285744, -0.00966490525752306, -0.031576599925756454, -0.06401672959327698, -0.05254754796624184, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # huner_ncbi_disease_dslim This model is a fine-tuned version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER) on the transformer_dataset_ner dataset. It achieves the following results on the evaluation set: - Loss: 0.1484 - Precision: 0.8325 - Recall: 0.8653 - F1: 0.8486 - Accuracy: 0.9850 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1243 \| 1.0 \| 667 \| 0.0669 \| 0.7013 \| 0.8412 \| 0.7649 \| 0.9787 \| \| 0.0512 \| 2.0 \| 1334 \| 0.0656 \| 0.7825 \| 0.8412 \| 0.8108 \| 0.9818 \| \| 0.0221 \| 3.0 \| 2001 \| 0.0744 \| 0.7908 \| 0.8501 \| 0.8194 \| 0.9822 \| \| 0.0107 \| 4.0 \| 2668 \| 0.1022 \| 0.7940 \| 0.8475 \| 0.8199 \| 0.9808 \| \| 0.008 \| 5.0 \| 3335 \| 0.1055 \| 0.7818 \| 0.8602 \| 0.8191 \| 0.9816 \| \| 0.0057 \| 6.0 \| 4002 \| 0.1173 \| 0.8067 \| 0.8590 \| 0.832 \| 0.9830 \| \| 0.0027 \| 7.0 \| 4669 \| 0.1188 \| 0.8188 \| 0.8501 \| 0.8342 \| 0.9834 \| \| 0.0022 \| 8.0 \| 5336 \| 0.1229 \| 0.8080 \| 0.8450 \| 0.8261 \| 0.9826 \| \| 0.0019 \| 9.0 \| 6003 \| 0.1341 \| 0.8007 \| 0.8526 \| 0.8258 \| 0.9834 \| \| 0.0019 \| 10.0 \| 6670 \| 0.1360 \| 0.8045 \| 0.8628 \| 0.8326 \| 0.9822 \| \| 0.0011 \| 11.0 \| 7337 \| 0.1376 \| 0.8163 \| 0.8640 \| 0.8395 \| 0.9838 \| \| 0.0008 \| 12.0 \| 8004 \| 0.1447 \| 0.8007 \| 0.8577 \| 0.8282 \| 0.9833 \| \| 0.0006 \| 13.0 \| 8671 \| 0.1381 \| 0.8139 \| 0.8615 \| 0.8370 \| 0.9839 \| \| 0.0005 \| 14.0 \| 9338 \| 0.1398 \| 0.8297 \| 0.8666 \| 0.8477 \| 0.9843 \| \| 0.0004 \| 15.0 \| 10005 \| 0.1404 \| 0.8232 \| 0.8640 \| 0.8431 \| 0.9842 \| \| 0.0003 \| 16.0 \| 10672 \| 0.1486 \| 0.8329 \| 0.8551 \| 0.8439 \| 0.9838 \| \| 0.0 \| 17.0 \| 11339 \| 0.1469 \| 0.8114 \| 0.8691 \| 0.8393 \| 0.9837 \| \| 0.0002 \| 18.0 \| 12006 \| 0.1500 \| 0.8297 \| 0.8602 \| 0.8447 \| 0.9843 \| \| 0.0001 \| 19.0 \| 12673 \| 0.1489 \| 0.8315 \| 0.8653 \| 0.8481 \| 0.9849 \| \| 0.0 \| 20.0 \| 13340 \| 0.1484 \| 0.8325 \| 0.8653 \| 0.8486 \| 0.9850 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["transformer_dataset_ner"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "dslim/distilbert-NER", "model-index": [{"name": "huner_ncbi_disease_dslim", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "transformer_dataset_ner", "type": "transformer_dataset_ner", "config": "ncbi_disease", "split": "validation", "args": "ncbi_disease"}, "metrics": [{"type": "precision", "value": 0.8325183374083129, "name": "Precision"}, {"type": "recall", "value": 0.8653113087674714, "name": "Recall"}, {"type": "f1", "value": 0.8485981308411215, "name": "F1"}, {"type": "accuracy", "value": 0.9849891909996041, "name": "Accuracy"}]}]}]}	token-classification	manibt1993/huner_ncbi_disease_dslim	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "dataset:transformer_dataset_ner", "base_model:dslim/distilbert-NER", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T05:05:05+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-transformer_dataset_ner #base_model-dslim/distilbert-NER #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	huner\_ncbi\_disease\_dslim =========================== This model is a fine-tuned version of dslim/distilbert-NER on the transformer\_dataset\_ner dataset. It achieves the following results on the evaluation set: * Loss: 0.1484 * Precision: 0.8325 * Recall: 0.8653 * F1: 0.8486 * Accuracy: 0.9850 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 20 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-transformer_dataset_ner #base_model-dslim/distilbert-NER #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 87, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #token-classification #generated_from_trainer #dataset-transformer_dataset_ner #base_model-dslim/distilbert-NER #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 20### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.11331743746995926, 0.16518492996692657, -0.0032087506260722876, 0.10786605626344681, 0.10086948424577713, -0.010844642296433449, 0.15423667430877686, 0.1319924294948578, -0.06677702069282532, 0.09634270519018173, 0.15692844986915588, 0.1441306322813034, 0.014604879543185234, 0.184182807803154, 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null	null	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # Aanshula/layoutlm-funsd-tf This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.3182 - Validation Loss: 0.6807 - Train Overall Precision: 0.7172 - Train Overall Recall: 0.7878 - Train Overall F1: 0.7508 - Train Overall Accuracy: 0.7864 - Epoch: 6 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Train Overall Precision \| Train Overall Recall \| Train Overall F1 \| Train Overall Accuracy \| Epoch \| \|:----------:\|:---------------:\|:-----------------------:\|:--------------------:\|:----------------:\|:----------------------:\|:-----:\| \| 1.7000 \| 1.4167 \| 0.2445 \| 0.2107 \| 0.2264 \| 0.4831 \| 0 \| \| 1.1656 \| 0.8677 \| 0.5749 \| 0.6257 \| 0.5992 \| 0.7251 \| 1 \| \| 0.7704 \| 0.7254 \| 0.6356 \| 0.7160 \| 0.6734 \| 0.7637 \| 2 \| \| 0.5758 \| 0.6690 \| 0.6851 \| 0.7476 \| 0.7150 \| 0.7857 \| 3 \| \| 0.4526 \| 0.6096 \| 0.7085 \| 0.7757 \| 0.7406 \| 0.8046 \| 4 \| \| 0.3614 \| 0.6834 \| 0.7118 \| 0.7657 \| 0.7377 \| 0.7872 \| 5 \| \| 0.3182 \| 0.6807 \| 0.7172 \| 0.7878 \| 0.7508 \| 0.7864 \| 6 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "microsoft/layoutlm-base-uncased", "model-index": [{"name": "Aanshula/layoutlm-funsd-tf", "results": []}]}	token-classification	Aanshula/layoutlm-funsd-tf	[ "transformers", "tf", "tensorboard", "layoutlm", "token-classification", "generated_from_keras_callback", "base_model:microsoft/layoutlm-base-uncased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T05:06:14+00:00	[]	[]	TAGS #transformers #tf #tensorboard #layoutlm #token-classification #generated_from_keras_callback #base_model-microsoft/layoutlm-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us	Aanshula/layoutlm-funsd-tf ========================== This model is a fine-tuned version of microsoft/layoutlm-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.3182 * Validation Loss: 0.6807 * Train Overall Precision: 0.7172 * Train Overall Recall: 0.7878 * Train Overall F1: 0.7508 * Train Overall Accuracy: 0.7864 * Epoch: 6 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': 3e-05, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\_decay\_rate': 0.01} * training\_precision: mixed\_float16 ### Training results ### Framework versions * Transformers 4.35.2 * TensorFlow 2.15.0 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tf #tensorboard #layoutlm #token-classification #generated_from_keras_callback #base_model-microsoft/layoutlm-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 74, 122, 4, 31 ]	[ "passage: TAGS\n#transformers #tf #tensorboard #layoutlm #token-classification #generated_from_keras_callback #base_model-microsoft/layoutlm-base-uncased #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': 3e-05, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: mixed\\_float16### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.09931270778179169, 0.09231573343276978, -0.0037152450531721115, 0.07736225426197052, 0.08962064236402512, 0.036955755203962326, 0.15003326535224915, 0.15483999252319336, -0.09336654841899872, 0.07638853043317795, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # safety-utcustom-train-SF-RGBD-b5 This model is a fine-tuned version of [nvidia/mit-b5](https://huggingface.co/nvidia/mit-b5) on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: - Loss: 0.0867 - Mean Iou: 0.7280 - Mean Accuracy: 0.7762 - Overall Accuracy: 0.9818 - Accuracy Unlabeled: nan - Accuracy Safe: 0.5578 - Accuracy Unsafe: 0.9947 - Iou Unlabeled: nan - Iou Safe: 0.4745 - Iou Unsafe: 0.9814 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 4e-06 - train_batch_size: 15 - eval_batch_size: 15 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 120 ### Training results \| Training Loss \| Epoch \| Step \| Accuracy Safe \| Accuracy Unlabeled \| Accuracy Unsafe \| Iou Safe \| Iou Unlabeled \| Iou Unsafe \| Validation Loss \| Mean Accuracy \| Mean Iou \| Overall Accuracy \| \|:-------------:\|:------:\|:----:\|:-------------:\|:------------------:\|:---------------:\|:--------:\|:-------------:\|:----------:\|:---------------:\|:-------------:\|:--------:\|:----------------:\| \| 0.789 \| 0.91 \| 10 \| 0.0203 \| nan \| 0.8957 \| 0.0095 \| 0.0 \| 0.8722 \| 0.9555 \| 0.4580 \| 0.2939 \| 0.8698 \| \| 0.7579 \| 1.82 \| 20 \| 0.0117 \| nan \| 0.9614 \| 0.0069 \| 0.0 \| 0.9338 \| 0.8322 \| 0.4866 \| 0.3136 \| 0.9334 \| \| 0.7103 \| 2.73 \| 30 \| 0.0051 \| nan \| 0.9893 \| 0.0043 \| 0.0 \| 0.9604 \| 0.6729 \| 0.4972 \| 0.3216 \| 0.9602 \| \| 0.676 \| 3.64 \| 40 \| 0.0021 \| nan \| 0.9969 \| 0.0020 \| 0.0 \| 0.9675 \| 0.5336 \| 0.4995 \| 0.3232 \| 0.9675 \| \| 0.5955 \| 4.55 \| 50 \| 0.0001 \| nan \| 0.9993 \| 0.0001 \| 0.0 \| 0.9698 \| 0.4440 \| 0.4997 \| 0.3233 \| 0.9698 \| \| 0.5691 \| 5.45 \| 60 \| 0.0000 \| nan \| 0.9997 \| 0.0000 \| 0.0 \| 0.9702 \| 0.3812 \| 0.4999 \| 0.3234 \| 0.9702 \| \| 0.5067 \| 6.36 \| 70 \| 0.0 \| nan \| 0.9996 \| 0.0 \| 0.0 \| 0.9701 \| 0.3590 \| 0.4998 \| 0.3234 \| 0.9701 \| \| 0.4656 \| 7.27 \| 80 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9703 \| 0.3247 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.4227 \| 8.18 \| 90 \| 0.0 \| nan \| 0.9998 \| 0.0 \| 0.0 \| 0.9702 \| 0.3171 \| 0.4999 \| 0.3234 \| 0.9702 \| \| 0.3898 \| 9.09 \| 100 \| 0.0004 \| nan \| 0.9996 \| 0.0004 \| 0.0 \| 0.9701 \| 0.3122 \| 0.5000 \| 0.3235 \| 0.9701 \| \| 0.3513 \| 10.0 \| 110 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9703 \| 0.2876 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.4157 \| 10.91 \| 120 \| 0.0000 \| nan \| 0.9998 \| 0.0000 \| 0.0 \| 0.9703 \| 0.2820 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.3317 \| 11.82 \| 130 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9703 \| 0.2693 \| 0.4999 \| 0.3234 \| 0.9703 \| \| 0.321 \| 12.73 \| 140 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2647 \| 0.4999 \| 0.3235 \| 0.9704 \| \| 0.2887 \| 13.64 \| 150 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2539 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.3008 \| 14.55 \| 160 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2536 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2853 \| 15.45 \| 170 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2397 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2684 \| 16.36 \| 180 \| 0.0 \| nan \| 0.9999 \| 0.0 \| 0.0 \| 0.9704 \| 0.2321 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2585 \| 17.27 \| 190 \| 0.0000 \| nan \| 0.9999 \| 0.0000 \| 0.0 \| 0.9704 \| 0.2208 \| 0.5000 \| 0.3235 \| 0.9704 \| \| 0.2088 \| 18.18 \| 200 \| 0.0084 \| nan \| 0.9997 \| 0.0083 \| 0.0 \| 0.9704 \| 0.2011 \| 0.5041 \| 0.3262 \| 0.9704 \| \| 0.2518 \| 19.09 \| 210 \| 0.0468 \| nan \| 0.9989 \| 0.0451 \| 0.0 \| 0.9707 \| 0.2026 \| 0.5228 \| 0.3386 \| 0.9707 \| \| 0.218 \| 20.0 \| 220 \| 0.0879 \| nan \| 0.9984 \| 0.0834 \| nan \| 0.9714 \| 0.1889 \| 0.5431 \| 0.5274 \| 0.9715 \| \| 0.2046 \| 20.91 \| 230 \| 0.1931 \| nan \| 0.9969 \| 0.1752 \| nan \| 0.9730 \| 0.1847 \| 0.5950 \| 0.5741 \| 0.9732 \| \| 0.2147 \| 21.82 \| 240 \| 0.2042 \| nan \| 0.9968 \| 0.1850 \| nan \| 0.9733 \| 0.1766 \| 0.6005 \| 0.5791 \| 0.9734 \| \| 0.188 \| 22.73 \| 250 \| 0.2020 \| nan \| 0.9972 \| 0.1849 \| nan \| 0.9735 \| 0.1726 \| 0.5996 \| 0.5792 \| 0.9737 \| \| 0.2175 \| 23.64 \| 260 \| 0.1898 \| nan \| 0.9974 \| 0.1748 \| nan \| 0.9734 \| 0.1706 \| 0.5936 \| 0.5741 \| 0.9735 \| \| 0.2059 \| 24.55 \| 270 \| 0.3006 \| nan \| 0.9962 \| 0.2670 \| nan \| 0.9754 \| 0.1689 \| 0.6484 \| 0.6212 \| 0.9756 \| \| 0.1776 \| 25.45 \| 280 \| 0.2870 \| nan \| 0.9967 \| 0.2587 \| nan \| 0.9755 \| 0.1612 \| 0.6418 \| 0.6171 \| 0.9757 \| \| 0.1585 \| 26.36 \| 290 \| 0.4254 \| nan \| 0.9944 \| 0.3593 \| nan \| 0.9773 \| 0.1537 \| 0.7099 \| 0.6683 \| 0.9776 \| \| 0.1588 \| 27.27 \| 300 \| 0.2798 \| nan \| 0.9970 \| 0.2548 \| nan \| 0.9756 \| 0.1527 \| 0.6384 \| 0.6152 \| 0.9758 \| \| 0.153 \| 28.18 \| 310 \| 0.4288 \| nan \| 0.9946 \| 0.3646 \| nan \| 0.9776 \| 0.1452 \| 0.7117 \| 0.6711 \| 0.9779 \| \| 0.1623 \| 29.09 \| 320 \| 0.4401 \| nan \| 0.9945 \| 0.3726 \| nan \| 0.9778 \| 0.1442 \| 0.7173 \| 0.6752 \| 0.9781 \| \| 0.1603 \| 30.0 \| 330 \| 0.4050 \| nan \| 0.9958 \| 0.3562 \| nan \| 0.9781 \| 0.1407 \| 0.7004 \| 0.6671 \| 0.9784 \| \| 0.1694 \| 30.91 \| 340 \| 0.4585 \| nan \| 0.9948 \| 0.3911 \| nan \| 0.9786 \| 0.1343 \| 0.7266 \| 0.6849 \| 0.9789 \| \| 0.1585 \| 31.82 \| 350 \| 0.3861 \| nan \| 0.9962 \| 0.3433 \| nan \| 0.9779 \| 0.1353 \| 0.6912 \| 0.6606 \| 0.9782 \| \| 0.1342 \| 32.73 \| 360 \| 0.4963 \| nan \| 0.9939 \| 0.4132 \| nan \| 0.9789 \| 0.1338 \| 0.7451 \| 0.6961 \| 0.9792 \| \| 0.1358 \| 33.64 \| 370 \| 0.5048 \| nan \| 0.9937 \| 0.4182 \| nan \| 0.9789 \| 0.1342 \| 0.7493 \| 0.6986 \| 0.9793 \| \| 0.1493 \| 34.55 \| 380 \| 0.4809 \| nan \| 0.9946 \| 0.4080 \| nan \| 0.9791 \| 0.1297 \| 0.7377 \| 0.6936 \| 0.9794 \| \| 0.1435 \| 35.45 \| 390 \| 0.5658 \| nan \| 0.9923 \| 0.4518 \| nan \| 0.9794 \| 0.1271 \| 0.7791 \| 0.7156 \| 0.9797 \| \| 0.1305 \| 36.36 \| 400 \| 0.4157 \| nan \| 0.9968 \| 0.3758 \| nan \| 0.9793 \| 0.1225 \| 0.7062 \| 0.6776 \| 0.9796 \| \| 0.1496 \| 37.27 \| 410 \| 0.5385 \| nan \| 0.9934 \| 0.4420 \| nan \| 0.9796 \| 0.1237 \| 0.7659 \| 0.7108 \| 0.9799 \| \| 0.1445 \| 38.18 \| 420 \| 0.5763 \| nan \| 0.9924 \| 0.4615 \| nan \| 0.9798 \| 0.1207 \| 0.7843 \| 0.7206 \| 0.9801 \| \| 0.1307 \| 39.09 \| 430 \| 0.4853 \| nan \| 0.9956 \| 0.4244 \| nan \| 0.9803 \| 0.1194 \| 0.7404 \| 0.7023 \| 0.9806 \| \| 0.1379 \| 40.0 \| 440 \| 0.5722 \| nan \| 0.9922 \| 0.4557 \| nan \| 0.9795 \| 0.1174 \| 0.7822 \| 0.7176 \| 0.9798 \| \| 0.1202 \| 40.91 \| 450 \| 0.5399 \| nan \| 0.9943 \| 0.4544 \| nan \| 0.9805 \| 0.1143 \| 0.7671 \| 0.7175 \| 0.9809 \| \| 0.1239 \| 41.82 \| 460 \| 0.5580 \| nan \| 0.9932 \| 0.4558 \| nan \| 0.9800 \| 0.1150 \| 0.7756 \| 0.7179 \| 0.9803 \| \| 0.1183 \| 42.73 \| 470 \| 0.4777 \| nan \| 0.9961 \| 0.4236 \| nan \| 0.9805 \| 0.1129 \| 0.7369 \| 0.7021 \| 0.9808 \| \| 0.1202 \| 43.64 \| 480 \| 0.5933 \| nan \| 0.9928 \| 0.4793 \| nan \| 0.9806 \| 0.1119 \| 0.7930 \| 0.7300 \| 0.9810 \| \| 0.1276 \| 44.55 \| 490 \| 0.5425 \| nan \| 0.9942 \| 0.4561 \| nan \| 0.9806 \| 0.1131 \| 0.7683 \| 0.7183 \| 0.9809 \| \| 0.1172 \| 45.45 \| 500 \| 0.6272 \| nan \| 0.9898 \| 0.4700 \| nan \| 0.9787 \| 0.1135 \| 0.8085 \| 0.7244 \| 0.9791 \| \| 0.1288 \| 46.36 \| 510 \| 0.4236 \| nan \| 0.9974 \| 0.3898 \| nan \| 0.9802 \| 0.1105 \| 0.7105 \| 0.6850 \| 0.9804 \| \| 0.1185 \| 47.27 \| 520 \| 0.6035 \| nan \| 0.9914 \| 0.4711 \| nan \| 0.9796 \| 0.1130 \| 0.7975 \| 0.7254 \| 0.9800 \| \| 0.1045 \| 48.18 \| 530 \| 0.5750 \| nan \| 0.9930 \| 0.4679 \| nan \| 0.9804 \| 0.1102 \| 0.7840 \| 0.7241 \| 0.9807 \| \| 0.1211 \| 49.09 \| 540 \| 0.5812 \| nan \| 0.9929 \| 0.4715 \| nan \| 0.9804 \| 0.1069 \| 0.7870 \| 0.7260 \| 0.9808 \| \| 0.1206 \| 50.0 \| 550 \| 0.5221 \| nan \| 0.9953 \| 0.4528 \| nan \| 0.9811 \| 0.1071 \| 0.7587 \| 0.7169 \| 0.9814 \| \| 0.1193 \| 50.91 \| 560 \| 0.4956 \| nan \| 0.9961 \| 0.4398 \| nan \| 0.9811 \| 0.1053 \| 0.7459 \| 0.7105 \| 0.9814 \| \| 0.1116 \| 51.82 \| 570 \| 0.5257 \| nan \| 0.9951 \| 0.4528 \| nan \| 0.9809 \| 0.1043 \| 0.7604 \| 0.7169 \| 0.9812 \| \| 0.1218 \| 52.73 \| 580 \| 0.5936 \| nan \| 0.9922 \| 0.4724 \| nan \| 0.9801 \| 0.1078 \| 0.7929 \| 0.7262 \| 0.9804 \| \| 0.1284 \| 53.64 \| 590 \| 0.5872 \| nan \| 0.9924 \| 0.4696 \| nan \| 0.9801 \| 0.1054 \| 0.7898 \| 0.7248 \| 0.9804 \| \| 0.096 \| 54.55 \| 600 \| 0.5451 \| nan \| 0.9942 \| 0.4580 \| nan \| 0.9806 \| 0.1028 \| 0.7697 \| 0.7193 \| 0.9809 \| \| 0.1091 \| 55.45 \| 610 \| 0.6014 \| nan \| 0.9917 \| 0.4725 \| nan \| 0.9798 \| 0.1022 \| 0.7965 \| 0.7261 \| 0.9802 \| \| 0.1068 \| 56.36 \| 620 \| 0.4926 \| nan \| 0.9962 \| 0.4374 \| nan \| 0.9810 \| 0.1015 \| 0.7444 \| 0.7092 \| 0.9813 \| \| 0.106 \| 57.27 \| 630 \| 0.5713 \| nan \| 0.9937 \| 0.4731 \| nan \| 0.9809 \| 0.1011 \| 0.7825 \| 0.7270 \| 0.9812 \| \| 0.1009 \| 58.18 \| 640 \| 0.4512 \| nan \| 0.9969 \| 0.4089 \| nan \| 0.9805 \| 0.1028 \| 0.7240 \| 0.6947 \| 0.9807 \| \| 0.1018 \| 59.09 \| 650 \| 0.6053 \| nan \| 0.9919 \| 0.4779 \| nan \| 0.9801 \| 0.1022 \| 0.7986 \| 0.7290 \| 0.9805 \| \| 0.1012 \| 60.0 \| 660 \| 0.5167 \| nan \| 0.9949 \| 0.4427 \| nan \| 0.9805 \| 0.1016 \| 0.7558 \| 0.7116 \| 0.9808 \| \| 0.1052 \| 60.91 \| 670 \| 0.5464 \| nan \| 0.9943 \| 0.4604 \| nan \| 0.9808 \| 0.0999 \| 0.7703 \| 0.7206 \| 0.9811 \| \| 0.1229 \| 61.82 \| 680 \| 0.5706 \| nan \| 0.9939 \| 0.4750 \| nan \| 0.9810 \| 0.0993 \| 0.7822 \| 0.7280 \| 0.9814 \| \| 0.0963 \| 62.73 \| 690 \| 0.5746 \| nan \| 0.9936 \| 0.4754 \| nan \| 0.9809 \| 0.0974 \| 0.7841 \| 0.7282 \| 0.9813 \| \| 0.1115 \| 63.64 \| 700 \| 0.5239 \| nan \| 0.9955 \| 0.4562 \| nan \| 0.9813 \| 0.0974 \| 0.7597 \| 0.7187 \| 0.9816 \| \| 0.1025 \| 64.55 \| 710 \| 0.5845 \| nan \| 0.9935 \| 0.4813 \| nan \| 0.9811 \| 0.0964 \| 0.7890 \| 0.7312 \| 0.9814 \| \| 0.0916 \| 65.45 \| 720 \| 0.5493 \| nan \| 0.9947 \| 0.4685 \| nan \| 0.9813 \| 0.0962 \| 0.7720 \| 0.7249 \| 0.9816 \| \| 0.1055 \| 66.36 \| 730 \| 0.5273 \| nan \| 0.9953 \| 0.4571 \| nan \| 0.9812 \| 0.0947 \| 0.7613 \| 0.7191 \| 0.9815 \| \| 0.1081 \| 67.27 \| 740 \| 0.6093 \| nan \| 0.9919 \| 0.4813 \| nan \| 0.9802 \| 0.0964 \| 0.8006 \| 0.7308 \| 0.9806 \| \| 0.1039 \| 68.18 \| 750 \| 0.5405 \| nan \| 0.9945 \| 0.4573 \| nan \| 0.9807 \| 0.0950 \| 0.7675 \| 0.7190 \| 0.9811 \| \| 0.106 \| 69.09 \| 760 \| 0.5564 \| nan \| 0.9943 \| 0.4682 \| nan \| 0.9810 \| 0.0939 \| 0.7753 \| 0.7246 \| 0.9813 \| \| 0.0912 \| 70.0 \| 770 \| 0.5377 \| nan \| 0.9949 \| 0.4612 \| nan \| 0.9811 \| 0.0936 \| 0.7663 \| 0.7212 \| 0.9814 \| \| 0.0951 \| 70.91 \| 780 \| 0.5600 \| nan \| 0.9941 \| 0.4689 \| nan \| 0.9809 \| 0.0938 \| 0.7771 \| 0.7249 \| 0.9813 \| \| 0.0998 \| 71.82 \| 790 \| 0.5573 \| nan \| 0.9944 \| 0.4705 \| nan \| 0.9812 \| 0.0928 \| 0.7759 \| 0.7258 \| 0.9815 \| \| 0.0889 \| 72.73 \| 800 \| 0.5398 \| nan \| 0.9949 \| 0.4628 \| nan \| 0.9812 \| 0.0931 \| 0.7674 \| 0.7220 \| 0.9815 \| \| 0.0906 \| 73.64 \| 810 \| 0.5151 \| nan \| 0.9958 \| 0.4528 \| nan \| 0.9813 \| 0.0928 \| 0.7555 \| 0.7171 \| 0.9816 \| \| 0.0911 \| 74.55 \| 820 \| 0.5682 \| nan \| 0.9938 \| 0.4722 \| nan \| 0.9809 \| 0.0924 \| 0.7810 \| 0.7265 \| 0.9812 \| \| 0.0907 \| 75.45 \| 830 \| 0.4864 \| nan \| 0.9965 \| 0.4365 \| nan \| 0.9812 \| 0.0929 \| 0.7415 \| 0.7089 \| 0.9815 \| \| 0.1117 \| 76.36 \| 840 \| 0.5239 \| nan \| 0.9956 \| 0.4576 \| nan \| 0.9814 \| 0.0934 \| 0.7598 \| 0.7195 \| 0.9817 \| \| 0.0812 \| 77.27 \| 850 \| 0.5279 \| nan \| 0.9956 \| 0.4605 \| nan \| 0.9814 \| 0.0915 \| 0.7617 \| 0.7210 \| 0.9817 \| \| 0.0888 \| 78.18 \| 860 \| 0.5615 \| nan \| 0.9942 \| 0.4720 \| nan \| 0.9811 \| 0.0915 \| 0.7778 \| 0.7266 \| 0.9814 \| \| 0.09 \| 79.09 \| 870 \| 0.5414 \| nan \| 0.9948 \| 0.4628 \| nan \| 0.9811 \| 0.0920 \| 0.7681 \| 0.7220 \| 0.9814 \| \| 0.1052 \| 80.0 \| 880 \| 0.5866 \| nan \| 0.9932 \| 0.4790 \| nan \| 0.9808 \| 0.0917 \| 0.7899 \| 0.7299 \| 0.9812 \| \| 0.0867 \| 80.91 \| 890 \| 0.5252 \| nan \| 0.9955 \| 0.4573 \| nan \| 0.9813 \| 0.0912 \| 0.7603 \| 0.7193 \| 0.9816 \| \| 0.0942 \| 81.82 \| 900 \| 0.5091 \| nan \| 0.9959 \| 0.4490 \| nan \| 0.9813 \| 0.0925 \| 0.7525 \| 0.7152 \| 0.9815 \| \| 0.0917 \| 82.73 \| 910 \| 0.5454 \| nan \| 0.9950 \| 0.4682 \| nan \| 0.9814 \| 0.0908 \| 0.7702 \| 0.7248 \| 0.9817 \| \| 0.103 \| 83.64 \| 920 \| 0.5452 \| nan \| 0.9949 \| 0.4672 \| nan \| 0.9813 \| 0.0912 \| 0.7701 \| 0.7243 \| 0.9816 \| \| 0.0939 \| 84.55 \| 930 \| 0.5539 \| nan \| 0.9947 \| 0.4717 \| nan \| 0.9814 \| 0.0900 \| 0.7743 \| 0.7265 \| 0.9817 \| \| 0.0892 \| 85.45 \| 940 \| 0.5330 \| nan \| 0.9954 \| 0.4635 \| nan \| 0.9815 \| 0.0900 \| 0.7642 \| 0.7225 \| 0.9818 \| \| 0.0899 \| 86.36 \| 950 \| 0.5756 \| nan \| 0.9938 \| 0.4778 \| nan \| 0.9811 \| 0.0905 \| 0.7847 \| 0.7295 \| 0.9814 \| \| 0.0877 \| 87.27 \| 960 \| 0.5771 \| nan \| 0.9937 \| 0.4787 \| nan \| 0.9811 \| 0.0893 \| 0.7854 \| 0.7299 \| 0.9814 \| \| 0.0851 \| 88.18 \| 970 \| 0.5087 \| nan \| 0.9961 \| 0.4512 \| nan \| 0.9814 \| 0.0897 \| 0.7524 \| 0.7163 \| 0.9817 \| \| 0.0857 \| 89.09 \| 980 \| 0.5363 \| nan \| 0.9953 \| 0.4644 \| nan \| 0.9814 \| 0.0894 \| 0.7658 \| 0.7229 \| 0.9817 \| \| 0.0821 \| 90.0 \| 990 \| 0.5333 \| nan \| 0.9953 \| 0.4623 \| nan \| 0.9814 \| 0.0895 \| 0.7643 \| 0.7218 \| 0.9817 \| \| 0.0931 \| 90.91 \| 1000 \| 0.5581 \| nan \| 0.9944 \| 0.4718 \| nan \| 0.9812 \| 0.0895 \| 0.7763 \| 0.7265 \| 0.9815 \| \| 0.0787 \| 91.82 \| 1010 \| 0.5525 \| nan \| 0.9946 \| 0.4689 \| nan \| 0.9812 \| 0.0889 \| 0.7735 \| 0.7251 \| 0.9815 \| \| 0.0865 \| 92.73 \| 1020 \| 0.5659 \| nan \| 0.9941 \| 0.4746 \| nan \| 0.9812 \| 0.0883 \| 0.7800 \| 0.7279 \| 0.9815 \| \| 0.0939 \| 93.64 \| 1030 \| 0.5583 \| nan \| 0.9945 \| 0.4723 \| nan \| 0.9813 \| 0.0891 \| 0.7764 \| 0.7268 \| 0.9816 \| \| 0.0874 \| 94.55 \| 1040 \| 0.5258 \| nan \| 0.9955 \| 0.4580 \| nan \| 0.9813 \| 0.0893 \| 0.7607 \| 0.7197 \| 0.9816 \| \| 0.0927 \| 95.45 \| 1050 \| 0.5319 \| nan \| 0.9953 \| 0.4608 \| nan \| 0.9813 \| 0.0894 \| 0.7636 \| 0.7211 \| 0.9816 \| \| 0.0808 \| 96.36 \| 1060 \| 0.5444 \| nan \| 0.9949 \| 0.4665 \| nan \| 0.9813 \| 0.0897 \| 0.7696 \| 0.7239 \| 0.9816 \| \| 0.0924 \| 97.27 \| 1070 \| 0.5445 \| nan \| 0.9950 \| 0.4671 \| nan \| 0.9814 \| 0.0892 \| 0.7697 \| 0.7243 \| 0.9817 \| \| 0.08 \| 98.18 \| 1080 \| 0.5522 \| nan \| 0.9947 \| 0.4703 \| nan \| 0.9813 \| 0.0884 \| 0.7735 \| 0.7258 \| 0.9816 \| \| 0.0798 \| 99.09 \| 1090 \| 0.0880 \| 0.7300 \| 0.7842 \| 0.9815 \| nan \| 0.5745 \| 0.9939 \| nan \| 0.4788 \| 0.9812 \| \| 0.0789 \| 100.0 \| 1100 \| 0.0877 \| 0.7231 \| 0.7668 \| 0.9817 \| nan \| 0.5383 \| 0.9952 \| nan \| 0.4647 \| 0.9814 \| \| 0.0801 \| 100.91 \| 1110 \| 0.0885 \| 0.7232 \| 0.7677 \| 0.9816 \| nan \| 0.5404 \| 0.9951 \| nan \| 0.4650 \| 0.9813 \| \| 0.1043 \| 101.82 \| 1120 \| 0.0891 \| 0.7242 \| 0.7697 \| 0.9816 \| nan \| 0.5445 \| 0.9950 \| nan \| 0.4670 \| 0.9813 \| \| 0.0893 \| 102.73 \| 1130 \| 0.0882 \| 0.7263 \| 0.7728 \| 0.9817 \| nan \| 0.5508 \| 0.9949 \| nan \| 0.4712 \| 0.9814 \| \| 0.0923 \| 103.64 \| 1140 \| 0.0892 \| 0.7134 \| 0.7504 \| 0.9815 \| nan \| 0.5048 \| 0.9960 \| nan \| 0.4457 \| 0.9812 \| \| 0.0915 \| 104.55 \| 1150 \| 0.0884 \| 0.7293 \| 0.7795 \| 0.9817 \| nan \| 0.5646 \| 0.9944 \| nan \| 0.4772 \| 0.9814 \| \| 0.0859 \| 105.45 \| 1160 \| 0.0880 \| 0.7340 \| 0.7941 \| 0.9815 \| nan \| 0.5949 \| 0.9932 \| nan \| 0.4869 \| 0.9811 \| \| 0.0872 \| 106.36 \| 1170 \| 0.0872 \| 0.7298 \| 0.7815 \| 0.9817 \| nan \| 0.5688 \| 0.9942 \| nan \| 0.4783 \| 0.9814 \| \| 0.0845 \| 107.27 \| 1180 \| 0.0881 \| 0.7310 \| 0.7843 \| 0.9817 \| nan \| 0.5746 \| 0.9940 \| nan \| 0.4806 \| 0.9813 \| \| 0.0842 \| 108.18 \| 1190 \| 0.0869 \| 0.7285 \| 0.7766 \| 0.9818 \| nan \| 0.5584 \| 0.9947 \| nan \| 0.4755 \| 0.9815 \| \| 0.0906 \| 109.09 \| 1200 \| 0.0875 \| 0.7277 \| 0.7754 \| 0.9818 \| nan \| 0.5560 \| 0.9947 \| nan \| 0.4740 \| 0.9815 \| \| 0.0953 \| 110.0 \| 1210 \| 0.0878 \| 0.7289 \| 0.7777 \| 0.9818 \| nan \| 0.5608 \| 0.9946 \| nan \| 0.4764 \| 0.9815 \| \| 0.0988 \| 110.91 \| 1220 \| 0.0880 \| 0.7303 \| 0.7809 \| 0.9818 \| nan \| 0.5674 \| 0.9944 \| nan \| 0.4790 \| 0.9815 \| \| 0.0894 \| 111.82 \| 1230 \| 0.0869 \| 0.7300 \| 0.7801 \| 0.9818 \| nan \| 0.5657 \| 0.9945 \| nan \| 0.4785 \| 0.9815 \| \| 0.0788 \| 112.73 \| 1240 \| 0.0868 \| 0.7283 \| 0.7758 \| 0.9818 \| nan \| 0.5569 \| 0.9948 \| nan \| 0.4750 \| 0.9815 \| \| 0.0793 \| 113.64 \| 1250 \| 0.0870 \| 0.7281 \| 0.7758 \| 0.9818 \| nan \| 0.5569 \| 0.9947 \| nan \| 0.4747 \| 0.9815 \| \| 0.084 \| 114.55 \| 1260 \| 0.0874 \| 0.7295 \| 0.7809 \| 0.9817 \| nan \| 0.5675 \| 0.9943 \| nan \| 0.4777 \| 0.9814 \| \| 0.0832 \| 115.45 \| 1270 \| 0.0875 \| 0.7277 \| 0.7760 \| 0.9817 \| nan \| 0.5574 \| 0.9946 \| nan \| 0.4739 \| 0.9814 \| \| 0.0833 \| 116.36 \| 1280 \| 0.0873 \| 0.7274 \| 0.7755 \| 0.9817 \| nan \| 0.5563 \| 0.9947 \| nan \| 0.4735 \| 0.9814 \| \| 0.0786 \| 117.27 \| 1290 \| 0.0867 \| 0.7277 \| 0.7754 \| 0.9818 \| nan \| 0.5561 \| 0.9947 \| nan \| 0.4740 \| 0.9815 \| \| 0.0839 \| 118.18 \| 1300 \| 0.0865 \| 0.7285 \| 0.7779 \| 0.9817 \| nan \| 0.5613 \| 0.9945 \| nan \| 0.4755 \| 0.9814 \| \| 0.0847 \| 119.09 \| 1310 \| 0.0877 \| 0.7293 \| 0.7816 \| 0.9816 \| nan \| 0.5691 \| 0.9941 \| nan \| 0.4773 \| 0.9813 \| \| 0.0933 \| 120.0 \| 1320 \| 0.0867 \| 0.7280 \| 0.7762 \| 0.9818 \| nan \| 0.5578 \| 0.9947 \| nan \| 0.4745 \| 0.9814 \| ### Framework versions - Transformers 4.30.2 - Pytorch 2.0.1+cu117 - Datasets 2.13.1 - Tokenizers 0.13.3	{"license": "other", "tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "safety-utcustom-train-SF-RGBD-b5", "results": []}]}	image-segmentation	sam1120/safety-utcustom-train-SF-RGBD-b5	[ "transformers", "pytorch", "tensorboard", "segformer", "vision", "image-segmentation", "generated_from_trainer", "license:other", "endpoints_compatible", "region:us" ]	2024-02-06T05:10:19+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us	safety-utcustom-train-SF-RGBD-b5 ================================ This model is a fine-tuned version of nvidia/mit-b5 on the sam1120/safety-utcustom-TRAIN dataset. It achieves the following results on the evaluation set: * Loss: 0.0867 * Mean Iou: 0.7280 * Mean Accuracy: 0.7762 * Overall Accuracy: 0.9818 * Accuracy Unlabeled: nan * Accuracy Safe: 0.5578 * Accuracy Unsafe: 0.9947 * Iou Unlabeled: nan * Iou Safe: 0.4745 * Iou Unsafe: 0.9814 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 4e-06 * train\_batch\_size: 15 * eval\_batch\_size: 15 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.05 * num\_epochs: 120 ### Training results ### Framework versions * Transformers 4.30.2 * Pytorch 2.0.1+cu117 * Datasets 2.13.1 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120", "### Training results", "### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ 48, 117, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #segformer #vision #image-segmentation #generated_from_trainer #license-other #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 4e-06\n* train\\_batch\\_size: 15\n* eval\\_batch\\_size: 15\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.05\n* num\\_epochs: 120### Training results### Framework versions\n\n\n* Transformers 4.30.2\n* Pytorch 2.0.1+cu117\n* Datasets 2.13.1\n* Tokenizers 0.13.3" ]	[ -0.10608691722154617, 0.03593092039227486, -0.001977757550776005, 0.11570718884468079, 0.17274364829063416, 0.02880287356674671, 0.1164056807756424, 0.11417382210493088, -0.11002407968044281, 0.03021562658250332, 0.10483107715845108, 0.14139693975448608, 0.01553534995764494, 0.09455715119838715, -0.02047760784626007, -0.3077249526977539, -0.025350933894515038, 0.03178860619664192, -0.08702671527862549, 0.12561443448066711, 0.06431467086076736, -0.1626301258802414, 0.0913885310292244, -0.0041993604972958565, -0.2213001698255539, 0.016075706109404564, -0.004638391546905041, -0.031258247792720795, 0.1599779725074768, 0.023195939138531685, 0.11598169803619385, 0.009773077443242073, 0.11373211443424225, -0.20232029259204865, 0.01815521903336048, 0.05520845577120781, -0.004302386660128832, 0.06771158427000046, 0.06416109204292297, 0.002084829146042466, 0.15074364840984344, -0.10706237703561783, 0.06794559210538864, 0.001361866481602192, -0.14491651952266693, -0.212060809135437, -0.0734177678823471, 0.02312960848212242, 0.07788404077291489, 0.09719705581665039, -0.005637998227030039, 0.11262689530849457, -0.09264029562473297, 0.11236855387687683, 0.26985809206962585, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	shidowake/cyber2-7B-base-bnb-4bit	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	2024-02-06T05:11:07+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.049007222056388855, 0.16460949182510376, -0.005271392408758402, 0.021910345181822777, 0.09685911983251572, 0.01403510570526123, 0.07018975168466568, 0.11002060770988464, -0.02425350993871689, 0.11399492621421814, 0.03344893455505371, 0.09780009090900421, 0.11368958652019501, 0.1498505026102066, -0.002398149576038122, -0.23227156698703766, 0.04924226179718971, -0.1249755248427391, -0.03746527433395386, 0.1159956082701683, 0.15001481771469116, -0.10170940309762955, 0.07611104100942612, -0.029819702729582787, -0.008722295984625816, -0.032589927315711975, -0.056551046669483185, -0.04997202008962631, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Data Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"}	null	simonycl/llama-2-7b-hf-cohere-KMenasRandomDeita-0.05-Llama-2-7b-hf-2e-5	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]	2024-02-06T05:15:55+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 41, 6, 3, 45, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.10578131675720215, 0.17948102951049805, -0.0035145513247698545, 0.03891412168741226, 0.08621077984571457, 0.021764913573861122, 0.05196864902973175, 0.1246534213423729, -0.04679914936423302, 0.10144586861133575, 0.05955370143055916, 0.10162605345249176, 0.09514814615249634, 0.19481638073921204, -0.003711341181769967, -0.19174233078956604, 0.019053086638450623, -0.09994730353355408, -0.005330710206180811, 0.12174459546804428, 0.15323303639888763, -0.09489279985427856, 0.08602795749902725, -0.017800018191337585, -0.013100131414830685, -0.033980559557676315, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-lora-592K-snli-model3 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8978 - Accuracy: 0.645 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 46 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.6372 \| 1.0 \| 2146 \| 0.5562 \| 0.7846 \| \| 0.5816 \| 2.0 \| 4292 \| 0.5116 \| 0.8021 \| \| 0.564 \| 3.0 \| 6438 \| 0.4957 \| 0.8072 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-base-cased-lora-592K-snli-model3", "results": []}]}	text-classification	varun-v-rao/bert-base-cased-lora-592K-snli-model3	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T05:23:02+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	bert-base-cased-lora-592K-snli-model3 ===================================== This model is a fine-tuned version of bert-base-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.8978 * Accuracy: 0.645 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 256 * eval\_batch\_size: 256 * seed: 46 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 46\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 46\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 67, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 46\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.08975468575954437, 0.10616015642881393, -0.002115728100761771, 0.10651184618473053, 0.15027600526809692, 0.02154955267906189, 0.15638510882854462, 0.10905218869447708, -0.07903268933296204, 0.046616457402706146, 0.12526820600032806, 0.12785860896110535, 0.01492181234061718, 0.12948037683963776, -0.062362026423215866, -0.22526569664478302, 0.008811607956886292, 0.03307228162884712, -0.07077199965715408, 0.1171075850725174, 0.09044181555509567, 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null	null	transformers	# MarcoroCapy-7B This model is a DPO fine tune of [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp) on [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized) <div align="center"> ![image/webp](https://cdn-uploads.huggingface.co/production/uploads/6455cc8d679315e4ef16fbec/oey_JDcpqQ0Lw-7KH0AIE.webp) [<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-dark.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel) </div> ## Process + Realigned the chat template to ChatML + Completed 1 Epoch + 5e-5 learning rate + Training time was about 4.5 hours on 1 H100 + Cost was ~$20 ## GGUF TODO ## Evaluations TODO	{"library_name": "transformers", "tags": []}	text-generation	macadeliccc/MarcoroCapy-7B	[ "transformers", "safetensors", "mistral", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T05:26:34+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# MarcoroCapy-7B This model is a DPO fine tune of mlabonne/Marcoro14-7B-slerp on argilla/distilabel-capybara-dpo-7k-binarized <div align="center"> !image/webp <img src="URL alt="Built with Distilabel" width="200" height="32"/> </div> ## Process + Realigned the chat template to ChatML + Completed 1 Epoch + 5e-5 learning rate + Training time was about 4.5 hours on 1 H100 + Cost was ~$20 ## GGUF TODO ## Evaluations TODO	[ "# MarcoroCapy-7B\n\nThis model is a DPO fine tune of mlabonne/Marcoro14-7B-slerp on argilla/distilabel-capybara-dpo-7k-binarized\n\n<div align=\"center\"> \n\n!image/webp\n\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>\n\n</div>", "## Process\n\n+ Realigned the chat template to ChatML \n+ Completed 1 Epoch\n+ 5e-5 learning rate\n+ Training time was about 4.5 hours on 1 H100\n+ Cost was ~$20", "## GGUF\n\nTODO", "## Evaluations\n\nTODO" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MarcoroCapy-7B\n\nThis model is a DPO fine tune of mlabonne/Marcoro14-7B-slerp on argilla/distilabel-capybara-dpo-7k-binarized\n\n<div align=\"center\"> \n\n!image/webp\n\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>\n\n</div>", "## Process\n\n+ Realigned the chat template to ChatML \n+ Completed 1 Epoch\n+ 5e-5 learning rate\n+ Training time was about 4.5 hours on 1 H100\n+ Cost was ~$20", "## GGUF\n\nTODO", "## Evaluations\n\nTODO" ]	[ 47, 91, 41, 6, 5 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MarcoroCapy-7B\n\nThis model is a DPO fine tune of mlabonne/Marcoro14-7B-slerp on argilla/distilabel-capybara-dpo-7k-binarized\n\n<div align=\"center\"> \n\n!image/webp\n\n<img src=\"URL alt=\"Built with Distilabel\" width=\"200\" height=\"32\"/>\n\n</div>## Process\n\n+ Realigned the chat template to ChatML \n+ Completed 1 Epoch\n+ 5e-5 learning rate\n+ Training time was about 4.5 hours on 1 H100\n+ Cost was ~$20## GGUF\n\nTODO## Evaluations\n\nTODO" ]	[ -0.14279377460479736, 0.010144504718482494, -0.0007890493143349886, 0.020312225446105003, 0.12760287523269653, -0.04121774062514305, 0.14451441168785095, 0.05564195290207863, -0.016270365566015244, 0.0357138067483902, 0.06444784998893738, 0.10640467703342438, 0.05382278189063072, 0.20492969453334808, -0.11337487399578094, -0.22819288074970245, 0.06786365061998367, 0.007044428028166294, 0.015516088344156742, 0.0931447297334671, 0.12585166096687317, -0.07392016798257828, 0.06829650700092316, -0.005876957438886166, -0.09072398394346237, -0.04663916677236557, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	TinyPixel/qwen-1	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-06T05:35:22+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	# LLaVA Model Card: This is a fork of https://huggingface.co/YouLiXiya/tinyllava-v1.0-1.1b-hf from January 2024 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/FPshq08TKYD0e-qwPLDVO.png) Below is the model card of TinyLlava model 1.1b. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1XtdA_UoyNzqiEYVR-iWA-xmit8Y2tKV2#scrollTo=DFVZgElEQk3x) ## Model details Model type: TinyLLaVA is an open-source chatbot trained by fine-tuning TinyLlama on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Paper or resources for more information: https://llava-vl.github.io/ ## How to use the model First, make sure to have `transformers >= 4.35.3`. The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images: ### Using `pipeline`: Below we used [`"YouLiXiya/tinyllava-v1.0-1.1b-hf"`](https://huggingface.co/YouLiXiya/tinyllava-v1.0-1.1b-hf) checkpoint. ```python from transformers import pipeline from PIL import Image import requests model_id = "YouLiXiya/tinyllava-v1.0-1.1b-hf" pipe = pipeline("image-to-text", model=model_id) url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg" image = Image.open(requests.get(url, stream=True).raw) prompt = "USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:" outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200}) print(outputs) {'generated_text': 'USER: \nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: The label 15 represents lava, which is the type of rock that is formed from molten magma. '} ``` ### Using pure `transformers`: Below is an example script to run generation in `float16` precision on a GPU device: ```python import requests from PIL import Image import torch from transformers import AutoProcessor, LlavaForConditionalGeneration model_id = "YouLiXiya/tinyllava-v1.0-1.1b-hf" prompt = "USER: <image>\nWhat are these?\nASSISTANT:" image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, ).to(0) processor = AutoProcessor.from_pretrained(model_id) raw_image = Image.open(requests.get(image_file, stream=True).raw) inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16) output = model.generate(**inputs, max_new_tokens=200, do_sample=False) print(processor.decode(output[0][2:], skip_special_tokens=True)) ``` ### Model optimization #### 4-bit quantization through `bitsandbytes` library First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: ```diff model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + load_in_4bit=True ) ``` #### Use Flash-Attention 2 to further speed-up generation First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with: ```diff model = LlavaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.float16, low_cpu_mem_usage=True, + use_flash_attention_2=True ).to(0) ``` ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.	{"language": ["en"], "license": "apache-2.0", "pipeline_tag": "image-to-text", "inference": false, "arxiv": 2304.08485}	image-to-text	sujitvasanth/YouLiXiya-tinyllava-v1.0-1.1b-hf	[ "transformers", "safetensors", "llava", "pretraining", "image-to-text", "en", "license:apache-2.0", "region:us" ]	2024-02-06T05:46:03+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llava #pretraining #image-to-text #en #license-apache-2.0 #region-us	# LLaVA Model Card: This is a fork of URL from January 2024 !image/png Below is the model card of TinyLlava model 1.1b. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: ![Open In Colab](URL ## Model details Model type: TinyLLaVA is an open-source chatbot trained by fine-tuning TinyLlama on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Paper or resources for more information: URL ## How to use the model First, make sure to have 'transformers >= 4.35.3'. The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template ('USER: xxx\nASSISTANT:') and add the token '<image>' to the location where you want to query images: ### Using 'pipeline': Below we used '"YouLiXiya/tinyllava-v1.0-1.1b-hf"' checkpoint. ### Using pure 'transformers': Below is an example script to run generation in 'float16' precision on a GPU device: ### Model optimization #### 4-bit quantization through 'bitsandbytes' library First make sure to install 'bitsandbytes', 'pip install bitsandbytes' and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: #### Use Flash-Attention 2 to further speed-up generation First make sure to install 'flash-attn'. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with: ## License Llama 2 is licensed under the LLAMA 2 Community License, Copyright (c) Meta Platforms, Inc. All Rights Reserved.	[ "# LLaVA Model Card: This is a fork of URL from January 2024\n\n!image/png\n\n\n\nBelow is the model card of TinyLlava model 1.1b.\n\nCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: ![Open In Colab](URL", "## Model details\n\nModel type:\nTinyLLaVA is an open-source chatbot trained by fine-tuning TinyLlama on GPT-generated multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture.\n\nPaper or resources for more information:\nURL", "## How to use the model\n\nFirst, make sure to have 'transformers >= 4.35.3'. \nThe model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template ('USER: xxx\\nASSISTANT:') and add the token '<image>' to the location where you want to query images:", "### Using 'pipeline':\n\nBelow we used '\"YouLiXiya/tinyllava-v1.0-1.1b-hf\"' checkpoint.", "### Using pure 'transformers':\n\nBelow is an example script to run generation in 'float16' precision on a GPU device:", "### Model optimization", "#### 4-bit quantization through 'bitsandbytes' library\n\nFirst make sure to install 'bitsandbytes', 'pip install bitsandbytes' and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:", "#### Use Flash-Attention 2 to further speed-up generation\n\nFirst make sure to install 'flash-attn'. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:", "## License\nLlama 2 is licensed under the LLAMA 2 Community License, \nCopyright (c) Meta Platforms, Inc. All Rights Reserved." ]	[ "TAGS\n#transformers #safetensors #llava #pretraining #image-to-text #en #license-apache-2.0 #region-us \n", "# LLaVA Model Card: This is a fork of URL from January 2024\n\n!image/png\n\n\n\nBelow is the model card of TinyLlava model 1.1b.\n\nCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: ![Open In Colab](URL", "## Model details\n\nModel type:\nTinyLLaVA is an open-source chatbot trained by fine-tuning TinyLlama on GPT-generated multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture.\n\nPaper or resources for more information:\nURL", "## How to use the model\n\nFirst, make sure to have 'transformers >= 4.35.3'. \nThe model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template ('USER: xxx\\nASSISTANT:') and add the token '<image>' to the location where you want to query images:", "### Using 'pipeline':\n\nBelow we used '\"YouLiXiya/tinyllava-v1.0-1.1b-hf\"' checkpoint.", "### Using pure 'transformers':\n\nBelow is an example script to run generation in 'float16' precision on a GPU device:", "### Model optimization", "#### 4-bit quantization through 'bitsandbytes' library\n\nFirst make sure to install 'bitsandbytes', 'pip install bitsandbytes' and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:", "#### Use Flash-Attention 2 to further speed-up generation\n\nFirst make sure to install 'flash-attn'. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:", "## License\nLlama 2 is licensed under the LLAMA 2 Community License, \nCopyright (c) Meta Platforms, Inc. All Rights Reserved." ]	[ 36, 68, 72, 95, 38, 32, 5, 60, 52, 30 ]	[ "passage: TAGS\n#transformers #safetensors #llava #pretraining #image-to-text #en #license-apache-2.0 #region-us \n# LLaVA Model Card: This is a fork of URL from January 2024\n\n!image/png\n\n\n\nBelow is the model card of TinyLlava model 1.1b.\n\nCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: ![Open In Colab](URL## Model details\n\nModel type:\nTinyLLaVA is an open-source chatbot trained by fine-tuning TinyLlama on GPT-generated multimodal instruction-following data.\nIt is an auto-regressive language model, based on the transformer architecture.\n\nPaper or resources for more information:\nURL## How to use the model\n\nFirst, make sure to have 'transformers >= 4.35.3'. \nThe model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template ('USER: xxx\\nASSISTANT:') and add the token '<image>' to the location where you want to query images:### Using 'pipeline':\n\nBelow we used '\"YouLiXiya/tinyllava-v1.0-1.1b-hf\"' checkpoint.### Using pure 'transformers':\n\nBelow is an example script to run generation in 'float16' precision on a GPU device:### Model optimization#### 4-bit quantization through 'bitsandbytes' library\n\nFirst make sure to install 'bitsandbytes', 'pip install bitsandbytes' and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with:#### Use Flash-Attention 2 to further speed-up generation\n\nFirst make sure to install 'flash-attn'. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:## License\nLlama 2 is licensed under the LLAMA 2 Community License, \nCopyright (c) Meta Platforms, Inc. 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text2text-generation	Kishan11/nepali-summ	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T05:47:11+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.051328662782907486, 0.16320262849330902, -0.005184381268918514, 0.02251756191253662, 0.0970073789358139, 0.014154063537716866, 0.06843415647745132, 0.1107407882809639, -0.01975325681269169, 0.11416343599557877, 0.03334542736411095, 0.09905029088258743, 0.11230985820293427, 0.15210844576358795, -0.0026883550453931093, -0.22916288673877716, 0.04717505723237991, -0.126259446144104, -0.0391545407474041, 0.11665356904268265, 0.14936181902885437, -0.10113005340099335, 0.07722076028585434, -0.030988991260528564, -0.0076387980952858925, -0.032814256846904755, -0.0584266260266304, -0.04825444892048836, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # test_2_3136_files This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the audiofolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1824 - Accuracy: 0.9570 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0927 \| 0.96 \| 19 \| 0.1981 \| 0.9347 \| \| 0.0582 \| 1.97 \| 39 \| 0.1831 \| 0.9506 \| \| 0.046 \| 2.99 \| 59 \| 0.1881 \| 0.9570 \| \| 0.0465 \| 4.0 \| 79 \| 0.3427 \| 0.9188 \| \| 0.0651 \| 4.96 \| 98 \| 0.2147 \| 0.9315 \| \| 0.0178 \| 5.97 \| 118 \| 0.1690 \| 0.9602 \| \| 0.0318 \| 6.99 \| 138 \| 0.2515 \| 0.9475 \| \| 0.0377 \| 8.0 \| 158 \| 0.1561 \| 0.9570 \| \| 0.0277 \| 8.96 \| 177 \| 0.2090 \| 0.9538 \| \| 0.0069 \| 9.62 \| 190 \| 0.1824 \| 0.9570 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["audiofolder"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "test_2_3136_files", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "audiofolder", "type": "audiofolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9570063694267515, "name": "Accuracy"}]}]}]}	audio-classification	PatricioMN/test_2_3136_files	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:audiofolder", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-06T05:49:25+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us	test\_2\_3136\_files ==================== This model is a fine-tuned version of facebook/wav2vec2-base on the audiofolder dataset. It achieves the following results on the evaluation set: * Loss: 0.1824 * Accuracy: 0.9570 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 128 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 78, 144, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-audiofolder #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 128\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SMIDS_5x_beit_large_RMSProp_lr00001_fold2 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.2683 - Accuracy: 0.9085 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.2133 \| 1.0 \| 750 \| 0.4438 \| 0.8619 \| \| 0.0933 \| 2.0 \| 1500 \| 0.3902 \| 0.9068 \| \| 0.0066 \| 3.0 \| 2250 \| 0.7047 \| 0.8885 \| \| 0.0781 \| 4.0 \| 3000 \| 0.7479 \| 0.8968 \| \| 0.0212 \| 5.0 \| 3750 \| 0.8114 \| 0.9052 \| \| 0.0005 \| 6.0 \| 4500 \| 0.7637 \| 0.9035 \| \| 0.095 \| 7.0 \| 5250 \| 0.7892 \| 0.8952 \| \| 0.0016 \| 8.0 \| 6000 \| 0.7555 \| 0.9002 \| \| 0.0 \| 9.0 \| 6750 \| 0.8496 \| 0.9101 \| \| 0.0291 \| 10.0 \| 7500 \| 0.7725 \| 0.9218 \| \| 0.0031 \| 11.0 \| 8250 \| 1.0613 \| 0.8869 \| \| 0.0 \| 12.0 \| 9000 \| 0.7920 \| 0.9135 \| \| 0.0 \| 13.0 \| 9750 \| 1.0124 \| 0.9002 \| \| 0.0021 \| 14.0 \| 10500 \| 0.9293 \| 0.8935 \| \| 0.0 \| 15.0 \| 11250 \| 0.8528 \| 0.8985 \| \| 0.0 \| 16.0 \| 12000 \| 1.0130 \| 0.9002 \| \| 0.0 \| 17.0 \| 12750 \| 0.8948 \| 0.8952 \| \| 0.0025 \| 18.0 \| 13500 \| 0.9897 \| 0.8952 \| \| 0.0 \| 19.0 \| 14250 \| 1.0959 \| 0.9002 \| \| 0.0 \| 20.0 \| 15000 \| 0.9871 \| 0.9151 \| \| 0.0 \| 21.0 \| 15750 \| 1.1370 \| 0.8968 \| \| 0.0 \| 22.0 \| 16500 \| 1.1472 \| 0.8935 \| \| 0.0032 \| 23.0 \| 17250 \| 0.9326 \| 0.9002 \| \| 0.0012 \| 24.0 \| 18000 \| 1.1430 \| 0.8852 \| \| 0.0183 \| 25.0 \| 18750 \| 1.0681 \| 0.8985 \| \| 0.0 \| 26.0 \| 19500 \| 1.1400 \| 0.9052 \| \| 0.0136 \| 27.0 \| 20250 \| 1.3202 \| 0.8902 \| \| 0.0 \| 28.0 \| 21000 \| 1.1445 \| 0.8935 \| \| 0.0 \| 29.0 \| 21750 \| 1.2039 \| 0.8852 \| \| 0.0102 \| 30.0 \| 22500 \| 1.0653 \| 0.8985 \| \| 0.0 \| 31.0 \| 23250 \| 1.0496 \| 0.9085 \| \| 0.0 \| 32.0 \| 24000 \| 1.0494 \| 0.9085 \| \| 0.0 \| 33.0 \| 24750 \| 1.2033 \| 0.9018 \| \| 0.0245 \| 34.0 \| 25500 \| 1.2752 \| 0.9052 \| \| 0.0048 \| 35.0 \| 26250 \| 1.1112 \| 0.9168 \| \| 0.0 \| 36.0 \| 27000 \| 1.0978 \| 0.9135 \| \| 0.0085 \| 37.0 \| 27750 \| 1.3039 \| 0.9018 \| \| 0.0 \| 38.0 \| 28500 \| 1.1925 \| 0.9101 \| \| 0.0 \| 39.0 \| 29250 \| 1.2043 \| 0.9068 \| \| 0.0 \| 40.0 \| 30000 \| 1.1617 \| 0.9085 \| \| 0.0 \| 41.0 \| 30750 \| 1.2087 \| 0.9068 \| \| 0.0 \| 42.0 \| 31500 \| 1.2116 \| 0.9101 \| \| 0.0 \| 43.0 \| 32250 \| 1.2536 \| 0.9118 \| \| 0.0 \| 44.0 \| 33000 \| 1.2498 \| 0.9101 \| \| 0.0 \| 45.0 \| 33750 \| 1.2443 \| 0.9118 \| \| 0.0 \| 46.0 \| 34500 \| 1.2623 \| 0.9085 \| \| 0.0 \| 47.0 \| 35250 \| 1.2620 \| 0.9085 \| \| 0.0 \| 48.0 \| 36000 \| 1.2632 \| 0.9085 \| \| 0.0 \| 49.0 \| 36750 \| 1.2697 \| 0.9085 \| \| 0.0 \| 50.0 \| 37500 \| 1.2683 \| 0.9085 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_5x_beit_large_RMSProp_lr00001_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.908485856905158, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_5x_beit_large_RMSProp_lr00001_fold2	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T05:49:49+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_5x\_beit\_large\_RMSProp\_lr00001\_fold2 =============================================== This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.2683 * Accuracy: 0.9085 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 116, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.1292150914669037, 0.17132072150707245, -0.002415567170828581, 0.13183215260505676, 0.11657863855361938, 0.020753253251314163, 0.1335890144109726, 0.16620413959026337, -0.08238927274942398, 0.04929587244987488, 0.13697229325771332, 0.1357421576976776, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "openai/whisper-large-v3"}	null	kenilshah35/whisper-large-dictation	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-large-v3", "region:us" ]	2024-02-06T05:53:38+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-openai/whisper-large-v3 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-openai/whisper-large-v3 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 40, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-openai/whisper-large-v3 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.12347478419542313, 0.2092142105102539, -0.002684095874428749, 0.03130857273936272, 0.08190006017684937, 0.019184697419404984, 0.048305243253707886, 0.12903062999248505, 0.008469058200716972, 0.10618655383586884, 0.07007666677236557, 0.11070923507213593, 0.11244861781597137, 0.215914785861969, 0.0028417520225048065, -0.17289768159389496, 0.02935832180082798, -0.08825898915529251, 0.004292030353099108, 0.12655508518218994, 0.14238038659095764, -0.1010560393333435, 0.08274201303720474, -0.013286365196108818, 0.001807458815164864, -0.03917964920401573, 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null	null	transformers	# miqu-1-120b ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6303ca537373aacccd85d8a7/LxO9j7OykuabKLYQHIodG.jpeg) * EXL2: 2.4bpw \| [2.65bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.65bpw-h6-exl2) \| [3.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-3.0bpw-h6-exl2) \| [4.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-4.0bpw-h6-exl2) \| [5.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-5.0bpw-h6-exl2) * GGUF: [Q2_K-Q5_K_M](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-GGUF/) \| [IQ3_XXS](https://huggingface.co/wolfram/miqu-1-120b-GGUF) * HF FP16: [wolfram/miqu-1-120b](https://huggingface.co/wolfram/miqu-1-120b) This is a 120b frankenmerge of [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) created by interleaving layers of [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) with itself using [mergekit](https://github.com/cg123/mergekit). Inspired by [Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2), [MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b), and [goliath-120b](https://huggingface.co/alpindale/goliath-120b). Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, [Lone Striker](https://huggingface.co/LoneStriker)! ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` See also: [🐺🐦‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/18ljvxb/llm_prompt_format_comparisontest_mixtral_8x7b/) ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 20] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [10, 30] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [20, 40] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [30, 50] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [40, 60] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [50, 70] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [60, 80] model: 152334H/miqu-1-70b-sf ``` ## Credits & Special Thanks * original (unreleased) model: [mistralai (Mistral AI_)](https://huggingface.co/mistralai) * leaked model: [miqudev/miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) * f16 model: [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) * mergekit: [arcee-ai/mergekit: Tools for merging pretrained large language models.](https://github.com/arcee-ai/mergekit) * mergekit_config.yml: [nsfwthrowitaway69/Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2) ### Support * [My Ko-fi page](https://ko-fi.com/wolframravenwolf) if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS [BASED ON A LEAKED ASSET](https://huggingface.co/miqudev/miqu-1-70b/discussions/10) AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	{"language": ["en", "de", "fr", "es", "it"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["152334H/miqu-1-70b-sf"]}	text-generation	LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "de", "fr", "es", "it", "base_model:152334H/miqu-1-70b-sf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T05:54:05+00:00	[]	[ "en", "de", "fr", "es", "it" ]	TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# miqu-1-120b !image/jpeg * EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw * GGUF: Q2_K-Q5_K_M \| IQ3_XXS * HF FP16: wolfram/miqu-1-120b This is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit. Inspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b. Thanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, Lone Striker! ## Prompt template: Mistral See also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * 152334H/miqu-1-70b-sf ### Configuration The following YAML configuration was used to produce this model: ## Credits & Special Thanks * original (unreleased) model: mistralai (Mistral AI_) * leaked model: miqudev/miqu-1-70b * f16 model: 152334H/miqu-1-70b-sf * mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models. * mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2 ### Support * My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	[ "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ 85, 206, 44, 31, 4, 17, 28, 17, 107, 69, 43 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ -0.08452296257019043, 0.07996358722448349, -0.006216804962605238, 0.07489742338657379, 0.062447689473629, 0.048032015562057495, 0.10850238054990768, 0.11730548739433289, 0.04997159540653229, 0.1304655373096466, 0.019059622660279274, 0.0882313996553421, 0.10314993560314178, 0.1546645164489746, -0.0008784975507296622, -0.13700617849826813, 0.03406155854463577, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-lora-1.77M-snli-model2 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7454 - Accuracy: 0.729 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 27 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5225 \| 1.0 \| 2146 \| 0.4209 \| 0.8470 \| \| 0.4716 \| 2.0 \| 4292 \| 0.3864 \| 0.8562 \| \| 0.4577 \| 3.0 \| 6438 \| 0.3797 \| 0.8591 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "t5-base", "model-index": [{"name": "t5-base-lora-1.77M-snli-model2", "results": []}]}	text-classification	varun-v-rao/t5-base-lora-1.77M-snli-model2	[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:t5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T05:56:37+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	t5-base-lora-1.77M-snli-model2 ============================== This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7454 * Accuracy: 0.729 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 256 * eval\_batch\_size: 256 * seed: 27 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 27\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 27\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 74, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 27\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.08444473147392273, 0.09445285052061081, -0.002206584205850959, 0.10973017662763596, 0.14101476967334747, 0.0062677632085978985, 0.16297617554664612, 0.11735495924949646, -0.07650014758110046, 0.04575206711888313, 0.12562991678714752, 0.12411639839410782, 0.030785663053393364, 0.13894754648208618, -0.08589791506528854, -0.22002537548542023, 0.013962888158857822, 0.020629338920116425, -0.05578804016113281, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "meta-llama/Llama-2-7b-hf"}	null	RohanKumarMishra/llama_dp2	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:meta-llama/Llama-2-7b-hf", "region:us" ]	2024-02-06T05:57:41+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 41, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-meta-llama/Llama-2-7b-hf #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.13237035274505615, 0.20393601059913635, -0.002511046128347516, 0.02874687872827053, 0.07912357151508331, 0.019634027034044266, 0.04979075863957405, 0.13531364500522614, 0.020043307915329933, 0.10580451786518097, 0.0737132653594017, 0.11718367785215378, 0.10926163196563721, 0.20654499530792236, 0.003890186781063676, -0.15440793335437775, 0.024214256554841995, -0.08298544585704803, 0.006814117077738047, 0.1290476769208908, 0.14319083094596863, -0.10468140989542007, 0.0831538662314415, -0.014203370548784733, 0.0008161105797626078, -0.03246506303548813, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "alexsherstinsky/Mistral-7B-v0.1-sharded"}	null	SudiptoPramanik/Mistral_RL_RL_ExtractiveSummary	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:alexsherstinsky/Mistral-7B-v0.1-sharded", "region:us" ]	2024-02-06T06:06:37+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-alexsherstinsky/Mistral-7B-v0.1-sharded #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-alexsherstinsky/Mistral-7B-v0.1-sharded #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 45, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-alexsherstinsky/Mistral-7B-v0.1-sharded #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.110109843313694, 0.20426276326179504, -0.0033242744393646717, 0.02683502435684204, 0.07316219061613083, 0.012933526188135147, 0.07240378856658936, 0.13029123842716217, 0.0330861434340477, 0.12623506784439087, 0.0663594976067543, 0.11143319308757782, 0.11400832235813141, 0.21561922132968903, -0.003767105983570218, -0.17360882461071014, 0.020239561796188354, -0.05931909754872322, 0.034331753849983215, 0.12480589002370834, 0.13765163719654083, -0.09187468141317368, 0.07441461831331253, -0.023067325353622437, -0.007257964462041855, -0.030011918395757675, 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null	null	transformers	# miqu-1-120b ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6303ca537373aacccd85d8a7/LxO9j7OykuabKLYQHIodG.jpeg) * EXL2: [2.4bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2) \| 2.65bpw \| [3.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-3.0bpw-h6-exl2) \| [4.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-4.0bpw-h6-exl2) \| [5.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-5.0bpw-h6-exl2) * GGUF: [Q2_K-Q5_K_M](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-GGUF/) \| [IQ3_XXS](https://huggingface.co/wolfram/miqu-1-120b-GGUF) * HF FP16: [wolfram/miqu-1-120b](https://huggingface.co/wolfram/miqu-1-120b) This is a 120b frankenmerge of [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) created by interleaving layers of [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) with itself using [mergekit](https://github.com/cg123/mergekit). Inspired by [Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2), [MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b), and [goliath-120b](https://huggingface.co/alpindale/goliath-120b). Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, [Lone Striker](https://huggingface.co/LoneStriker)! ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` See also: [🐺🐦‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/18ljvxb/llm_prompt_format_comparisontest_mixtral_8x7b/) ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 20] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [10, 30] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [20, 40] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [30, 50] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [40, 60] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [50, 70] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [60, 80] model: 152334H/miqu-1-70b-sf ``` ## Credits & Special Thanks * original (unreleased) model: [mistralai (Mistral AI_)](https://huggingface.co/mistralai) * leaked model: [miqudev/miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) * f16 model: [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) * mergekit: [arcee-ai/mergekit: Tools for merging pretrained large language models.](https://github.com/arcee-ai/mergekit) * mergekit_config.yml: [nsfwthrowitaway69/Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2) ### Support * [My Ko-fi page](https://ko-fi.com/wolframravenwolf) if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS [BASED ON A LEAKED ASSET](https://huggingface.co/miqudev/miqu-1-70b/discussions/10) AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	{"language": ["en", "de", "fr", "es", "it"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["152334H/miqu-1-70b-sf"]}	text-generation	LoneStriker/wolfram_miqu-1-120b-2.65bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "de", "fr", "es", "it", "base_model:152334H/miqu-1-70b-sf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:09:50+00:00	[]	[ "en", "de", "fr", "es", "it" ]	TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# miqu-1-120b !image/jpeg * EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw * GGUF: Q2_K-Q5_K_M \| IQ3_XXS * HF FP16: wolfram/miqu-1-120b This is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit. Inspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b. Thanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, Lone Striker! ## Prompt template: Mistral See also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * 152334H/miqu-1-70b-sf ### Configuration The following YAML configuration was used to produce this model: ## Credits & Special Thanks * original (unreleased) model: mistralai (Mistral AI_) * leaked model: miqudev/miqu-1-70b * f16 model: 152334H/miqu-1-70b-sf * mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models. * mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2 ### Support * My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	[ "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ 85, 206, 44, 31, 4, 17, 28, 17, 107, 69, 43 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ -0.08452296257019043, 0.07996358722448349, -0.006216804962605238, 0.07489742338657379, 0.062447689473629, 0.048032015562057495, 0.10850238054990768, 0.11730548739433289, 0.04997159540653229, 0.1304655373096466, 0.019059622660279274, 0.0882313996553421, 0.10314993560314178, 0.1546645164489746, -0.0008784975507296622, -0.13700617849826813, 0.03406155854463577, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-turkish-informal2formal_6.02.2024_1 This model is a fine-tuned version of [alpcansoydas/t5-turkish-informal2formal_30.01.2024_3](https://huggingface.co/alpcansoydas/t5-turkish-informal2formal_30.01.2024_3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1459 - Rouge1: 0.8953 - Rouge2: 0.7840 - Rougel: 0.8937 - Rougelsum: 0.8939 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 0.2779 \| 1.0 \| 818 \| 0.1459 \| 0.8953 \| 0.7840 \| 0.8937 \| 0.8939 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "alpcansoydas/t5-turkish-informal2formal_30.01.2024_3", "model-index": [{"name": "t5-turkish-informal2formal_6.02.2024_1", "results": []}]}	text2text-generation	alpcansoydas/t5-turkish-informal2formal_6.02.2024_1	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "base_model:alpcansoydas/t5-turkish-informal2formal_30.01.2024_3", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:10:17+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #base_model-alpcansoydas/t5-turkish-informal2formal_30.01.2024_3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	t5-turkish-informal2formal\_6.02.2024\_1 ======================================== This model is a fine-tuned version of alpcansoydas/t5-turkish-informal2formal\_30.01.2024\_3 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.1459 * Rouge1: 0.8953 * Rouge2: 0.7840 * Rougel: 0.8937 * Rougelsum: 0.8939 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0003 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #base_model-alpcansoydas/t5-turkish-informal2formal_30.01.2024_3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 86, 97, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #t5 #text2text-generation #generated_from_trainer #base_model-alpcansoydas/t5-turkish-informal2formal_30.01.2024_3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0003\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.10181097686290741, 0.061611875891685486, -0.0037703930865973234, 0.09674203395843506, 0.11984594911336899, 0.00984271988272667, 0.19014200568199158, 0.13951700925827026, -0.09205296635627747, 0.05088312551379204, 0.15390239655971527, 0.12694700062274933, 0.03817252814769745, 0.12204194813966751, -0.03428639471530914, -0.2597179114818573, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper tiny ZH This model is a fine-tuned version of [ZhihCheng/whisper-base-zh](https://huggingface.co/ZhihCheng/whisper-base-zh) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0002 - Wer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 25 - training_steps: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 0.0006 \| 0.67 \| 50 \| 0.0004 \| 0.0 \| \| 0.0004 \| 1.33 \| 100 \| 0.0002 \| 0.0 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.0	{"language": ["zh"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["ZhihCheng/Motor_jargon_dataset"], "metrics": ["wer"], "base_model": "ZhihCheng/whisper-base-zh", "model-index": [{"name": "Whisper tiny ZH", "results": []}]}	automatic-speech-recognition	ZhihCheng/whisper-base-zh_motor	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "zh", "dataset:ZhihCheng/Motor_jargon_dataset", "base_model:ZhihCheng/whisper-base-zh", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-06T06:10:48+00:00	[]	[ "zh" ]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #zh #dataset-ZhihCheng/Motor_jargon_dataset #base_model-ZhihCheng/whisper-base-zh #license-apache-2.0 #endpoints_compatible #region-us	Whisper tiny ZH =============== This model is a fine-tuned version of ZhihCheng/whisper-base-zh on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0002 * Wer: 0.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 25 * training\_steps: 100 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.0.1+cu117 * Datasets 2.16.1 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 25\n* training\\_steps: 100\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #zh #dataset-ZhihCheng/Motor_jargon_dataset #base_model-ZhihCheng/whisper-base-zh #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 25\n* training\\_steps: 100\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ 92, 130, 4, 38 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #zh #dataset-ZhihCheng/Motor_jargon_dataset #base_model-ZhihCheng/whisper-base-zh #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 25\n* training\\_steps: 100\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.0.1+cu117\n* Datasets 2.16.1\n* Tokenizers 0.15.0" ]	[ 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SMIDS_3x_beit_large_RMSProp_lr0001_fold1 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 1.1638 - Accuracy: 0.8848 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.467 \| 1.0 \| 451 \| 0.4114 \| 0.8230 \| \| 0.3239 \| 2.0 \| 902 \| 0.3424 \| 0.8648 \| \| 0.2765 \| 3.0 \| 1353 \| 0.4139 \| 0.8531 \| \| 0.2282 \| 4.0 \| 1804 \| 0.3451 \| 0.8865 \| \| 0.1605 \| 5.0 \| 2255 \| 0.4701 \| 0.8748 \| \| 0.1651 \| 6.0 \| 2706 \| 0.4789 \| 0.8798 \| \| 0.1547 \| 7.0 \| 3157 \| 0.4952 \| 0.8815 \| \| 0.1301 \| 8.0 \| 3608 \| 0.4426 \| 0.8715 \| \| 0.1681 \| 9.0 \| 4059 \| 0.5755 \| 0.8681 \| \| 0.0267 \| 10.0 \| 4510 \| 0.6593 \| 0.8698 \| \| 0.0932 \| 11.0 \| 4961 \| 0.4949 \| 0.8715 \| \| 0.0591 \| 12.0 \| 5412 \| 0.6634 \| 0.8748 \| \| 0.0288 \| 13.0 \| 5863 \| 0.5789 \| 0.9015 \| \| 0.0018 \| 14.0 \| 6314 \| 0.6155 \| 0.8881 \| \| 0.0273 \| 15.0 \| 6765 \| 0.7024 \| 0.8698 \| \| 0.0311 \| 16.0 \| 7216 \| 0.7733 \| 0.8581 \| \| 0.0203 \| 17.0 \| 7667 \| 0.7893 \| 0.8765 \| \| 0.0002 \| 18.0 \| 8118 \| 0.9239 \| 0.8798 \| \| 0.0465 \| 19.0 \| 8569 \| 0.6952 \| 0.8881 \| \| 0.0769 \| 20.0 \| 9020 \| 0.7171 \| 0.8865 \| \| 0.0014 \| 21.0 \| 9471 \| 0.8100 \| 0.8715 \| \| 0.0013 \| 22.0 \| 9922 \| 0.7003 \| 0.8765 \| \| 0.0277 \| 23.0 \| 10373 \| 0.8631 \| 0.8781 \| \| 0.0002 \| 24.0 \| 10824 \| 0.9872 \| 0.8765 \| \| 0.0001 \| 25.0 \| 11275 \| 0.7627 \| 0.8948 \| \| 0.0331 \| 26.0 \| 11726 \| 0.8254 \| 0.8915 \| \| 0.0061 \| 27.0 \| 12177 \| 0.8133 \| 0.8932 \| \| 0.0187 \| 28.0 \| 12628 \| 0.9134 \| 0.8765 \| \| 0.0002 \| 29.0 \| 13079 \| 0.9734 \| 0.8831 \| \| 0.0172 \| 30.0 \| 13530 \| 0.7547 \| 0.8831 \| \| 0.0 \| 31.0 \| 13981 \| 0.8396 \| 0.8865 \| \| 0.0001 \| 32.0 \| 14432 \| 1.0245 \| 0.8815 \| \| 0.0 \| 33.0 \| 14883 \| 0.7812 \| 0.9015 \| \| 0.0055 \| 34.0 \| 15334 \| 0.9777 \| 0.8965 \| \| 0.0062 \| 35.0 \| 15785 \| 1.1854 \| 0.8831 \| \| 0.0 \| 36.0 \| 16236 \| 0.9153 \| 0.8881 \| \| 0.0 \| 37.0 \| 16687 \| 1.0422 \| 0.8898 \| \| 0.0 \| 38.0 \| 17138 \| 1.1991 \| 0.8798 \| \| 0.0073 \| 39.0 \| 17589 \| 0.9200 \| 0.8982 \| \| 0.0 \| 40.0 \| 18040 \| 0.9841 \| 0.9015 \| \| 0.0 \| 41.0 \| 18491 \| 1.0684 \| 0.8982 \| \| 0.0104 \| 42.0 \| 18942 \| 1.0722 \| 0.9032 \| \| 0.0 \| 43.0 \| 19393 \| 1.0249 \| 0.8765 \| \| 0.0 \| 44.0 \| 19844 \| 1.0517 \| 0.8848 \| \| 0.0 \| 45.0 \| 20295 \| 1.1233 \| 0.8831 \| \| 0.0 \| 46.0 \| 20746 \| 1.1565 \| 0.8848 \| \| 0.0 \| 47.0 \| 21197 \| 1.1511 \| 0.8815 \| \| 0.0 \| 48.0 \| 21648 \| 1.1693 \| 0.8798 \| \| 0.0 \| 49.0 \| 22099 \| 1.1569 \| 0.8865 \| \| 0.0 \| 50.0 \| 22550 \| 1.1638 \| 0.8848 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr0001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8848080133555927, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_3x_beit_large_RMSProp_lr0001_fold1	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T06:13:25+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_3x\_beit\_large\_RMSProp\_lr0001\_fold1 ============================================== This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 1.1638 * Accuracy: 0.8848 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 115, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.12968555092811584, 0.17251011729240417, -0.0023243443574756384, 0.1362919956445694, 0.1120586097240448, 0.015268749557435513, 0.14003369212150574, 0.16890837252140045, -0.08239254355430603, 0.046998485922813416, 0.14023225009441376, 0.13628867268562317, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SMIDS_3x_beit_large_RMSProp_lr001_fold1 This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.9451 - Accuracy: 0.7846 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.9818 \| 1.0 \| 451 \| 1.0065 \| 0.5042 \| \| 0.8866 \| 2.0 \| 902 \| 0.8036 \| 0.5543 \| \| 0.8057 \| 3.0 \| 1353 \| 0.7826 \| 0.6210 \| \| 0.782 \| 4.0 \| 1804 \| 0.8406 \| 0.5893 \| \| 0.7177 \| 5.0 \| 2255 \| 0.7766 \| 0.6294 \| \| 0.714 \| 6.0 \| 2706 \| 0.8601 \| 0.6110 \| \| 0.7301 \| 7.0 \| 3157 \| 0.7598 \| 0.6244 \| \| 0.7096 \| 8.0 \| 3608 \| 0.7396 \| 0.6411 \| \| 0.674 \| 9.0 \| 4059 \| 0.7122 \| 0.6511 \| \| 0.6528 \| 10.0 \| 4510 \| 0.7225 \| 0.6578 \| \| 0.7275 \| 11.0 \| 4961 \| 0.7112 \| 0.6644 \| \| 0.7084 \| 12.0 \| 5412 \| 0.7121 \| 0.6678 \| \| 0.6609 \| 13.0 \| 5863 \| 0.7224 \| 0.6611 \| \| 0.6102 \| 14.0 \| 6314 \| 0.6504 \| 0.7162 \| \| 0.6754 \| 15.0 \| 6765 \| 0.6465 \| 0.7346 \| \| 0.6619 \| 16.0 \| 7216 \| 0.7280 \| 0.6828 \| \| 0.5585 \| 17.0 \| 7667 \| 0.6490 \| 0.7362 \| \| 0.5824 \| 18.0 \| 8118 \| 0.6272 \| 0.7329 \| \| 0.5641 \| 19.0 \| 8569 \| 0.7113 \| 0.6795 \| \| 0.5807 \| 20.0 \| 9020 \| 0.6510 \| 0.7312 \| \| 0.4625 \| 21.0 \| 9471 \| 0.6480 \| 0.7412 \| \| 0.4992 \| 22.0 \| 9922 \| 0.6230 \| 0.7412 \| \| 0.4944 \| 23.0 \| 10373 \| 0.6202 \| 0.7730 \| \| 0.5334 \| 24.0 \| 10824 \| 0.6215 \| 0.7629 \| \| 0.495 \| 25.0 \| 11275 \| 0.6512 \| 0.7429 \| \| 0.4575 \| 26.0 \| 11726 \| 0.6122 \| 0.7629 \| \| 0.4237 \| 27.0 \| 12177 \| 0.5877 \| 0.7713 \| \| 0.501 \| 28.0 \| 12628 \| 0.7039 \| 0.7112 \| \| 0.3935 \| 29.0 \| 13079 \| 0.5859 \| 0.7830 \| \| 0.4914 \| 30.0 \| 13530 \| 0.5515 \| 0.7880 \| \| 0.4409 \| 31.0 \| 13981 \| 0.5910 \| 0.7746 \| \| 0.4164 \| 32.0 \| 14432 \| 0.5925 \| 0.7913 \| \| 0.3075 \| 33.0 \| 14883 \| 0.6194 \| 0.7863 \| \| 0.4528 \| 34.0 \| 15334 \| 0.5905 \| 0.7796 \| \| 0.41 \| 35.0 \| 15785 \| 0.5836 \| 0.8047 \| \| 0.3679 \| 36.0 \| 16236 \| 0.5875 \| 0.7963 \| \| 0.3906 \| 37.0 \| 16687 \| 0.6031 \| 0.7963 \| \| 0.3932 \| 38.0 \| 17138 \| 0.6262 \| 0.7830 \| \| 0.3054 \| 39.0 \| 17589 \| 0.6577 \| 0.7846 \| \| 0.2711 \| 40.0 \| 18040 \| 0.6789 \| 0.7980 \| \| 0.3077 \| 41.0 \| 18491 \| 0.6804 \| 0.7830 \| \| 0.2701 \| 42.0 \| 18942 \| 0.7232 \| 0.7980 \| \| 0.3307 \| 43.0 \| 19393 \| 0.7018 \| 0.7796 \| \| 0.2751 \| 44.0 \| 19844 \| 0.7219 \| 0.7846 \| \| 0.2205 \| 45.0 \| 20295 \| 0.7445 \| 0.7880 \| \| 0.2015 \| 46.0 \| 20746 \| 0.8209 \| 0.7880 \| \| 0.2845 \| 47.0 \| 21197 \| 0.8487 \| 0.7796 \| \| 0.3266 \| 48.0 \| 21648 \| 0.9033 \| 0.7746 \| \| 0.2425 \| 49.0 \| 22099 \| 0.9204 \| 0.7863 \| \| 0.1226 \| 50.0 \| 22550 \| 0.9451 \| 0.7846 \| ### Framework versions - Transformers 4.32.1 - Pytorch 2.0.1 - Datasets 2.12.0 - Tokenizers 0.13.2	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-large-patch16-224", "model-index": [{"name": "SMIDS_3x_beit_large_RMSProp_lr001_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7846410684474123, "name": "Accuracy"}]}]}]}	image-classification	onizukal/SMIDS_3x_beit_large_RMSProp_lr001_fold1	[ "transformers", "pytorch", "beit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:microsoft/beit-large-patch16-224", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T06:14:36+00:00	[]	[]	TAGS #transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	SMIDS\_3x\_beit\_large\_RMSProp\_lr001\_fold1 ============================================= This model is a fine-tuned version of microsoft/beit-large-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.9451 * Accuracy: 0.7846 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.001 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 50 ### Training results ### Framework versions * Transformers 4.32.1 * Pytorch 2.0.1 * Datasets 2.12.0 * Tokenizers 0.13.2	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ "TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50", "### Training results", "### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ 81, 115, 4, 30 ]	[ "passage: TAGS\n#transformers #pytorch #beit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/beit-large-patch16-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 50### Training results### Framework versions\n\n\n* Transformers 4.32.1\n* Pytorch 2.0.1\n* Datasets 2.12.0\n* Tokenizers 0.13.2" ]	[ -0.1293599158525467, 0.1724882274866104, -0.0023454553447663784, 0.13587836921215057, 0.11250235140323639, 0.015344180166721344, 0.13944171369075775, 0.16905122995376587, -0.08231265097856522, 0.04725457355380058, 0.1399839073419571, 0.13659004867076874, 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null	null	transformers	# Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets. For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/). ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. ``` text = "<s>[INST] What is your favourite condiment? [/INST]" "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> " "[INST] Do you have mayonnaise recipes? [/INST]" ``` This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" # the device to load the model onto model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt") model_inputs = encodeds.to(device) model.to(device) generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True) decoded = tokenizer.batch_decode(generated_ids) print(decoded[0]) ``` ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: ``` Traceback (most recent call last): File "", line 1, in File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained config, kwargs = AutoConfig.from_pretrained( File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained config_class = CONFIG_MAPPING[config_dict["model_type"]] File "/transformers/models/auto/configuration_auto.py", line 723, in getitem raise KeyError(key) KeyError: 'mistral' ``` Installing transformers from source should solve the issue pip install git+https://github.com/huggingface/transformers This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.	{"license": "apache-2.0", "tags": ["finetuned"], "pipeline_tag": "text-generation", "inference": false}	text-generation	gadkins/Mistral-7B-Instruct-v0.1-function-calling	[ "transformers", "safetensors", "mistral", "text-generation", "finetuned", "conversational", "arxiv:2310.06825", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:21:55+00:00	[ "2310.06825" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us	# Model Card for Mistral-7B-Instruct-v0.1 The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets. For full details of this model please read our paper and release blog post. ## Instruction format In order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id. E.g. This format is available as a chat template via the 'apply_chat_template()' method: ## Model Architecture This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices: - Grouped-Query Attention - Sliding-Window Attention - Byte-fallback BPE tokenizer ## Troubleshooting - If you see the following error: Installing transformers from source should solve the issue pip install git+URL This should not be required after transformers-v4.33.4. ## Limitations The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs. ## The Mistral AI Team Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed.	[ "# Model Card for Mistral-7B-Instruct-v0.1\n\nThe Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.\n\nFor full details of this model please read our paper and release blog post.", "## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:", "## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer", "## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.", "## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.", "## The Mistral AI Team\n\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed." ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n", "# Model Card for Mistral-7B-Instruct-v0.1\n\nThe Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.\n\nFor full details of this model please read our paper and release blog post.", "## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:", "## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer", "## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.", "## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.", "## The Mistral AI Team\n\nAlbert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed." ]	[ 64, 81, 105, 56, 42, 85, 100 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #finetuned #conversational #arxiv-2310.06825 #license-apache-2.0 #autotrain_compatible #text-generation-inference #region-us \n# Model Card for Mistral-7B-Instruct-v0.1\n\nThe Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the Mistral-7B-v0.1 generative text model using a variety of publicly available conversation datasets.\n\nFor full details of this model please read our paper and release blog post.## Instruction format\n\nIn order to leverage instruction fine-tuning, your prompt should be surrounded by '[INST]' and '[/INST]' tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.\n\nE.g.\n\n\nThis format is available as a chat template via the 'apply_chat_template()' method:## Model Architecture\nThis instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:\n- Grouped-Query Attention\n- Sliding-Window Attention\n- Byte-fallback BPE tokenizer## Troubleshooting\n- If you see the following error:\n\n\nInstalling transformers from source should solve the issue\npip install git+URL\n\nThis should not be required after transformers-v4.33.4.## Limitations\n\nThe Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance. \nIt does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to\nmake the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs." ]	[ -0.08154156804084778, 0.09633228182792664, -0.006207073573023081, 0.008780199103057384, 0.1072206050157547, -0.007847028784453869, 0.10169675201177597, 0.06143496558070183, 0.031456686556339264, 0.05882946029305458, -0.00011387487757019699, 0.07851465046405792, 0.06145763024687767, 0.12915445864200592, 0.008660025894641876, -0.19192884862422943, 0.0663599744439125, -0.05721339210867882, 0.08362677693367004, 0.06200484558939934, 0.1273060292005539, -0.04884813725948334, 0.03294702246785164, 0.0712774395942688, -0.08009567111730576, -0.0010871957056224346, -0.0017357796896249056, -0.020775537937879562, 0.08311796933412552, 0.061596859246492386, 0.04196586459875107, 0.021883295848965645, 0.006327025592327118, -0.1697111576795578, 0.017486199736595154, 0.08615092933177948, -0.02327769808471203, 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null	null	diffusers	# SDXL LoRA DreamBooth - Samoi/mimi <Gallery /> ## Model description These are Samoi/mimi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of mimimi cat to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](Samoi/mimi/tree/main) them in the Files & versions tab.	{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A photo of mimimi cat in a bucket", "output": {"url": "image_0.png"}}, {"text": "A photo of mimimi cat in a bucket", "output": {"url": "image_1.png"}}, {"text": "A photo of mimimi cat in a bucket", "output": {"url": "image_2.png"}}, {"text": "A photo of mimimi cat in a bucket", "output": {"url": "image_3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of mimimi cat"}	text-to-image	Samoi/mimi	[ "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]	2024-02-06T06:24:59+00:00	[]	[]	TAGS #diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us	# SDXL LoRA DreamBooth - Samoi/mimi <Gallery /> ## Model description These are Samoi/mimi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use a photo of mimimi cat to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.	[ "# SDXL LoRA DreamBooth - Samoi/mimi\n\n<Gallery />", "## Model description\n\nThese are Samoi/mimi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of mimimi cat to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ "TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - Samoi/mimi\n\n<Gallery />", "## Model description\n\nThese are Samoi/mimi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use a photo of mimimi cat to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ 82, 19, 84, 19, 28 ]	[ "passage: TAGS\n#diffusers #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - Samoi/mimi\n\n<Gallery />## Model description\n\nThese are Samoi/mimi LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use a photo of mimimi cat to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ -0.07323776930570602, 0.1373778134584427, 0.0008950817282311618, 0.03581885248422623, 0.16353446245193481, 0.03289283439517021, 0.15238776803016663, 0.07972008734941483, 0.04729083925485611, 0.05916251987218857, 0.009752539917826653, 0.022786438465118408, 0.05051017925143242, 0.20628586411476135, -0.0341084785759449, -0.17340077459812164, 0.030142785981297493, -0.012765240855515003, -0.026404032483696938, 0.004539403133094311, 0.030575241893529892, -0.07424163073301315, 0.07119353115558624, -0.051844868808984756, -0.07180151343345642, 0.04338885098695755, -0.004896384198218584, -0.022190440446138382, 0.010161012411117554, 0.046881236135959625, 0.0875430479645729, 0.04974127933382988, 0.08372192084789276, -0.15682704746723175, 0.03501250967383385, 0.08155472576618195, -0.024743424728512764, 0.022489165887236595, -0.034062035381793976, -0.07367642223834991, 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null	null	transformers	# miqu-1-120b ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6303ca537373aacccd85d8a7/LxO9j7OykuabKLYQHIodG.jpeg) * EXL2: [2.4bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2) \| [2.65bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.65bpw-h6-exl2) \| 3.0bpw \| [4.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-4.0bpw-h6-exl2) \| [5.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-5.0bpw-h6-exl2) * GGUF: [Q2_K-Q5_K_M](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-GGUF/) \| [IQ3_XXS](https://huggingface.co/wolfram/miqu-1-120b-GGUF) * HF FP16: [wolfram/miqu-1-120b](https://huggingface.co/wolfram/miqu-1-120b) This is a 120b frankenmerge of [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) created by interleaving layers of [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) with itself using [mergekit](https://github.com/cg123/mergekit). Inspired by [Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2), [MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b), and [goliath-120b](https://huggingface.co/alpindale/goliath-120b). Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, [Lone Striker](https://huggingface.co/LoneStriker)! ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` See also: [🐺🐦‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/18ljvxb/llm_prompt_format_comparisontest_mixtral_8x7b/) ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 20] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [10, 30] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [20, 40] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [30, 50] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [40, 60] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [50, 70] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [60, 80] model: 152334H/miqu-1-70b-sf ``` ## Credits & Special Thanks * original (unreleased) model: [mistralai (Mistral AI_)](https://huggingface.co/mistralai) * leaked model: [miqudev/miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) * f16 model: [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) * mergekit: [arcee-ai/mergekit: Tools for merging pretrained large language models.](https://github.com/arcee-ai/mergekit) * mergekit_config.yml: [nsfwthrowitaway69/Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2) ### Support * [My Ko-fi page](https://ko-fi.com/wolframravenwolf) if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS [BASED ON A LEAKED ASSET](https://huggingface.co/miqudev/miqu-1-70b/discussions/10) AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	{"language": ["en", "de", "fr", "es", "it"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["152334H/miqu-1-70b-sf"]}	text-generation	LoneStriker/wolfram_miqu-1-120b-3.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "de", "fr", "es", "it", "base_model:152334H/miqu-1-70b-sf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:27:12+00:00	[]	[ "en", "de", "fr", "es", "it" ]	TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# miqu-1-120b !image/jpeg * EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw * GGUF: Q2_K-Q5_K_M \| IQ3_XXS * HF FP16: wolfram/miqu-1-120b This is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit. Inspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b. Thanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, Lone Striker! ## Prompt template: Mistral See also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * 152334H/miqu-1-70b-sf ### Configuration The following YAML configuration was used to produce this model: ## Credits & Special Thanks * original (unreleased) model: mistralai (Mistral AI_) * leaked model: miqudev/miqu-1-70b * f16 model: 152334H/miqu-1-70b-sf * mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models. * mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2 ### Support * My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	[ "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ 85, 206, 44, 31, 4, 17, 28, 17, 107, 69, 43 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ -0.08452296257019043, 0.07996358722448349, -0.006216804962605238, 0.07489742338657379, 0.062447689473629, 0.048032015562057495, 0.10850238054990768, 0.11730548739433289, 0.04997159540653229, 0.1304655373096466, 0.019059622660279274, 0.0882313996553421, 0.10314993560314178, 0.1546645164489746, -0.0008784975507296622, -0.13700617849826813, 0.03406155854463577, 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null	null	transformers	# MarkrAI/RAG-KO-Mixtral-7Bx2-v2.0 # Model Details ## Model Developers MarkrAI - AI Researchers ## Base Model [DopeorNope/Ko-Mixtral-v1.4-MoE-7Bx2](https://huggingface.co/DopeorNope/Ko-Mixtral-v1.4-MoE-7Bx2). ## Instruction tuning Method Using QLoRA. ``` 4-bit quantization Lora_r: 64 Lora_alpha: 64 Lora_dropout: 0.05 Lora_target_modules: [embed_tokens, q_proj, k_proj, v_proj, o_proj, gate, w1, w2, w3, lm_head] ``` ## Hyperparameters ``` Epoch: 5 Batch size: 64 Learning_rate: 1e-5 Learning scheduler: linear Warmup_ratio: 0.06 ``` ## Datasets Private datasets: [HumanF-MarkrAI/Korean-RAG-ver2](https://huggingface.co/datasets/HumanF-MarkrAI/Korean-RAG-ver2) ``` Aihub datasets 활용하여서 제작함. ``` ## Implmentation Code ``` from transformers import AutoModelForCausalLM, AutoTokenizer import torch repo = "MarkrAI/RAG-KO-Mixtral-7Bx2-v2.0" markrAI_RAG = AutoModelForCausalLM.from_pretrained( repo, return_dict=True, torch_dtype=torch.float16, device_map='auto' ) markrAI_RAG_tokenizer = AutoTokenizer.from_pretrained(repo) ``` # Model Benchmark - Coming soon...	{"language": ["ko"], "license": "cc-by-nc-sa-4.0", "tags": ["Retrieval Augmented Generation", "RAG", "Multi-domain"], "datasets": ["HumanF-MarkrAI/Korean-RAG-ver2"]}	text-generation	MarkrAI/RAG-KO-Mixtral-7Bx2-v2.0	[ "transformers", "safetensors", "mixtral", "text-generation", "Retrieval Augmented Generation", "RAG", "Multi-domain", "ko", "dataset:HumanF-MarkrAI/Korean-RAG-ver2", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:28:22+00:00	[]	[ "ko" ]	TAGS #transformers #safetensors #mixtral #text-generation #Retrieval Augmented Generation #RAG #Multi-domain #ko #dataset-HumanF-MarkrAI/Korean-RAG-ver2 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# MarkrAI/RAG-KO-Mixtral-7Bx2-v2.0 # Model Details ## Model Developers MarkrAI - AI Researchers ## Base Model DopeorNope/Ko-Mixtral-v1.4-MoE-7Bx2. ## Instruction tuning Method Using QLoRA. ## Hyperparameters ## Datasets Private datasets: HumanF-MarkrAI/Korean-RAG-ver2 ## Implmentation Code # Model Benchmark - Coming soon...	[ "# MarkrAI/RAG-KO-Mixtral-7Bx2-v2.0", "# Model Details", "## Model Developers \nMarkrAI - AI Researchers", "## Base Model \nDopeorNope/Ko-Mixtral-v1.4-MoE-7Bx2.", "## Instruction tuning Method \nUsing QLoRA.", "## Hyperparameters", "## Datasets\nPrivate datasets: HumanF-MarkrAI/Korean-RAG-ver2", "## Implmentation Code", "# Model Benchmark\n- Coming soon..." ]	[ "TAGS\n#transformers #safetensors #mixtral #text-generation #Retrieval Augmented Generation #RAG #Multi-domain #ko #dataset-HumanF-MarkrAI/Korean-RAG-ver2 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MarkrAI/RAG-KO-Mixtral-7Bx2-v2.0", "# Model Details", "## Model Developers \nMarkrAI - AI Researchers", "## Base Model \nDopeorNope/Ko-Mixtral-v1.4-MoE-7Bx2.", "## Instruction tuning Method \nUsing QLoRA.", "## Hyperparameters", "## Datasets\nPrivate datasets: HumanF-MarkrAI/Korean-RAG-ver2", "## Implmentation Code", "# Model Benchmark\n- Coming soon..." ]	[ 98, 19, 3, 11, 24, 12, 5, 24, 6, 10 ]	[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #Retrieval Augmented Generation #RAG #Multi-domain #ko #dataset-HumanF-MarkrAI/Korean-RAG-ver2 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MarkrAI/RAG-KO-Mixtral-7Bx2-v2.0# Model Details## Model Developers \nMarkrAI - AI Researchers## Base Model \nDopeorNope/Ko-Mixtral-v1.4-MoE-7Bx2.## Instruction tuning Method \nUsing QLoRA.## Hyperparameters## Datasets\nPrivate datasets: HumanF-MarkrAI/Korean-RAG-ver2## Implmentation Code# Model Benchmark\n- Coming soon..." ]	[ -0.03690986707806587, 0.13318860530853271, -0.00145150744356215, -0.03523612394928932, 0.1257423609495163, -0.029501287266612053, 0.08868687599897385, 0.08752609044313431, -0.028227047994732857, 0.07319030910730362, 0.1198701411485672, 0.058575715869665146, 0.054031625390052795, 0.19805492460727692, -0.02174859680235386, -0.33433428406715393, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	question-answering	Poulami/muril-base-cased-finetuned-QA-SQuADv2	[ "transformers", "safetensors", "bert", "question-answering", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-06T06:32:37+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 39, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #question-answering #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05259126052260399, 0.2028854638338089, -0.0044862194918096066, 0.02425798401236534, 0.10576079040765762, 0.0068638501688838005, 0.06693174690008163, 0.1155666932463646, 0.002374236471951008, 0.12093950808048248, 0.028730813413858414, 0.0795159563422203, 0.11937985569238663, 0.150146484375, 0.0030903066508471966, -0.24085353314876556, 0.05294279009103775, -0.09337051957845688, 0.0006256133783608675, 0.1166968047618866, 0.13365985453128815, -0.108995720744133, 0.08927806466817856, -0.010429061949253082, -0.015317060984671116, -0.016092488542199135, -0.07380294054746628, -0.06513319909572601, 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null	null	stable-baselines3	# A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaReachDense-v3", "type": "PandaReachDense-v3"}, "metrics": [{"type": "mean_reward", "value": "-0.14 +/- 0.09", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	r0in/a2c-PandaReachDense-v3	[ "stable-baselines3", "PandaReachDense-v3", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-06T06:35:50+00:00	[]	[]	TAGS #stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# A2C Agent playing PandaReachDense-v3 This is a trained model of a A2C agent playing PandaReachDense-v3 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 41, 45, 17 ]	[ "passage: TAGS\n#stable-baselines3 #PandaReachDense-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# A2C Agent playing PandaReachDense-v3\nThis is a trained model of a A2C agent playing PandaReachDense-v3\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.028780510649085045, 0.06549051403999329, -0.004174588713794947, 0.028733979910612106, 0.12748076021671295, -0.010029550641775131, 0.16130082309246063, 0.07903143763542175, 0.052706290036439896, -0.055043965578079224, 0.09157051891088486, -0.079488605260849, 0.04699381813406944, 0.3393711447715759, 0.029525093734264374, -0.186785027384758, 0.08573613315820694, 0.015584449283778667, 0.018966808915138245, 0.09867662936449051, 0.03466832637786865, -0.08736564218997955, 0.04568251967430115, 0.03800429776310921, -0.07686931639909744, -0.04319252818822861, -0.03975098207592964, -0.06744661927223206, 0.10361767560243607, -0.044310007244348526, 0.1670169234275818, -0.03489987552165985, 0.10219604521989822, -0.12577489018440247, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # hubert-rinna-jdrt-RTSPsplit-0206-2 This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0758 - Wer: 0.2423 - Cer: 0.0746 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 32 - eval_batch_size: 32 - seed: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 35 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 14.3788 \| 1.0 \| 60 \| 13.6628 \| 0.9855 \| 0.9894 \| \| 7.2762 \| 2.0 \| 120 \| 6.5080 \| 0.9855 \| 0.9894 \| \| 5.3923 \| 3.0 \| 180 \| 5.1318 \| 0.9855 \| 0.9894 \| \| 4.1787 \| 4.0 \| 240 \| 3.9522 \| 0.9855 \| 0.9894 \| \| 3.3514 \| 5.0 \| 300 \| 3.1901 \| 0.9855 \| 0.9894 \| \| 2.8121 \| 6.0 \| 360 \| 2.6919 \| 0.9855 \| 0.9894 \| \| 2.0411 \| 7.0 \| 420 \| 1.9743 \| 1.0 \| 0.8450 \| \| 1.4368 \| 8.0 \| 480 \| 1.2798 \| 1.0 \| 0.5637 \| \| 1.1834 \| 9.0 \| 540 \| 1.0842 \| 1.0 \| 0.5095 \| \| 1.0832 \| 10.0 \| 600 \| 1.0301 \| 0.9903 \| 0.5145 \| \| 0.9659 \| 11.0 \| 660 \| 0.8236 \| 0.8099 \| 0.4526 \| \| 0.7583 \| 12.0 \| 720 \| 0.7280 \| 0.8166 \| 0.4551 \| \| 0.7525 \| 13.0 \| 780 \| 0.7380 \| 0.8118 \| 0.4639 \| \| 0.7021 \| 14.0 \| 840 \| 0.6548 \| 0.8114 \| 0.4818 \| \| 0.6591 \| 15.0 \| 900 \| 0.6535 \| 0.8043 \| 0.4555 \| \| 0.6141 \| 16.0 \| 960 \| 0.5780 \| 0.7767 \| 0.3729 \| \| 0.5776 \| 17.0 \| 1020 \| 0.5662 \| 0.7726 \| 0.3717 \| \| 1.1193 \| 18.0 \| 1080 \| 0.4865 \| 0.7044 \| 0.3245 \| \| 0.5106 \| 19.0 \| 1140 \| 0.5317 \| 0.6884 \| 0.2815 \| \| 0.4611 \| 20.0 \| 1200 \| 0.4132 \| 0.6135 \| 0.2433 \| \| 0.4474 \| 21.0 \| 1260 \| 0.3655 \| 0.5785 \| 0.1975 \| \| 0.5201 \| 22.0 \| 1320 \| 0.3572 \| 0.5594 \| 0.1805 \| \| 0.3513 \| 23.0 \| 1380 \| 0.2872 \| 0.4983 \| 0.1652 \| \| 0.3245 \| 24.0 \| 1440 \| 0.2417 \| 0.4383 \| 0.1531 \| \| 0.2967 \| 25.0 \| 1500 \| 0.2275 \| 0.4089 \| 0.1305 \| \| 0.2441 \| 26.0 \| 1560 \| 0.1895 \| 0.3835 \| 0.1419 \| \| 0.2236 \| 27.0 \| 1620 \| 0.1519 \| 0.3254 \| 0.1029 \| \| 0.2215 \| 28.0 \| 1680 \| 0.1423 \| 0.3254 \| 0.1046 \| \| 0.1583 \| 29.0 \| 1740 \| 0.1195 \| 0.2985 \| 0.0955 \| \| 0.1627 \| 30.0 \| 1800 \| 0.1101 \| 0.2836 \| 0.0888 \| \| 0.1528 \| 31.0 \| 1860 \| 0.0973 \| 0.2680 \| 0.0830 \| \| 0.1585 \| 32.0 \| 1920 \| 0.0909 \| 0.2635 \| 0.0828 \| \| 0.1443 \| 33.0 \| 1980 \| 0.0825 \| 0.2520 \| 0.0784 \| \| 0.1322 \| 34.0 \| 2040 \| 0.0775 \| 0.2467 \| 0.0765 \| \| 0.1824 \| 35.0 \| 2100 \| 0.0758 \| 0.2423 \| 0.0746 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "rinna/japanese-hubert-base", "model-index": [{"name": "hubert-rinna-jdrt-RTSPsplit-0206-2", "results": []}]}	automatic-speech-recognition	tndklab/hubert-rinna-jdrt-RTSPsplit-0206-2	[ "transformers", "safetensors", "hubert", "automatic-speech-recognition", "generated_from_trainer", "base_model:rinna/japanese-hubert-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-06T06:37:34+00:00	[]	[]	TAGS #transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us	hubert-rinna-jdrt-RTSPsplit-0206-2 ================================== This model is a fine-tuned version of rinna/japanese-hubert-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0758 * Wer: 0.2423 * Cer: 0.0746 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0002 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 4 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 35 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 35", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 35", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ 66, 115, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #hubert #automatic-speech-recognition #generated_from_trainer #base_model-rinna/japanese-hubert-base #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 35### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ -0.09041319042444229, 0.10401695966720581, -0.0023518067318946123, 0.08839446306228638, 0.12425249069929123, -0.0035006816033273935, 0.13787509500980377, 0.14191928505897522, -0.06928262114524841, 0.0741477906703949, 0.09589006006717682, 0.10972340404987335, 0.02624472975730896, 0.14655694365501404, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mi_modelo_small This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9190 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 156 \| 2.0811 \| \| No log \| 2.0 \| 312 \| 1.3007 \| \| No log \| 3.0 \| 468 \| 0.9190 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "mi_modelo_small", "results": []}]}	question-answering	asier86/mi_modelo_small	[ "transformers", "tensorboard", "safetensors", "distilbert", "question-answering", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-06T06:37:38+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us	mi\_modelo\_small ================= This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.9190 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 65, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #question-answering #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.10708221793174744, 0.08787639439105988, -0.0020266782958060503, 0.10252698510885239, 0.1274273842573166, 0.02112007699906826, 0.141371950507164, 0.11134063452482224, -0.08131598681211472, 0.05829233676195145, 0.13410121202468872, 0.11583946645259857, -0.0018265031976625323, 0.09120819717645645, -0.0611470602452755, -0.2005513310432434, 0.004526883829385042, 0.03799702972173691, -0.09097661823034286, 0.11530310660600662, 0.08769755810499191, -0.13717275857925415, 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null	null	null	ERROR: type should be string, got "\nhttps://civitai.com/models/20282/henmixreal\n"	{"license": "mit"}	null	prazzz/henmmix5c	[ "license:mit", "region:us" ]	2024-02-06T06:37:50+00:00	[]	[]	TAGS #license-mit #region-us	URL	[]	[ "TAGS\n#license-mit #region-us \n" ]	[ 11 ]	[ "passage: TAGS\n#license-mit #region-us \n" ]	[ 0.026221778243780136, -0.033018264919519424, -0.008281232789158821, -0.05295303836464882, 0.052470896393060684, 0.06768012046813965, 0.1598525494337082, 0.04655371606349945, 0.23683255910873413, -0.05407243221998215, 0.11752297729253769, 0.08923697471618652, 0.004284696187824011, -0.0009730930323712528, 0.014216204173862934, -0.17134642601013184, 0.04864625632762909, -0.02878100797533989, 0.08764812350273132, 0.032233644276857376, -0.006205103360116482, -0.03845774009823799, -0.0022142508532851934, -0.03178790956735611, -0.057939812541007996, 0.03869890421628952, 0.045729056000709534, -0.02754949778318405, 0.14189864695072174, -0.021783310920000076, 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null	null	null	# Lora of Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order) ## What Is This? This is the LoRA model of waifu Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order). ## How Is It Trained? * This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). * The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs). * The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest). * Dataset used for training is the `stage3-p480-800` in [CyberHarem/sugitani_zenjubou_fgo](https://huggingface.co/datasets/CyberHarem/sugitani_zenjubou_fgo), which contains 60 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets. * Trained for 800 steps, 40 checkpoints were saved and evaluated. * Trigger word is `sugitani_zenjubou_fgo`. * Pruned core tags for this waifu are `brown_hair, breasts, brown_eyes, large_breasts, yellow_eyes, ahoge, ponytail`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. ## How to Use It? ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 700, you need to download [`700/sugitani_zenjubou_fgo.pt`](https://huggingface.co/CyberHarem/sugitani_zenjubou_fgo/resolve/main/700/sugitani_zenjubou_fgo.pt) as the embedding and [`700/sugitani_zenjubou_fgo.safetensors`](https://huggingface.co/CyberHarem/sugitani_zenjubou_fgo/resolve/main/700/sugitani_zenjubou_fgo.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters. ## Which Step Should I Use? We selected 5 good steps for you to choose. The best one is step 700. 1480 images (1.52 GiB) were generated for auto-testing. ![Metrics Plot](metrics_plot.png) The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). Here are the preview of the recommended steps: \| Step \| Epoch \| CCIP \| AI Corrupt \| Bikini Plus \| Score \| Download \| pattern_0 \| pattern_1 \| portrait_0 \| portrait_1 \| portrait_2 \| full_body_0 \| full_body_1 \| profile_0 \| profile_1 \| free_0 \| free_1 \| shorts \| maid_0 \| maid_1 \| miko \| yukata \| suit \| china \| bikini_0 \| bikini_1 \| bikini_2 \| sit \| squat \| kneel \| jump \| crossed_arms \| angry \| smile \| cry \| grin \| n_lie_0 \| n_lie_1 \| n_stand_0 \| n_stand_1 \| n_stand_2 \| n_sex_0 \| n_sex_1 \| \|-------:\|--------:\|:----------\|:-------------\|:--------------\|:----------\|:---------------------------------------------------------------------------------------------------------------\|:-----------------------------------------\|:-----------------------------------------\|:-------------------------------------------\|:-------------------------------------------\|:-------------------------------------------\|:---------------------------------------------\|:---------------------------------------------\|:-----------------------------------------\|:-----------------------------------------\|:-----------------------------------\|:-----------------------------------\|:-----------------------------------\|:-----------------------------------\|:-----------------------------------\|:-------------------------------\|:-----------------------------------\|:-------------------------------\|:---------------------------------\|:---------------------------------------\|:---------------------------------------\|:---------------------------------------\|:-----------------------------\|:---------------------------------\|:---------------------------------\|:-------------------------------\|:-----------------------------------------------\|:---------------------------------\|:---------------------------------\|:-----------------------------\|:-------------------------------\|:-------------------------------------\|:-------------------------------------\|:-----------------------------------------\|:-----------------------------------------\|:-----------------------------------------\|:-------------------------------------\|:-------------------------------------\| \| 700 \| 47 \| 0.729 \| 0.975 \| 0.854 \| 0.857 \| [Download](https://huggingface.co/CyberHarem/sugitani_zenjubou_fgo/resolve/main/700/sugitani_zenjubou_fgo.zip) \| ![pattern_0](700/previews/pattern_0.png) \| ![pattern_1](700/previews/pattern_1.png) \| ![portrait_0](700/previews/portrait_0.png) \| ![portrait_1](700/previews/portrait_1.png) \| ![portrait_2](700/previews/portrait_2.png) \| ![full_body_0](700/previews/full_body_0.png) \| ![full_body_1](700/previews/full_body_1.png) \| ![profile_0](700/previews/profile_0.png) \| ![profile_1](700/previews/profile_1.png) \| ![free_0](700/previews/free_0.png) \| ![free_1](700/previews/free_1.png) \| ![shorts](700/previews/shorts.png) \| ![maid_0](700/previews/maid_0.png) \| ![maid_1](700/previews/maid_1.png) \| ![miko](700/previews/miko.png) \| ![yukata](700/previews/yukata.png) \| ![suit](700/previews/suit.png) \| ![china](700/previews/china.png) \| ![bikini_0](700/previews/bikini_0.png) \| ![bikini_1](700/previews/bikini_1.png) \| ![bikini_2](700/previews/bikini_2.png) \| ![sit](700/previews/sit.png) \| ![squat](700/previews/squat.png) \| ![kneel](700/previews/kneel.png) \| ![jump](700/previews/jump.png) \| ![crossed_arms](700/previews/crossed_arms.png) \| ![angry](700/previews/angry.png) \| ![smile](700/previews/smile.png) \| ![cry](700/previews/cry.png) \| ![grin](700/previews/grin.png) \| ![n_lie_0](700/previews/n_lie_0.png) \| ![n_lie_1](700/previews/n_lie_1.png) \| ![n_stand_0](700/previews/n_stand_0.png) \| ![n_stand_1](700/previews/n_stand_1.png) \| ![n_stand_2](700/previews/n_stand_2.png) \| ![n_sex_0](700/previews/n_sex_0.png) \| ![n_sex_1](700/previews/n_sex_1.png) \| \| 580 \| 39 \| 0.663 \| 0.963 \| 0.851 \| 0.800 \| [Download](https://huggingface.co/CyberHarem/sugitani_zenjubou_fgo/resolve/main/580/sugitani_zenjubou_fgo.zip) \| ![pattern_0](580/previews/pattern_0.png) \| ![pattern_1](580/previews/pattern_1.png) \| ![portrait_0](580/previews/portrait_0.png) \| ![portrait_1](580/previews/portrait_1.png) \| ![portrait_2](580/previews/portrait_2.png) \| ![full_body_0](580/previews/full_body_0.png) \| ![full_body_1](580/previews/full_body_1.png) \| ![profile_0](580/previews/profile_0.png) \| ![profile_1](580/previews/profile_1.png) \| ![free_0](580/previews/free_0.png) \| ![free_1](580/previews/free_1.png) \| ![shorts](580/previews/shorts.png) \| ![maid_0](580/previews/maid_0.png) \| ![maid_1](580/previews/maid_1.png) \| ![miko](580/previews/miko.png) \| ![yukata](580/previews/yukata.png) \| ![suit](580/previews/suit.png) \| ![china](580/previews/china.png) \| ![bikini_0](580/previews/bikini_0.png) \| ![bikini_1](580/previews/bikini_1.png) \| ![bikini_2](580/previews/bikini_2.png) \| ![sit](580/previews/sit.png) \| ![squat](580/previews/squat.png) \| ![kneel](580/previews/kneel.png) \| ![jump](580/previews/jump.png) \| ![crossed_arms](580/previews/crossed_arms.png) \| ![angry](580/previews/angry.png) \| ![smile](580/previews/smile.png) \| ![cry](580/previews/cry.png) \| ![grin](580/previews/grin.png) \| ![n_lie_0](580/previews/n_lie_0.png) \| ![n_lie_1](580/previews/n_lie_1.png) \| ![n_stand_0](580/previews/n_stand_0.png) \| ![n_stand_1](580/previews/n_stand_1.png) \| ![n_stand_2](580/previews/n_stand_2.png) \| ![n_sex_0](580/previews/n_sex_0.png) \| ![n_sex_1](580/previews/n_sex_1.png) \| \| 780 \| 52 \| 0.657 \| 0.971 \| 0.851 \| 0.796 \| [Download](https://huggingface.co/CyberHarem/sugitani_zenjubou_fgo/resolve/main/780/sugitani_zenjubou_fgo.zip) \| ![pattern_0](780/previews/pattern_0.png) \| ![pattern_1](780/previews/pattern_1.png) \| ![portrait_0](780/previews/portrait_0.png) \| ![portrait_1](780/previews/portrait_1.png) \| ![portrait_2](780/previews/portrait_2.png) \| ![full_body_0](780/previews/full_body_0.png) \| ![full_body_1](780/previews/full_body_1.png) \| ![profile_0](780/previews/profile_0.png) \| ![profile_1](780/previews/profile_1.png) \| ![free_0](780/previews/free_0.png) \| ![free_1](780/previews/free_1.png) \| ![shorts](780/previews/shorts.png) \| ![maid_0](780/previews/maid_0.png) \| ![maid_1](780/previews/maid_1.png) \| ![miko](780/previews/miko.png) \| ![yukata](780/previews/yukata.png) \| ![suit](780/previews/suit.png) \| ![china](780/previews/china.png) \| ![bikini_0](780/previews/bikini_0.png) \| ![bikini_1](780/previews/bikini_1.png) \| ![bikini_2](780/previews/bikini_2.png) \| ![sit](780/previews/sit.png) \| ![squat](780/previews/squat.png) \| ![kneel](780/previews/kneel.png) \| ![jump](780/previews/jump.png) \| ![crossed_arms](780/previews/crossed_arms.png) \| ![angry](780/previews/angry.png) \| ![smile](780/previews/smile.png) \| ![cry](780/previews/cry.png) \| ![grin](780/previews/grin.png) \| ![n_lie_0](780/previews/n_lie_0.png) \| ![n_lie_1](780/previews/n_lie_1.png) \| ![n_stand_0](780/previews/n_stand_0.png) \| ![n_stand_1](780/previews/n_stand_1.png) \| ![n_stand_2](780/previews/n_stand_2.png) \| ![n_sex_0](780/previews/n_sex_0.png) \| ![n_sex_1](780/previews/n_sex_1.png) \| \| 540 \| 36 \| 0.608 \| 0.971 \| 0.853 \| 0.764 \| [Download](https://huggingface.co/CyberHarem/sugitani_zenjubou_fgo/resolve/main/540/sugitani_zenjubou_fgo.zip) \| ![pattern_0](540/previews/pattern_0.png) \| ![pattern_1](540/previews/pattern_1.png) \| ![portrait_0](540/previews/portrait_0.png) \| ![portrait_1](540/previews/portrait_1.png) \| ![portrait_2](540/previews/portrait_2.png) \| ![full_body_0](540/previews/full_body_0.png) \| ![full_body_1](540/previews/full_body_1.png) \| ![profile_0](540/previews/profile_0.png) \| ![profile_1](540/previews/profile_1.png) \| ![free_0](540/previews/free_0.png) \| ![free_1](540/previews/free_1.png) \| ![shorts](540/previews/shorts.png) \| ![maid_0](540/previews/maid_0.png) \| ![maid_1](540/previews/maid_1.png) \| ![miko](540/previews/miko.png) \| ![yukata](540/previews/yukata.png) \| ![suit](540/previews/suit.png) \| ![china](540/previews/china.png) \| ![bikini_0](540/previews/bikini_0.png) \| ![bikini_1](540/previews/bikini_1.png) \| ![bikini_2](540/previews/bikini_2.png) \| ![sit](540/previews/sit.png) \| ![squat](540/previews/squat.png) \| ![kneel](540/previews/kneel.png) \| ![jump](540/previews/jump.png) \| ![crossed_arms](540/previews/crossed_arms.png) \| ![angry](540/previews/angry.png) \| ![smile](540/previews/smile.png) \| ![cry](540/previews/cry.png) \| ![grin](540/previews/grin.png) \| ![n_lie_0](540/previews/n_lie_0.png) \| ![n_lie_1](540/previews/n_lie_1.png) \| ![n_stand_0](540/previews/n_stand_0.png) \| ![n_stand_1](540/previews/n_stand_1.png) \| ![n_stand_2](540/previews/n_stand_2.png) \| ![n_sex_0](540/previews/n_sex_0.png) \| ![n_sex_1](540/previews/n_sex_1.png) \| \| 660 \| 44 \| 0.598 \| 0.975 \| 0.852 \| 0.755 \| [Download](https://huggingface.co/CyberHarem/sugitani_zenjubou_fgo/resolve/main/660/sugitani_zenjubou_fgo.zip) \| ![pattern_0](660/previews/pattern_0.png) \| ![pattern_1](660/previews/pattern_1.png) \| ![portrait_0](660/previews/portrait_0.png) \| ![portrait_1](660/previews/portrait_1.png) \| ![portrait_2](660/previews/portrait_2.png) \| ![full_body_0](660/previews/full_body_0.png) \| ![full_body_1](660/previews/full_body_1.png) \| ![profile_0](660/previews/profile_0.png) \| ![profile_1](660/previews/profile_1.png) \| ![free_0](660/previews/free_0.png) \| ![free_1](660/previews/free_1.png) \| ![shorts](660/previews/shorts.png) \| ![maid_0](660/previews/maid_0.png) \| ![maid_1](660/previews/maid_1.png) \| ![miko](660/previews/miko.png) \| ![yukata](660/previews/yukata.png) \| ![suit](660/previews/suit.png) \| ![china](660/previews/china.png) \| ![bikini_0](660/previews/bikini_0.png) \| ![bikini_1](660/previews/bikini_1.png) \| ![bikini_2](660/previews/bikini_2.png) \| ![sit](660/previews/sit.png) \| ![squat](660/previews/squat.png) \| ![kneel](660/previews/kneel.png) \| ![jump](660/previews/jump.png) \| ![crossed_arms](660/previews/crossed_arms.png) \| ![angry](660/previews/angry.png) \| ![smile](660/previews/smile.png) \| ![cry](660/previews/cry.png) \| ![grin](660/previews/grin.png) \| ![n_lie_0](660/previews/n_lie_0.png) \| ![n_lie_1](660/previews/n_lie_1.png) \| ![n_stand_0](660/previews/n_stand_0.png) \| ![n_stand_1](660/previews/n_stand_1.png) \| ![n_stand_2](660/previews/n_stand_2.png) \| ![n_sex_0](660/previews/n_sex_0.png) \| ![n_sex_1](660/previews/n_sex_1.png) \| ## Anything Else? Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. ## All Steps We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * [Steps From 620 to 800](all/0.md) * [Steps From 420 to 600](all/1.md) * [Steps From 220 to 400](all/2.md) * [Steps From 20 to 200](all/3.md)	{"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/sugitani_zenjubou_fgo"], "pipeline_tag": "text-to-image"}	text-to-image	CyberHarem/sugitani_zenjubou_fgo	[ "art", "not-for-all-audiences", "text-to-image", "dataset:CyberHarem/sugitani_zenjubou_fgo", "license:mit", "region:us" ]	2024-02-06T06:38:49+00:00	[]	[]	TAGS #art #not-for-all-audiences #text-to-image #dataset-CyberHarem/sugitani_zenjubou_fgo #license-mit #region-us	Lora of Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order) =================================================== What Is This? ------------- This is the LoRA model of waifu Sugitani Zenjyubou/杉谷善住坊 (Fate/Grand Order). How Is It Trained? ------------------ * This model is trained with HCP-Diffusion. * The auto-training framework is maintained by DeepGHS Team. * The base model used for training is deepghs/animefull-latest. * Dataset used for training is the 'stage3-p480-800' in CyberHarem/sugitani\_zenjubou\_fgo, which contains 60 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 16, resolution is 720x720, clustering into 20 buckets. * Trained for 800 steps, 40 checkpoints were saved and evaluated. * Trigger word is 'sugitani\_zenjubou\_fgo'. * Pruned core tags for this waifu are 'brown\_hair, breasts, brown\_eyes, large\_breasts, yellow\_eyes, ahoge, ponytail'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. How to Use It? -------------- ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 700, you need to download '700/sugitani\_zenjubou\_fgo.pt' as the embedding and '700/sugitani\_zenjubou\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters. Which Step Should I Use? ------------------------ We selected 5 good steps for you to choose. The best one is step 700. 1480 images (1.52 GiB) were generated for auto-testing. !Metrics Plot The base model used for generating preview images is Meina/MeinaMix\_V11. Here are the preview of the recommended steps: Anything Else? -------------- Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. All Steps --------- We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * Steps From 620 to 800 * Steps From 420 to 600 * Steps From 220 to 400 * Steps From 20 to 200	[ "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 700, you need to download '700/sugitani\\_zenjubou\\_fgo.pt' as the embedding and '700/sugitani\\_zenjubou\\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 700.\n\n\n1480 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200" ]	[ "TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/sugitani_zenjubou_fgo #license-mit #region-us \n", "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 700, you need to download '700/sugitani\\_zenjubou\\_fgo.pt' as the embedding and '700/sugitani\\_zenjubou\\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 700.\n\n\n1480 images (1.52 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 620 to 800\n* Steps From 420 to 600\n* Steps From 220 to 400\n* Steps From 20 to 200" ]	[ 48, 38, 475 ]	[ "passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/sugitani_zenjubou_fgo #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. 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null	null	transformers	# Model Card for Model ID * DPO trained on ENERGY-DRINK-LOVE/SOLAR_merge2 ## Training Details ### Training Data * custom collected open-Korean DPO datasets * remove duplication data [More Information Needed] ### Training Procedure DPO	{"license": "apache-2.0", "library_name": "transformers"}	null	ENERGY-DRINK-LOVE/SOLAR_merge2_dpo	[ "transformers", "safetensors", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-06T06:40:32+00:00	[]	[]	TAGS #transformers #safetensors #license-apache-2.0 #endpoints_compatible #region-us	# Model Card for Model ID * DPO trained on ENERGY-DRINK-LOVE/SOLAR_merge2 ## Training Details ### Training Data * custom collected open-Korean DPO datasets * remove duplication data ### Training Procedure DPO	[ "# Model Card for Model ID\n\n* DPO trained on ENERGY-DRINK-LOVE/SOLAR_merge2", "## Training Details", "### Training Data\n\n* custom collected open-Korean DPO datasets\n* remove duplication data", "### Training Procedure \n\nDPO" ]	[ "TAGS\n#transformers #safetensors #license-apache-2.0 #endpoints_compatible #region-us \n", "# Model Card for Model ID\n\n* DPO trained on ENERGY-DRINK-LOVE/SOLAR_merge2", "## Training Details", "### Training Data\n\n* custom collected open-Korean DPO datasets\n* remove duplication data", "### Training Procedure \n\nDPO" ]	[ 30, 28, 3, 22, 7 ]	[ "passage: TAGS\n#transformers #safetensors #license-apache-2.0 #endpoints_compatible #region-us \n# Model Card for Model ID\n\n* DPO trained on ENERGY-DRINK-LOVE/SOLAR_merge2## Training Details### Training Data\n\n* custom collected open-Korean DPO datasets\n* remove duplication data### Training Procedure \n\nDPO" ]	[ -0.07228292524814606, 0.09506982564926147, -0.0025390717200934887, 0.003201268147677183, 0.2155381441116333, 0.016359560191631317, 0.1747826784849167, 0.016049304977059364, 0.01872035674750805, -0.048484668135643005, 0.06837460398674011, 0.20537391304969788, 0.1113729402422905, 0.15011128783226013, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GPTuz-finetuned-uzwikitext ******************************************************************************************************* Bu spaceda text generation modelni test(o'rganish maqsadida) fine-tuned qilingan. Model asosan 50 MB dataset bilan 1.30 minut oralig'ida bajarildi, Agarda modelga fine-tuning qilmoqchi bo'lsangiz, sizdan kamida 10GB va Google colab Pro version foydalanishni tafsiya qilaman, natia zo'rchiqdi. ******************************************************************************************************* It achieves the following results on the evaluation set: - Loss: 2.8346 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.4436 \| 1.0 \| 3206 \| 2.9914 \| \| 2.2235 \| 2.0 \| 6412 \| 2.8723 \| \| 2.1544 \| 3.0 \| 9618 \| 2.8346 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Tokenizers 0.15.1	{"language": ["uz"], "license": "mit", "pipeline_tag": "text-generation"}	text-generation	ai-nightcoder/GPTuz-finetuned-uzwikitext	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "uz", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:42:28+00:00	[]	[ "uz" ]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #uz #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	GPTuz-finetuned-uzwikitext ========================== * Bu spaceda text generation modelni test(o'rganish maqsadida) fine-tuned qilingan. Model asosan 50 MB dataset bilan 1.30 minut oralig'ida bajarildi, Agarda modelga fine-tuning qilmoqchi bo'lsangiz, sizdan kamida 10GB va Google colab Pro version foydalanishni tafsiya qilaman, natia zo'rchiqdi. * It achieves the following results on the evaluation set: * Loss: 2.8346 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #uz #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1" ]	[ 59, 98, 4, 27 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #uz #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Tokenizers 0.15.1" ]	[ -0.09045927971601486, -0.007780480198562145, -0.0013700980925932527, 0.08111386746168137, 0.16786383092403412, 0.012307386845350266, 0.14631955325603485, 0.102086640894413, -0.07945239543914795, 0.04729935899376869, 0.13522423803806305, 0.15497030317783356, 0.019745422527194023, 0.1214909702539444, -0.08240294456481934, -0.23160430788993835, 0.004439142066985369, 0.02983146160840988, -0.06485424935817719, 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null	null	transformers	# 🗿 ruGPT-3.5 13B Language model for Russian. Model has 13B parameters as you can guess from it's name. This is our biggest model so far and it was used for trainig GigaChat (read more about it in the [article](https://habr.com/ru/companies/sberbank/articles/730108/)). ## Dataset Model was pretrained on a 300Gb of various domains, than additionaly trained on the 100 Gb of code and legal documets. Here is the dataset structure: ![](https://habrastorage.org/getpro/habr/upload_files/384/cd1/40f/384cd140fbd9b4e7dd5427801be13ca0.png) Training data was deduplicated, the text deduplication includes 64-bit hashing of each text in the corpus for keeping texts with a unique hash. We also filter the documents based on their text compression rate using zlib4. The most strongly and weakly compressing deduplicated texts are discarded. ## Technical details Model was trained using Deepspeed and Megatron libraries, on 300B tokens dataset for 3 epochs, around 45 days on 512 V100. After that model was finetuned 1 epoch with sequence length 2048 around 20 days on 200 GPU A100 on additional data (see above). After the final training perplexity for this model was around 8.8 for Russian. ![](https://i.imgur.com/0yx67yl.png) ## Examples of usage Try different generation strategies to reach better results. ```python request = "Стих про программиста может быть таким:" encoded_input = tokenizer(request, return_tensors='pt', \ add_special_tokens=False).to('cuda:0') output = model.generate( encoded_input, num_beams=2, do_sample=True, max_new_tokens=100 ) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ``` >>> Стих про программиста может быть таким: Программист сидит в кресле, Стих сочиняет он про любовь, Он пишет, пишет, пишет, пишет... И не выходит ни черта! ``` ```python request = "Нейронная сеть — это" encoded_input = tokenizer(request, return_tensors='pt', \ add_special_tokens=False).to('cuda:0') output = model.generate( encoded_input, num_beams=4, do_sample=True, max_new_tokens=100 ) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ``` >>> Нейронная сеть — это математическая модель, состоящая из большого количества нейронов, соединенных между собой электрическими связями. Нейронная сеть может быть смоделирована на компьютере, и с ее помощью можно решать задачи, которые не поддаются решению с помощью традиционных математических методов. ``` ```python request = "Гагарин полетел в космос в" encoded_input = tokenizer(request, return_tensors='pt', \ add_special_tokens=False).to('cuda:0') output = model.generate( **encoded_input, num_beams=2, do_sample=True, max_new_tokens=100 ) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ``` >>> Гагарин полетел в космос в 1961 году. Это было первое в истории человечества космическое путешествие. Юрий Гагарин совершил его на космическом корабле Восток-1. Корабль был запущен с космодрома Байконур. ```	{"language": ["en", "ru"], "license": "mit", "tags": ["gpt3", "transformers"]}	text-generation	KrafterDen/copy	[ "transformers", "pytorch", "gpt2", "text-generation", "gpt3", "en", "ru", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:44:09+00:00	[]	[ "en", "ru" ]	TAGS #transformers #pytorch #gpt2 #text-generation #gpt3 #en #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# ruGPT-3.5 13B Language model for Russian. Model has 13B parameters as you can guess from it's name. This is our biggest model so far and it was used for trainig GigaChat (read more about it in the article). ## Dataset Model was pretrained on a 300Gb of various domains, than additionaly trained on the 100 Gb of code and legal documets. Here is the dataset structure: ![](URL Training data was deduplicated, the text deduplication includes 64-bit hashing of each text in the corpus for keeping texts with a unique hash. We also filter the documents based on their text compression rate using zlib4. The most strongly and weakly compressing deduplicated texts are discarded. ## Technical details Model was trained using Deepspeed and Megatron libraries, on 300B tokens dataset for 3 epochs, around 45 days on 512 V100. After that model was finetuned 1 epoch with sequence length 2048 around 20 days on 200 GPU A100 on additional data (see above). After the final training perplexity for this model was around 8.8 for Russian. ![](https://i.URL ## Examples of usage Try different generation strategies to reach better results.	[ "# ruGPT-3.5 13B\n\nLanguage model for Russian. Model has 13B parameters as you can guess from it's name. This is our biggest model so far and it was used for trainig GigaChat (read more about it in the article).", "## Dataset\n\nModel was pretrained on a 300Gb of various domains, than additionaly trained on the 100 Gb of code and legal documets. Here is the dataset structure:\n\n![](URL\n\nTraining data was deduplicated, the text deduplication includes 64-bit hashing of each text in the corpus for keeping texts with a unique hash. We also filter the documents based on their text compression rate using zlib4. The most strongly and weakly compressing deduplicated texts are discarded.", "## Technical details\n\nModel was trained using Deepspeed and Megatron libraries, on 300B tokens dataset for 3 epochs, around 45 days on 512 V100. After that model was finetuned 1 epoch with sequence length 2048 around 20 days on 200 GPU A100 on additional data (see above).\n\nAfter the final training perplexity for this model was around 8.8 for Russian.\n\n![](https://i.URL", "## Examples of usage\n\nTry different generation strategies to reach better results." ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt3 #en #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ruGPT-3.5 13B\n\nLanguage model for Russian. Model has 13B parameters as you can guess from it's name. This is our biggest model so far and it was used for trainig GigaChat (read more about it in the article).", "## Dataset\n\nModel was pretrained on a 300Gb of various domains, than additionaly trained on the 100 Gb of code and legal documets. Here is the dataset structure:\n\n![](URL\n\nTraining data was deduplicated, the text deduplication includes 64-bit hashing of each text in the corpus for keeping texts with a unique hash. We also filter the documents based on their text compression rate using zlib4. The most strongly and weakly compressing deduplicated texts are discarded.", "## Technical details\n\nModel was trained using Deepspeed and Megatron libraries, on 300B tokens dataset for 3 epochs, around 45 days on 512 V100. After that model was finetuned 1 epoch with sequence length 2048 around 20 days on 200 GPU A100 on additional data (see above).\n\nAfter the final training perplexity for this model was around 8.8 for Russian.\n\n![](https://i.URL", "## Examples of usage\n\nTry different generation strategies to reach better results." ]	[ 60, 55, 121, 98, 15 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #gpt3 #en #ru #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ruGPT-3.5 13B\n\nLanguage model for Russian. Model has 13B parameters as you can guess from it's name. This is our biggest model so far and it was used for trainig GigaChat (read more about it in the article).## Dataset\n\nModel was pretrained on a 300Gb of various domains, than additionaly trained on the 100 Gb of code and legal documets. Here is the dataset structure:\n\n![](URL\n\nTraining data was deduplicated, the text deduplication includes 64-bit hashing of each text in the corpus for keeping texts with a unique hash. We also filter the documents based on their text compression rate using zlib4. The most strongly and weakly compressing deduplicated texts are discarded.## Technical details\n\nModel was trained using Deepspeed and Megatron libraries, on 300B tokens dataset for 3 epochs, around 45 days on 512 V100. After that model was finetuned 1 epoch with sequence length 2048 around 20 days on 200 GPU A100 on additional data (see above).\n\nAfter the final training perplexity for this model was around 8.8 for Russian.\n\n![](https://i.URL## Examples of usage\n\nTry different generation strategies to reach better results." ]	[ -0.1106293722987175, -0.02898118458688259, -0.00041247467743232846, 0.06586306542158127, 0.06663593649864197, 0.049007244408130646, 0.02296552248299122, 0.10537455976009369, -0.02987976372241974, 0.11883533000946045, 0.06606552004814148, -0.11620030552148819, 0.0494183786213398, 0.05000165477395058, 0.01748531498014927, -0.24934490025043488, 0.016091952100396156, -0.027939727529883385, 0.00019840465392917395, 0.07204484194517136, 0.14281190931797028, -0.08177702873945236, 0.04002670571208, -0.037420403212308884, 0.0003981828922405839, -0.010370718315243721, -0.009858541190624237, -0.003926579840481281, 0.0765654444694519, 0.008343227207660675, 0.016850242391228676, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	tyson0420/stack-llama-2-web	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:45:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06061961501836777, 0.15481999516487122, -0.004844071343541145, 0.02074851468205452, 0.0983177199959755, 0.007407687604427338, 0.07119518518447876, 0.11185134947299957, -0.023851769044995308, 0.1167980208992958, 0.031993988901376724, 0.09781743586063385, 0.11217817664146423, 0.16186554729938507, 0.0015333457849919796, -0.22897611558437347, 0.049678247421979904, -0.125278040766716, -0.0294334813952446, 0.11977242678403854, 0.1422213912010193, -0.10954539477825165, 0.0752737894654274, -0.038042325526475906, -0.005828251596540213, -0.0323176346719265, -0.06205610930919647, -0.05266609415411949, 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null	null	null	Original model: https://huggingface.co/DAMO-NLP-MT/polylm-chat-13b # Model Card for PolyLM-Multialpaca This model is finetuned on [polyLM-13b](https://huggingface.co/DAMO-NLP-MT/polylm-13b) using the following datasets: # Demo [Open](https://modelscope.cn/studios/damo/demo-polylm-multialpaca-13b/summary) # Bias, Risks, and Limitations The information below in this section are copied from the model's [official model card](https://arxiv.org/pdf/2307.06018.pdf): > Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. # Citation BibTeX: ```bibtex @misc{wei2023polylm, title={PolyLM: An Open Source Polyglot Large Language Model}, author={Xiangpeng Wei and Haoran Wei and Huan Lin and Tianhao Li and Pei Zhang and Xingzhang Ren and Mei Li and Yu Wan and Zhiwei Cao and Binbin Xie and Tianxiang Hu and Shangjie Li and Binyuan Hui and Bowen Yu and Dayiheng Liu and Baosong Yang and Fei Huang and Jun Xie}, year={2023}, eprint={2307.06018}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```	{"license": "apache-2.0"}	null	Sosnitskij/polylm-chat-13b-gguf	[ "gguf", "arxiv:2307.06018", "license:apache-2.0", "region:us" ]	2024-02-06T06:45:50+00:00	[ "2307.06018" ]	[]	TAGS #gguf #arxiv-2307.06018 #license-apache-2.0 #region-us	Original model: URL # Model Card for PolyLM-Multialpaca This model is finetuned on polyLM-13b using the following datasets: # Demo Open # Bias, Risks, and Limitations The information below in this section are copied from the model's official model card: > Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application. BibTeX:	[ "# Model Card for PolyLM-Multialpaca\n\nThis model is finetuned on polyLM-13b using the following datasets:", "# Demo\nOpen", "# Bias, Risks, and Limitations\n\nThe information below in this section are copied from the model's official model card:\n\n> Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.\n\nBibTeX:" ]	[ "TAGS\n#gguf #arxiv-2307.06018 #license-apache-2.0 #region-us \n", "# Model Card for PolyLM-Multialpaca\n\nThis model is finetuned on polyLM-13b using the following datasets:", "# Demo\nOpen", "# Bias, Risks, and Limitations\n\nThe information below in this section are copied from the model's official model card:\n\n> Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.\n\nBibTeX:" ]	[ 25, 29, 3, 122 ]	[ "passage: TAGS\n#gguf #arxiv-2307.06018 #license-apache-2.0 #region-us \n# Model Card for PolyLM-Multialpaca\n\nThis model is finetuned on polyLM-13b using the following datasets:# Demo\nOpen# Bias, Risks, and Limitations\n\nThe information below in this section are copied from the model's official model card:\n\n> Our contributions are fully methodological: adding the support of multilingualism to LLM during training and SFT phases. It is unavoidable that PolyLM might exhibit several common deficiencies of language models, e.g. hallucination and toxicity. PolyLM should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application.\n\nBibTeX:" ]	[ -0.07694800198078156, -0.003832208923995495, -0.0032171246130019426, 0.08892849087715149, 0.09044913947582245, 0.02589600160717964, 0.18602341413497925, -0.004531085956841707, 0.07030679285526276, -0.019696708768606186, 0.15103453397750854, 0.020258476957678795, 0.043562617152929306, 0.09524308145046234, 0.01582084782421589, -0.11507944017648697, 0.05005201697349548, -0.052176568657159805, 0.012144417501986027, 0.08980162441730499, 0.06570253521203995, -0.018752053380012512, 0.10131511837244034, 0.07999244332313538, -0.12267062813043594, -0.06206631660461426, 0.05898025259375572, -0.005066779907792807, 0.06277142465114594, 0.15878790616989136, 0.008600753732025623, 0.026749469339847565, -0.0034254705533385277, -0.19390162825584412, 0.055648382753133774, 0.015892144292593002, -0.021240461617708206, 0.04429454356431961, 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null	null	transformers	# miqu-1-120b ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6303ca537373aacccd85d8a7/LxO9j7OykuabKLYQHIodG.jpeg) * EXL2: [2.4bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2) \| [2.65bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.65bpw-h6-exl2) \| [3.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-3.0bpw-h6-exl2) \| 4.0bpw \| [5.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-5.0bpw-h6-exl2) * GGUF: [Q2_K-Q5_K_M](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-GGUF/) \| [IQ3_XXS](https://huggingface.co/wolfram/miqu-1-120b-GGUF) * HF FP16: [wolfram/miqu-1-120b](https://huggingface.co/wolfram/miqu-1-120b) This is a 120b frankenmerge of [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) created by interleaving layers of [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) with itself using [mergekit](https://github.com/cg123/mergekit). Inspired by [Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2), [MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b), and [goliath-120b](https://huggingface.co/alpindale/goliath-120b). Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, [Lone Striker](https://huggingface.co/LoneStriker)! ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` See also: [🐺🐦‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/18ljvxb/llm_prompt_format_comparisontest_mixtral_8x7b/) ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 20] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [10, 30] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [20, 40] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [30, 50] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [40, 60] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [50, 70] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [60, 80] model: 152334H/miqu-1-70b-sf ``` ## Credits & Special Thanks * original (unreleased) model: [mistralai (Mistral AI_)](https://huggingface.co/mistralai) * leaked model: [miqudev/miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) * f16 model: [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) * mergekit: [arcee-ai/mergekit: Tools for merging pretrained large language models.](https://github.com/arcee-ai/mergekit) * mergekit_config.yml: [nsfwthrowitaway69/Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2) ### Support * [My Ko-fi page](https://ko-fi.com/wolframravenwolf) if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS [BASED ON A LEAKED ASSET](https://huggingface.co/miqudev/miqu-1-70b/discussions/10) AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	{"language": ["en", "de", "fr", "es", "it"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["152334H/miqu-1-70b-sf"]}	text-generation	LoneStriker/wolfram_miqu-1-120b-4.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "de", "fr", "es", "it", "base_model:152334H/miqu-1-70b-sf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:46:49+00:00	[]	[ "en", "de", "fr", "es", "it" ]	TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# miqu-1-120b !image/jpeg * EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw * GGUF: Q2_K-Q5_K_M \| IQ3_XXS * HF FP16: wolfram/miqu-1-120b This is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit. Inspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b. Thanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, Lone Striker! ## Prompt template: Mistral See also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * 152334H/miqu-1-70b-sf ### Configuration The following YAML configuration was used to produce this model: ## Credits & Special Thanks * original (unreleased) model: mistralai (Mistral AI_) * leaked model: miqudev/miqu-1-70b * f16 model: 152334H/miqu-1-70b-sf * mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models. * mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2 ### Support * My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	[ "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ 85, 206, 44, 31, 4, 17, 28, 17, 107, 69, 43 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ -0.08452296257019043, 0.07996358722448349, -0.006216804962605238, 0.07489742338657379, 0.062447689473629, 0.048032015562057495, 0.10850238054990768, 0.11730548739433289, 0.04997159540653229, 0.1304655373096466, 0.019059622660279274, 0.0882313996553421, 0.10314993560314178, 0.1546645164489746, -0.0008784975507296622, -0.13700617849826813, 0.03406155854463577, 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null	null	diffusers	# SDXL LoRA DreamBooth - AndyMacool/youzi_lora <Gallery /> ## Model description These are AndyMacool/youzi_lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using [DreamBooth](https://dreambooth.github.io/). LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use YOU12ZI to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](AndyMacool/youzi_lora/tree/main) them in the Files & versions tab.	{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "YOU12ZI"}	text-to-image	AndyMacool/youzi_lora	[ "diffusers", "tensorboard", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "has_space", "region:us" ]	2024-02-06T06:50:32+00:00	[]	[]	TAGS #diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us	# SDXL LoRA DreamBooth - AndyMacool/youzi_lora <Gallery /> ## Model description These are AndyMacool/youzi_lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained using DreamBooth. LoRA for the text encoder was enabled: False. Special VAE used for training: madebyollin/sdxl-vae-fp16-fix. ## Trigger words You should use YOU12ZI to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. Download them in the Files & versions tab.	[ "# SDXL LoRA DreamBooth - AndyMacool/youzi_lora\n\n<Gallery />", "## Model description\n\nThese are AndyMacool/youzi_lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use YOU12ZI to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ "TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n", "# SDXL LoRA DreamBooth - AndyMacool/youzi_lora\n\n<Gallery />", "## Model description\n\nThese are AndyMacool/youzi_lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.", "## Trigger words\n\nYou should use YOU12ZI to trigger the image generation.", "## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ 86, 23, 88, 16, 28 ]	[ "passage: TAGS\n#diffusers #tensorboard #stable-diffusion-xl #stable-diffusion-xl-diffusers #text-to-image #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #license-openrail++ #has_space #region-us \n# SDXL LoRA DreamBooth - AndyMacool/youzi_lora\n\n<Gallery />## Model description\n\nThese are AndyMacool/youzi_lora LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.\n\nThe weights were trained using DreamBooth.\n\nLoRA for the text encoder was enabled: False.\n\nSpecial VAE used for training: madebyollin/sdxl-vae-fp16-fix.## Trigger words\n\nYou should use YOU12ZI to trigger the image generation.## Download model\n\nWeights for this model are available in Safetensors format.\n\nDownload them in the Files & versions tab." ]	[ -0.08291801810264587, 0.15645891427993774, 0.0002142619778169319, 0.04682808369398117, 0.1519443839788437, 0.03818978741765022, 0.1825568526983261, 0.08811298757791519, 0.09585180878639221, 0.08578862994909286, 0.04177426919341087, 0.04136626049876213, 0.05594373866915703, 0.20016299188137054, -0.018728015944361687, -0.196250319480896, 0.011166650801897049, 0.0047994754277169704, -0.04334042966365814, 0.023908259347081184, 0.04029730334877968, -0.0775936171412468, 0.08661036193370819, -0.024593690410256386, -0.06058221682906151, 0.04006580263376236, 0.002358958823606372, -0.030370088294148445, 0.013182793743908405, 0.053841885179281235, 0.06819406896829605, 0.06361761689186096, 0.07752515375614166, -0.15658703446388245, 0.03212793543934822, 0.08959480375051498, -0.02726241759955883, 0.05588458478450775, -0.031047601252794266, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	GowthamMl/deepseeker-finetuned-v1	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-06T06:52:49+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # nslPOS This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "nslPOS", "results": []}]}	null	saniasinghania/nslPOS	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	2024-02-06T06:54:47+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us	# nslPOS This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.0 - Pytorch 2.1.2+cu118 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# nslPOS\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.1.2+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us \n", "# nslPOS\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.1.2+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 70, 34, 6, 12, 8, 3, 103, 4, 35 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us \n# nslPOS\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- Transformers 4.36.0\n- Pytorch 2.1.2+cu118\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.06370986253023148, 0.1284140646457672, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-lora-1.58M-snli-model2 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8331 - Accuracy: 0.687 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 6 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5164 \| 1.0 \| 2146 \| 0.4262 \| 0.8406 \| \| 0.4687 \| 2.0 \| 4292 \| 0.3904 \| 0.8540 \| \| 0.4562 \| 3.0 \| 6438 \| 0.3824 \| 0.8575 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-large-cased", "model-index": [{"name": "bert-large-cased-lora-1.58M-snli-model2", "results": []}]}	text-classification	varun-v-rao/bert-large-cased-lora-1.58M-snli-model2	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-large-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T06:57:08+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	bert-large-cased-lora-1.58M-snli-model2 ======================================= This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.8331 * Accuracy: 0.687 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 256 * eval\_batch\_size: 256 * seed: 6 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 6\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 6\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 68, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 6\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.08380687236785889, 0.09604667127132416, -0.0022442650515586138, 0.1115875169634819, 0.14797595143318176, 0.022443508729338646, 0.15785902738571167, 0.10879659652709961, -0.06997837126255035, 0.050637874752283096, 0.12808381021022797, 0.12629282474517822, 0.012304246425628662, 0.12039896100759506, -0.06541161984205246, -0.22940978407859802, 0.00501704216003418, 0.0354124940931797, -0.07027395069599152, 0.11373141407966614, 0.09139653295278549, 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null	null	transformers	# dolly-v2-3b Model Card ## Summary Databricks' `dolly-v2-3b`, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on `pythia-2.8b`, Dolly is trained on ~15k instruction/response fine tuning records [`databricks-dolly-15k`](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. `dolly-v2-3b` is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these larger models sizes: * [dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b), a 12 billion parameter based on `pythia-12b` * [dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b), a 6.9 billion parameter based on `pythia-6.9b` Please refer to the [dolly GitHub repo](https://github.com/databrickslabs/dolly#getting-started-with-response-generation) for tips on running inference for various GPU configurations. Owner: Databricks, Inc. ## Model Overview `dolly-v2-3b` is a 2.8 billion parameter causal language model created by [Databricks](https://databricks.com/) that is derived from [EleutherAI's](https://www.eleuther.ai/) [Pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) and fine-tuned on a [~15K record instruction corpus](https://github.com/databrickslabs/dolly/tree/master/data) generated by Databricks employees and released under a permissive license (CC-BY-SA) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` and `accelerate` libraries installed. In a Databricks notebook you could run: ```python %pip install "accelerate>=0.16.0,<1" "transformers[torch]>=4.28.1,<5" "torch>=1.13.1,<2" ``` The instruction following pipeline can be loaded using the `pipeline` function as shown below. This loads a custom `InstructionTextGenerationPipeline` found in the model repo [here](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), which is why `trust_remote_code=True` is required. Including `torch_dtype=torch.bfloat16` is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto") ``` You can then use the pipeline to answer instructions: ```python res = generate_text("Explain to me the difference between nuclear fission and fusion.") print(res[0]["generated_text"]) ``` Alternatively, if you prefer to not use `trust_remote_code=True` you can download [instruct_pipeline.py](https://huggingface.co/databricks/dolly-v2-3b/blob/main/instruct_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ```python import torch from instruct_pipeline import InstructionTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("databricks/dolly-v2-3b", padding_side="left") model = AutoModelForCausalLM.from_pretrained("databricks/dolly-v2-3b", device_map="auto", torch_dtype=torch.bfloat16) generate_text = InstructionTextGenerationPipeline(model=model, tokenizer=tokenizer) ``` ### LangChain Usage To use the pipeline with LangChain, you must set `return_full_text=True`, as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. ```python import torch from transformers import pipeline generate_text = pipeline(model="databricks/dolly-v2-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", return_full_text=True) ``` You can create a prompt that either has only an instruction or has an instruction with context: ```python from langchain import PromptTemplate, LLMChain from langchain.llms import HuggingFacePipeline # template for an instrution with no input prompt = PromptTemplate( input_variables=["instruction"], template="{instruction}") # template for an instruction with input prompt_with_context = PromptTemplate( input_variables=["instruction", "context"], template="{instruction}\n\nInput:\n{context}") hf_pipeline = HuggingFacePipeline(pipeline=generate_text) llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt) llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context) ``` Example predicting using a simple instruction: ```python print(llm_chain.predict(instruction="Explain to me the difference between nuclear fission and fusion.").lstrip()) ``` Example predicting using an instruction with context: ```python context = """George Washington (February 22, 1732[b] - December 14, 1799) was an American military officer, statesman, and Founding Father who served as the first president of the United States from 1789 to 1797.""" print(llm_context_chain.predict(instruction="When was George Washington president?", context=context).lstrip()) ``` ## Known Limitations ### Performance Limitations `dolly-v2-3b` is not a state-of-the-art generative language model and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, `dolly-v2-3b` struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that `dolly-v2-3b` does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, `dolly-v2-3b` reflects the content and limitations of its training corpuses. - The Pile: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. - `databricks-dolly-15k`: The training data on which `dolly-v2-3b` is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the [EleutherAI LLM Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness); model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that `dolly-v2-3b` is not state of the art. It underperforms `dolly-v1-6b` in the evaluation benchmarks, which is not surprising considering it has half the number of parameters. \| model \| openbookqa \| arc_easy \| winogrande \| hellaswag \| arc_challenge \| piqa \| boolq \| gmean \| \| --------------------------------- \| ------------ \| ---------- \| ------------ \| ----------- \| --------------- \| -------- \| -------- \| ---------\| \| EleutherAI/pythia-2.8b \| 0.348 \| 0.585859 \| 0.589582 \| 0.591217 \| 0.323379 \| 0.73395 \| 0.638226 \| 0.523431 \| \| EleutherAI/pythia-6.9b \| 0.368 \| 0.604798 \| 0.608524 \| 0.631548 \| 0.343857 \| 0.761153 \| 0.6263 \| 0.543567 \| \| databricks/dolly-v2-3b \| 0.384 \| 0.611532 \| 0.589582 \| 0.650767 \| 0.370307 \| 0.742655 \| 0.575535 \| 0.544886 \| \| EleutherAI/pythia-12b \| 0.364 \| 0.627104 \| 0.636148 \| 0.668094 \| 0.346416 \| 0.760065 \| 0.673394 \| 0.559676 \| \| EleutherAI/gpt-j-6B \| 0.382 \| 0.621633 \| 0.651144 \| 0.662617 \| 0.363481 \| 0.761153 \| 0.655963 \| 0.565936 \| \| databricks/dolly-v2-12b \| 0.408 \| 0.63931 \| 0.616417 \| 0.707927 \| 0.388225 \| 0.757889 \| 0.568196 \| 0.56781 \| \| databricks/dolly-v2-7b \| 0.392 \| 0.633838 \| 0.607735 \| 0.686517 \| 0.406997 \| 0.750816 \| 0.644037 \| 0.573487 \| \| databricks/dolly-v1-6b \| 0.41 \| 0.62963 \| 0.643252 \| 0.676758 \| 0.384812 \| 0.773667 \| 0.687768 \| 0.583431 \| \| EleutherAI/gpt-neox-20b \| 0.402 \| 0.683923 \| 0.656669 \| 0.7142 \| 0.408703 \| 0.784004 \| 0.695413 \| 0.602236 \| # Citation ``` @online{DatabricksBlog2023DollyV2, author = {Mike Conover and Matt Hayes and Ankit Mathur and Jianwei Xie and Jun Wan and Sam Shah and Ali Ghodsi and Patrick Wendell and Matei Zaharia and Reynold Xin}, title = {Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM}, year = {2023}, url = {https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm}, urldate = {2023-06-30} } ``` # Happy Hacking!	{"language": ["en"], "license": "mit", "library_name": "transformers", "datasets": ["databricks/databricks-dolly-15k"], "inference": false}	text-generation	BashitAli/GPT_model	[ "transformers", "pytorch", "gpt_neox", "text-generation", "en", "dataset:databricks/databricks-dolly-15k", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]	2024-02-06T06:59:34+00:00	[]	[ "en" ]	TAGS #transformers #pytorch #gpt_neox #text-generation #en #dataset-databricks/databricks-dolly-15k #license-mit #autotrain_compatible #text-generation-inference #region-us	dolly-v2-3b Model Card ====================== Summary ------- Databricks' 'dolly-v2-3b', an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on 'pythia-2.8b', Dolly is trained on ~15k instruction/response fine tuning records 'databricks-dolly-15k' generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. 'dolly-v2-3b' is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based. Dolly v2 is also available in these larger models sizes: * dolly-v2-12b, a 12 billion parameter based on 'pythia-12b' * dolly-v2-7b, a 6.9 billion parameter based on 'pythia-6.9b' Please refer to the dolly GitHub repo for tips on running inference for various GPU configurations. Owner: Databricks, Inc. Model Overview -------------- 'dolly-v2-3b' is a 2.8 billion parameter causal language model created by Databricks that is derived from EleutherAI's Pythia-2.8b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA) Usage ----- To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' and 'accelerate' libraries installed. In a Databricks notebook you could run: The instruction following pipeline can be loaded using the 'pipeline' function as shown below. This loads a custom 'InstructionTextGenerationPipeline' found in the model repo here, which is why 'trust\_remote\_code=True' is required. Including 'torch\_dtype=torch.bfloat16' is generally recommended if this type is supported in order to reduce memory usage. It does not appear to impact output quality. It is also fine to remove it if there is sufficient memory. You can then use the pipeline to answer instructions: Alternatively, if you prefer to not use 'trust\_remote\_code=True' you can download instruct\_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer: ### LangChain Usage To use the pipeline with LangChain, you must set 'return\_full\_text=True', as LangChain expects the full text to be returned and the default for the pipeline is to only return the new text. You can create a prompt that either has only an instruction or has an instruction with context: Example predicting using a simple instruction: Example predicting using an instruction with context: Known Limitations ----------------- ### Performance Limitations 'dolly-v2-3b' is not a state-of-the-art generative language model and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses. The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, 'dolly-v2-3b' struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc. Moreover, we find that 'dolly-v2-3b' does not have some capabilities, such as well-formatted letter writing, present in the original model. ### Dataset Limitations Like all language models, 'dolly-v2-3b' reflects the content and limitations of its training corpuses. * The Pile: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations. * 'databricks-dolly-15k': The training data on which 'dolly-v2-3b' is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or personally identifying information about non-public figures, but it may contain typos and factual errors. The dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects the interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large. Databricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that maximize the potential of all individuals and organizations. ### Benchmark Metrics Below you'll find various models benchmark performance on the EleutherAI LLM Evaluation Harness; model results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that 'dolly-v2-3b' is not state of the art. It underperforms 'dolly-v1-6b' in the evaluation benchmarks, which is not surprising considering it has half the number of parameters. Happy Hacking! ==============	[ "### LangChain Usage\n\n\nTo use the pipeline with LangChain, you must set 'return\\_full\\_text=True', as LangChain expects the full text to be returned\nand the default for the pipeline is to only return the new text.\n\n\nYou can create a prompt that either has only an instruction or has an instruction with context:\n\n\nExample predicting using a simple instruction:\n\n\nExample predicting using an instruction with context:\n\n\nKnown Limitations\n-----------------", "### Performance Limitations\n\n\n'dolly-v2-3b' is not a state-of-the-art generative language model and, though quantitative benchmarking is ongoing, is not designed to perform\ncompetitively with more modern model architectures or models subject to larger pretraining corpuses.\n\n\nThe Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. \n\nIn particular, 'dolly-v2-3b' struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors,\ndates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.\nMoreover, we find that 'dolly-v2-3b' does not have some capabilities, such as well-formatted letter writing, present in the original model.", "### Dataset Limitations\n\n\nLike all language models, 'dolly-v2-3b' reflects the content and limitations of its training corpuses.\n\n\n* The Pile: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,\nit contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly\nin the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit\nassociations.\n* 'databricks-dolly-15k': The training data on which 'dolly-v2-3b' is instruction tuned represents natural language instructions generated\nby Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages\nfor instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or\npersonally identifying information about non-public figures, but it may contain typos and factual errors.\nThe dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects\nthe interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large.\n\n\nDatabricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that\nmaximize the potential of all individuals and organizations.", "### Benchmark Metrics\n\n\nBelow you'll find various models benchmark performance on the EleutherAI LLM Evaluation Harness;\nmodel results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that 'dolly-v2-3b' is not state of the art.\nIt underperforms 'dolly-v1-6b' in the evaluation benchmarks, which is not surprising considering it has half the number of parameters.\n\n\n\nHappy Hacking!\n==============" ]	[ "TAGS\n#transformers #pytorch #gpt_neox #text-generation #en #dataset-databricks/databricks-dolly-15k #license-mit #autotrain_compatible #text-generation-inference #region-us \n", "### LangChain Usage\n\n\nTo use the pipeline with LangChain, you must set 'return\\_full\\_text=True', as LangChain expects the full text to be returned\nand the default for the pipeline is to only return the new text.\n\n\nYou can create a prompt that either has only an instruction or has an instruction with context:\n\n\nExample predicting using a simple instruction:\n\n\nExample predicting using an instruction with context:\n\n\nKnown Limitations\n-----------------", "### Performance Limitations\n\n\n'dolly-v2-3b' is not a state-of-the-art generative language model and, though quantitative benchmarking is ongoing, is not designed to perform\ncompetitively with more modern model architectures or models subject to larger pretraining corpuses.\n\n\nThe Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. \n\nIn particular, 'dolly-v2-3b' struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors,\ndates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.\nMoreover, we find that 'dolly-v2-3b' does not have some capabilities, such as well-formatted letter writing, present in the original model.", "### Dataset Limitations\n\n\nLike all language models, 'dolly-v2-3b' reflects the content and limitations of its training corpuses.\n\n\n* The Pile: GPT-J's pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets,\nit contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly\nin the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit\nassociations.\n* 'databricks-dolly-15k': The training data on which 'dolly-v2-3b' is instruction tuned represents natural language instructions generated\nby Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages\nfor instruction categories like closed QA and summarization. To our knowledge it does not contain obscenity, intellectual property or\npersonally identifying information about non-public figures, but it may contain typos and factual errors.\nThe dataset may also reflect biases found in Wikipedia. Finally, the dataset likely reflects\nthe interests and semantic choices of Databricks employees, a demographic which is not representative of the global population at large.\n\n\nDatabricks is committed to ongoing research and development efforts to develop helpful, honest and harmless AI technologies that\nmaximize the potential of all individuals and organizations.", "### Benchmark Metrics\n\n\nBelow you'll find various models benchmark performance on the EleutherAI LLM Evaluation Harness;\nmodel results are sorted by geometric mean to produce an intelligible ordering. As outlined above, these results demonstrate that 'dolly-v2-3b' is not state of the art.\nIt underperforms 'dolly-v1-6b' in the evaluation benchmarks, which is not surprising considering it has half the number of parameters.\n\n\n\nHappy Hacking!\n==============" ]	[ 64, 109, 227, 332, 119 ]	[ "passage: TAGS\n#transformers #pytorch #gpt_neox #text-generation #en #dataset-databricks/databricks-dolly-15k #license-mit #autotrain_compatible #text-generation-inference #region-us \n### LangChain Usage\n\n\nTo use the pipeline with LangChain, you must set 'return\\_full\\_text=True', as LangChain expects the full text to be returned\nand the default for the pipeline is to only return the new text.\n\n\nYou can create a prompt that either has only an instruction or has an instruction with context:\n\n\nExample predicting using a simple instruction:\n\n\nExample predicting using an instruction with context:\n\n\nKnown Limitations\n-----------------### Performance Limitations\n\n\n'dolly-v2-3b' is not a state-of-the-art generative language model and, though quantitative benchmarking is ongoing, is not designed to perform\ncompetitively with more modern model architectures or models subject to larger pretraining corpuses.\n\n\nThe Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. \n\nIn particular, 'dolly-v2-3b' struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors,\ndates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.\nMoreover, we find that 'dolly-v2-3b' does not have some capabilities, such as well-formatted letter writing, present in the original model." ]	[ -0.04902537539601326, 0.02031327225267887, -0.004264208488166332, 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null	null	null	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-7B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/second-state/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-7B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-7B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Qwen1.5-7B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q2_K.gguf) \| Q2_K \| 2 \| 3.10 GB\| smallest, significant quality loss - not recommended for most purposes \| \| [Qwen1.5-7B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 4.22 GB\| small, substantial quality loss \| \| [Qwen1.5-7B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.92 GB\| very small, high quality loss \| \| [Qwen1.5-7B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.57 GB\| very small, high quality loss \| \| [Qwen1.5-7B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_0.gguf) \| Q4_0 \| 4 \| 4.51 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Qwen1.5-7B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.77 GB\| medium, balanced quality - recommended \| \| [Qwen1.5-7B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.54 GB\| small, greater quality loss \| \| [Qwen1.5-7B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_0.gguf) \| Q5_0 \| 5 \| 5.40 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Qwen1.5-7B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.53 GB\| large, very low quality loss - recommended \| \| [Qwen1.5-7B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.4 GB\| large, low quality loss - recommended \| \| [Qwen1.5-7B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q6_K.gguf) \| Q6_K \| 6 \| 6.34 GB\| very large, extremely low quality loss \| \| [Qwen1.5-7B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q8_0.gguf) \| Q8_0 \| 8 \| 8.21 GB\| very large, extremely low quality loss - not recommended \|	{"language": ["en"], "license": "other", "tags": ["chat"], "model_name": "Qwen1.5 7B Chat", "base_model": "Qwen/Qwen1.5-7B-Chat", "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/LICENSE", "model_creator": "Qwen", "quantized_by": "Second State Inc.", "pipeline_tag": "text-generation"}	text-generation	second-state/Qwen1.5-7B-Chat-GGUF	[ "gguf", "chat", "text-generation", "en", "base_model:Qwen/Qwen1.5-7B-Chat", "license:other", "region:us" ]	2024-02-06T06:59:47+00:00	[]	[ "en" ]	TAGS #gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-7B-Chat #license-other #region-us	![](URL style=) --- Qwen1.5-7B-Chat-GGUF ==================== Original Model -------------- Qwen/Qwen1.5-7B-Chat Run with LlamaEdge ------------------ * LlamaEdge version: v0.2.15 and above * Prompt template + Prompt type: 'chatml' + Prompt string * Run as LlamaEdge service * Run as LlamaEdge command app Quantized GGUF Models ---------------------	[]	[ "TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-7B-Chat #license-other #region-us \n" ]	[ 38 ]	[ "passage: TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-7B-Chat #license-other #region-us \n" ]	[ -0.010023923590779305, -0.012251344509422779, -0.003757887752726674, -0.015251122415065765, 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null	null	null	https://civitai.com/models/122359/detail-tweaker-xl?modelVersionId=135867	{}	null	Yuriy81/DetailTweakerXL_lora	[ "region:us" ]	2024-02-06T07:01:14+00:00	[]	[]	TAGS #region-us	URL	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]	[ 0.024608636274933815, -0.026205500587821007, -0.009666500613093376, -0.10395516455173492, 0.08638657629489899, 0.059816278517246246, 0.01882290467619896, 0.020661840215325356, 0.23975107073783875, -0.005599027033895254, 0.1219947561621666, 0.0015615287702530622, -0.037353623658418655, 0.03733762726187706, -0.0035912662278860807, -0.17583473026752472, 0.03876631706953049, -0.018274923786520958, 0.01843859627842903, 0.026470553129911423, -0.07776834815740585, -0.07564429938793182, 0.015296397730708122, -0.10247814655303955, -0.083692267537117, 0.11002834886312485, 0.031466204673051834, -0.019670886918902397, 0.10779199749231339, -0.04243955761194229, 0.18699054419994354, -0.011512263678014278, -0.11213519424200058, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_eli5_clm-model This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.7407 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.9157 \| 1.0 \| 560 \| 3.7574 \| \| 3.8162 \| 2.0 \| 1120 \| 3.7432 \| \| 3.7746 \| 3.0 \| 1680 \| 3.7407 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilgpt2", "model-index": [{"name": "my_awesome_eli5_clm-model", "results": []}]}	text-generation	UjjwalP/my_awesome_eli5_clm-model	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilgpt2", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T07:04:58+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	my\_awesome\_eli5\_clm-model ============================ This model is a fine-tuned version of distilgpt2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.7407 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ 77, 98, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-distilgpt2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ -0.09235620498657227, 0.09283239394426346, -0.0020225364714860916, 0.10447591543197632, 0.12615281343460083, 0.000539363652933389, 0.15626034140586853, 0.12509731948375702, -0.06289782375097275, 0.03875778988003731, 0.1471046507358551, 0.12405938655138016, 0.029295656830072403, 0.13688847422599792, -0.055826954543590546, -0.2088497281074524, 0.02179415337741375, 0.027372518554329872, -0.05079090967774391, 0.12472070753574371, 0.09375898540019989, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # checkpoints This model is a fine-tuned version of [kavg/LiLT-RE-ZH](https://huggingface.co/kavg/LiLT-RE-ZH) on the xfun dataset. It achieves the following results on the evaluation set: - Precision: 0.4194 - Recall: 0.5166 - F1: 0.4629 - Loss: 0.1587 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| F1 \| Validation Loss \| Precision \| Recall \| \|:-------------:\|:------:\|:----:\|:------:\|:---------------:\|:---------:\|:------:\| \| 0.0851 \| 41.67 \| 250 \| 0.3776 \| 0.1733 \| 0.4 \| 0.3576 \| \| 0.0596 \| 83.33 \| 500 \| 0.4629 \| 0.1587 \| 0.4194 \| 0.5166 \| \| 0.0385 \| 125.0 \| 750 \| 0.5181 \| 0.2059 \| 0.4471 \| 0.6159 \| \| 0.0198 \| 166.67 \| 1000 \| 0.5311 \| 0.2371 \| 0.4631 \| 0.6225 \| \| 0.015 \| 208.33 \| 1250 \| 0.5299 \| 0.2241 \| 0.465 \| 0.6159 \| \| 0.0169 \| 250.0 \| 1500 \| 0.5057 \| 0.2671 \| 0.4467 \| 0.5828 \| \| 0.0158 \| 291.67 \| 1750 \| 0.5341 \| 0.2537 \| 0.4839 \| 0.5960 \| \| 0.0184 \| 333.33 \| 2000 \| 0.5187 \| 0.2883 \| 0.4592 \| 0.5960 \| \| 0.013 \| 375.0 \| 2250 \| 0.5215 \| 0.2755 \| 0.4596 \| 0.6026 \| \| 0.0027 \| 416.67 \| 2500 \| 0.5210 \| 0.3146 \| 0.4515 \| 0.6159 \| \| 0.0094 \| 458.33 \| 2750 \| 0.5239 \| 0.3298 \| 0.4559 \| 0.6159 \| \| 0.0042 \| 500.0 \| 3000 \| 0.5158 \| 0.3348 \| 0.4545 \| 0.5960 \| \| 0.0057 \| 541.67 \| 3250 \| 0.5254 \| 0.3423 \| 0.4581 \| 0.6159 \| \| 0.0049 \| 583.33 \| 3500 \| 0.5254 \| 0.3517 \| 0.4581 \| 0.6159 \| \| 0.0131 \| 625.0 \| 3750 \| 0.5341 \| 0.3328 \| 0.4677 \| 0.6225 \| \| 0.0077 \| 666.67 \| 4000 \| 0.5326 \| 0.3384 \| 0.4653 \| 0.6225 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["xfun"], "metrics": ["precision", "recall", "f1"], "base_model": "kavg/LiLT-RE-ZH", "model-index": [{"name": "checkpoints", "results": []}]}	null	kavg/LiLT-RE-ZH-SIN	[ "transformers", "safetensors", "lilt", "generated_from_trainer", "dataset:xfun", "base_model:kavg/LiLT-RE-ZH", "license:mit", "endpoints_compatible", "region:us" ]	2024-02-06T07:05:30+00:00	[]	[]	TAGS #transformers #safetensors #lilt #generated_from_trainer #dataset-xfun #base_model-kavg/LiLT-RE-ZH #license-mit #endpoints_compatible #region-us	checkpoints =========== This model is a fine-tuned version of kavg/LiLT-RE-ZH on the xfun dataset. It achieves the following results on the evaluation set: * Precision: 0.4194 * Recall: 0.5166 * F1: 0.4629 * Loss: 0.1587 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 8 * eval\_batch\_size: 2 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 4000 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 4000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #lilt #generated_from_trainer #dataset-xfun #base_model-kavg/LiLT-RE-ZH #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 4000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 57, 115, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #lilt #generated_from_trainer #dataset-xfun #base_model-kavg/LiLT-RE-ZH #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 2\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 4000### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.1150847002863884, 0.03121502324938774, -0.0011505711590871215, 0.09313024580478668, 0.1461421549320221, 0.018554477021098137, 0.10845281928777695, 0.11453615874052048, -0.08778540790081024, 0.030916592106223106, 0.12830661237239838, 0.13907849788665771, 0.00880915205925703, 0.17564842104911804, -0.059255219995975494, -0.2351016253232956, 0.010735034942626953, 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null	null	null	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0-3-layer-mlp-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "24.70 +/- 27.06", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	TanHanlin/Reinforce-Pixelcopter-PLE-v0-3-layer-mlp-v0	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	2024-02-06T07:06:12+00:00	[]	[]	TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL	[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 41, 58 ]	[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 0.0073175891302526, -0.2259262204170227, -0.0017347558168694377, 0.05054566636681557, 0.0658537745475769, -0.055378563702106476, 0.1412602812051773, 0.05916554853320122, -0.04990595206618309, 0.059261854737997055, 0.14166708290576935, 0.03996060788631439, 0.022112762555480003, 0.1513713151216507, 0.09764605015516281, -0.2469022423028946, 0.07438477873802185, 0.01641594059765339, 0.008152224123477936, 0.09583204984664917, 0.060265738517045975, -0.1405058205127716, 0.037032704800367355, -0.01332044042646885, -0.13650871813297272, 0.0010478810872882605, -0.021802188828587532, -0.03625129908323288, 0.15681709349155426, 0.006844013463705778, 0.09602472931146622, -0.001560068572871387, 0.06475798785686493, -0.12438877671957016, 0.05466329678893089, 0.06455880403518677, -0.06293967366218567, 0.058029334992170334, -0.057374246418476105, 0.11959903687238693, 0.04641333222389221, -0.01578129455447197, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	shidowake/test-240206-cyber2base-7B-qlora-adaptor	[ "transformers", "tensorboard", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-06T07:07:08+00:00	[ "1910.09700" ]	[]	TAGS #transformers #tensorboard #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #tensorboard #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 35, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05129265785217285, 0.22092880308628082, -0.0035179967526346445, 0.02501223422586918, 0.11708144098520279, -0.0005181307788006961, 0.04833335056900978, 0.126449316740036, -0.019315702840685844, 0.11368890851736069, 0.02830384485423565, 0.087161585688591, 0.10932693630456924, 0.14976787567138672, 0.027767986059188843, -0.22400330007076263, 0.013545092195272446, -0.08547151833772659, 0.007360507268458605, 0.10887812823057175, 0.13407185673713684, -0.10422329604625702, 0.0799589455127716, -0.022218724712729454, -0.01576193980872631, -0.0024709447752684355, -0.09191098809242249, -0.07704412192106247, 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null	null	transformers	# Model Card for Model ID merge(ties) model * base model for ENERGY-DRINK-LOVE/SOLAR_merge2_dpo	{"license": "apache-2.0", "library_name": "transformers"}	text-generation	ENERGY-DRINK-LOVE/SOLAR_merge2	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T07:07:28+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID merge(ties) model * base model for ENERGY-DRINK-LOVE/SOLAR_merge2_dpo	[ "# Model Card for Model ID\n\nmerge(ties) model\n\n* base model for ENERGY-DRINK-LOVE/SOLAR_merge2_dpo" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID\n\nmerge(ties) model\n\n* base model for ENERGY-DRINK-LOVE/SOLAR_merge2_dpo" ]	[ 59, 34 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID\n\nmerge(ties) model\n\n* base model for ENERGY-DRINK-LOVE/SOLAR_merge2_dpo" ]	[ -0.05547250062227249, -0.003707007272168994, -0.0045189084485173225, -0.007060525473207235, 0.08705079555511475, 0.021368956193327904, 0.20419573783874512, 0.029576925560832024, 0.06726778298616409, -0.024634862318634987, 0.10710130631923676, 0.17125500738620758, 0.04742515832185745, 0.1649344563484192, -0.06739622354507446, -0.15354694426059723, 0.14162424206733704, 0.043411701917648315, 0.008861851878464222, 0.0635051354765892, 0.13338401913642883, -0.02757152169942856, 0.09661448001861572, -0.036647979170084, -0.10551515221595764, 0.029409293085336685, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-lora-1.77M-snli-model3 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.7590 - Accuracy: 0.7255 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 84 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5194 \| 1.0 \| 2146 \| 0.4198 \| 0.8470 \| \| 0.4687 \| 2.0 \| 4292 \| 0.3898 \| 0.8574 \| \| 0.4586 \| 3.0 \| 6438 \| 0.3852 \| 0.8599 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "t5-base", "model-index": [{"name": "t5-base-lora-1.77M-snli-model3", "results": []}]}	text-classification	varun-v-rao/t5-base-lora-1.77M-snli-model3	[ "transformers", "tensorboard", "safetensors", "t5", "text-classification", "generated_from_trainer", "base_model:t5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T07:08:12+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	t5-base-lora-1.77M-snli-model3 ============================== This model is a fine-tuned version of t5-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.7590 * Accuracy: 0.7255 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 256 * eval\_batch\_size: 256 * seed: 84 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 74, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text-classification #generated_from_trainer #base_model-t5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 84\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.08506516367197037, 0.09446289390325546, -0.0021764938719570637, 0.11164454370737076, 0.14140236377716064, 0.0059529514983296394, 0.16060514748096466, 0.1175040453672409, -0.07660192251205444, 0.04582127928733826, 0.12605127692222595, 0.12365572899580002, 0.02987685054540634, 0.13668763637542725, -0.0862204059958458, -0.21965326368808746, 0.013931778259575367, 0.02073846198618412, -0.05457837134599686, 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null	null	null	https://civitai.com/models/184528?modelVersionId=207142	{}	null	Yuriy81/VasilyLozhkin_lora	[ "region:us" ]	2024-02-06T07:09:25+00:00	[]	[]	TAGS #region-us	URL	[]	[ "TAGS\n#region-us \n" ]	[ 6 ]	[ "passage: TAGS\n#region-us \n" ]	[ 0.024608636274933815, -0.026205500587821007, -0.009666500613093376, -0.10395516455173492, 0.08638657629489899, 0.059816278517246246, 0.01882290467619896, 0.020661840215325356, 0.23975107073783875, -0.005599027033895254, 0.1219947561621666, 0.0015615287702530622, -0.037353623658418655, 0.03733762726187706, -0.0035912662278860807, -0.17583473026752472, 0.03876631706953049, -0.018274923786520958, 0.01843859627842903, 0.026470553129911423, -0.07776834815740585, -0.07564429938793182, 0.015296397730708122, -0.10247814655303955, -0.083692267537117, 0.11002834886312485, 0.031466204673051834, -0.019670886918902397, 0.10779199749231339, -0.04243955761194229, 0.18699054419994354, -0.011512263678014278, -0.11213519424200058, -0.2536850869655609, 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null	null	transformers	<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # wkqco33/transformer_study This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.7143 - Validation Loss: 0.5352 - Train Accuracy: 0.9 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 12000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 2.7907 \| 1.6296 \| 0.816 \| 0 \| \| 1.2019 \| 0.8294 \| 0.883 \| 1 \| \| 0.7143 \| 0.5352 \| 0.9 \| 2 \| ### Framework versions - Transformers 4.35.2 - TensorFlow 2.15.0 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "wkqco33/transformer_study", "results": []}]}	image-classification	wkqco33/transformer_study	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T07:12:44+00:00	[]	[]	TAGS #transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	wkqco33/transformer\_study ========================== This model is a fine-tuned version of google/vit-base-patch16-224-in21k on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 0.7143 * Validation Loss: 0.5352 * Train Accuracy: 0.9 * Epoch: 2 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 3e-05, 'decay\_steps': 12000, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01} * training\_precision: float32 ### Training results ### Framework versions * Transformers 4.35.2 * TensorFlow 2.15.0 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 12000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 12000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 73, 227, 4, 31 ]	[ "passage: TAGS\n#transformers #tf #vit #image-classification #generated_from_keras_callback #base_model-google/vit-base-patch16-224-in21k #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* optimizer: {'name': 'AdamWeightDecay', 'learning\\_rate': {'module': 'keras.optimizers.schedules', 'class\\_name': 'PolynomialDecay', 'config': {'initial\\_learning\\_rate': 3e-05, 'decay\\_steps': 12000, 'end\\_learning\\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\\_name': None}, 'decay': 0.0, 'beta\\_1': 0.9, 'beta\\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\\_decay\\_rate': 0.01}\n* training\\_precision: float32### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* TensorFlow 2.15.0\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.06904222071170807, 0.09147489815950394, -0.007227006834000349, 0.08273784071207047, 0.14767566323280334, 0.0562569722533226, 0.1297539323568344, 0.123295359313488, -0.08313818275928497, 0.13441653549671173, 0.09632871299982071, 0.10123317688703537, 0.059004731476306915, 0.11456355452537537, -0.05947503447532654, -0.14696024358272552, 0.03985626995563507, -0.04118388891220093, 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null	null	transformers	# miqu-1-120b ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6303ca537373aacccd85d8a7/LxO9j7OykuabKLYQHIodG.jpeg) * EXL2: [2.4bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.4bpw-h6-exl2) \| [2.65bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-2.65bpw-h6-exl2) \| [3.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-3.0bpw-h6-exl2) \| [4.0bpw](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-4.0bpw-h6-exl2) \| 5.0bpw * GGUF: [Q2_K-Q5_K_M](https://huggingface.co/LoneStriker/wolfram_miqu-1-120b-GGUF/) \| [IQ3_XXS](https://huggingface.co/wolfram/miqu-1-120b-GGUF) * HF FP16: [wolfram/miqu-1-120b](https://huggingface.co/wolfram/miqu-1-120b) This is a 120b frankenmerge of [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) created by interleaving layers of [miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) with itself using [mergekit](https://github.com/cg123/mergekit). Inspired by [Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2), [MegaDolphin-120b](https://huggingface.co/cognitivecomputations/MegaDolphin-120b), and [goliath-120b](https://huggingface.co/alpindale/goliath-120b). Thanks for the support, [CopilotKit](https://github.com/CopilotKit/CopilotKit) - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, [Lone Striker](https://huggingface.co/LoneStriker)! ## Prompt template: Mistral ``` <s>[INST] {prompt} [/INST] ``` See also: [🐺🐦‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/comments/18ljvxb/llm_prompt_format_comparisontest_mixtral_8x7b/) ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 20] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [10, 30] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [20, 40] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [30, 50] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [40, 60] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [50, 70] model: 152334H/miqu-1-70b-sf - sources: - layer_range: [60, 80] model: 152334H/miqu-1-70b-sf ``` ## Credits & Special Thanks * original (unreleased) model: [mistralai (Mistral AI_)](https://huggingface.co/mistralai) * leaked model: [miqudev/miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b) * f16 model: [152334H/miqu-1-70b-sf](https://huggingface.co/152334H/miqu-1-70b-sf) * mergekit: [arcee-ai/mergekit: Tools for merging pretrained large language models.](https://github.com/arcee-ai/mergekit) * mergekit_config.yml: [nsfwthrowitaway69/Venus-120b-v1.2](https://huggingface.co/nsfwthrowitaway69/Venus-120b-v1.2) ### Support * [My Ko-fi page](https://ko-fi.com/wolframravenwolf) if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS [BASED ON A LEAKED ASSET](https://huggingface.co/miqudev/miqu-1-70b/discussions/10) AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	{"language": ["en", "de", "fr", "es", "it"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["152334H/miqu-1-70b-sf"]}	text-generation	LoneStriker/wolfram_miqu-1-120b-5.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "en", "de", "fr", "es", "it", "base_model:152334H/miqu-1-70b-sf", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T07:12:47+00:00	[]	[ "en", "de", "fr", "es", "it" ]	TAGS #transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# miqu-1-120b !image/jpeg * EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw * GGUF: Q2_K-Q5_K_M \| IQ3_XXS * HF FP16: wolfram/miqu-1-120b This is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit. Inspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b. Thanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub. Thanks for the EXL2 and GGUF quants, Lone Striker! ## Prompt template: Mistral See also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA ## Model Details * Max Context: 32764 tokens (kept the weird number from the original/base model) * Layers: 140 ## Merge Details ### Merge Method This model was merged using the passthrough merge method. ### Models Merged The following models were included in the merge: * 152334H/miqu-1-70b-sf ### Configuration The following YAML configuration was used to produce this model: ## Credits & Special Thanks * original (unreleased) model: mistralai (Mistral AI_) * leaked model: miqudev/miqu-1-70b * f16 model: 152334H/miqu-1-70b-sf * mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models. * mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2 ### Support * My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it! #### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.	[ "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!", "## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA", "## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140", "## Merge Details", "### Merge Method\n\nThis model was merged using the passthrough merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf", "### Configuration\n\nThe following YAML configuration was used to produce this model:", "## Credits & Special Thanks\n\n* original (unreleased) model: mistralai (Mistral AI_)\n* leaked model: miqudev/miqu-1-70b\n* f16 model: 152334H/miqu-1-70b-sf\n* mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.\n* mergekit_config.yml: nsfwthrowitaway69/Venus-120b-v1.2", "### Support\n\n* My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!", "#### DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK." ]	[ 85, 206, 44, 31, 4, 17, 28, 17, 107, 69, 43 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #en #de #fr #es #it #base_model-152334H/miqu-1-70b-sf #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# miqu-1-120b\n\n!image/jpeg\n\n* EXL2: 2.4bpw \| 2.65bpw \| 3.0bpw \| 4.0bpw \| 5.0bpw\n* GGUF: Q2_K-Q5_K_M \| IQ3_XXS\n* HF FP16: wolfram/miqu-1-120b\n\nThis is a 120b frankenmerge of miqu-1-70b created by interleaving layers of miqu-1-70b-sf with itself using mergekit.\n\nInspired by Venus-120b-v1.2, MegaDolphin-120b, and goliath-120b.\n\nThanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.\n\nThanks for the EXL2 and GGUF quants, Lone Striker!## Prompt template: Mistral\n\n\n\nSee also: ‍⬛ LLM Prompt Format Comparison/Test: Mixtral 8x7B Instruct with 17 different instruct templates : LocalLLaMA## Model Details\n\n* Max Context: 32764 tokens (kept the weird number from the original/base model)\n* Layers: 140## Merge Details### Merge Method\n\nThis model was merged using the passthrough merge method.### Models Merged\n\nThe following models were included in the merge:\n* 152334H/miqu-1-70b-sf### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ -0.08452296257019043, 0.07996358722448349, -0.006216804962605238, 0.07489742338657379, 0.062447689473629, 0.048032015562057495, 0.10850238054990768, 0.11730548739433289, 0.04997159540653229, 0.1304655373096466, 0.019059622660279274, 0.0882313996553421, 0.10314993560314178, 0.1546645164489746, -0.0008784975507296622, -0.13700617849826813, 0.03406155854463577, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "alexsherstinsky/Mistral-7B-v0.1-sharded"}	null	SudiptoPramanik/MistraWithRef_RL_RL_ExtractiveSummary	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:alexsherstinsky/Mistral-7B-v0.1-sharded", "region:us" ]	2024-02-06T07:23:38+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-alexsherstinsky/Mistral-7B-v0.1-sharded #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-alexsherstinsky/Mistral-7B-v0.1-sharded #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 45, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-alexsherstinsky/Mistral-7B-v0.1-sharded #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.110109843313694, 0.20426276326179504, -0.0033242744393646717, 0.02683502435684204, 0.07316219061613083, 0.012933526188135147, 0.07240378856658936, 0.13029123842716217, 0.0330861434340477, 0.12623506784439087, 0.0663594976067543, 0.11143319308757782, 0.11400832235813141, 0.21561922132968903, -0.003767105983570218, -0.17360882461071014, 0.020239561796188354, -0.05931909754872322, 0.034331753849983215, 0.12480589002370834, 0.13765163719654083, -0.09187468141317368, 0.07441461831331253, -0.023067325353622437, -0.007257964462041855, -0.030011918395757675, 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null	null	null	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```	{"license": "other", "tags": ["autotrain", "text-generation"], "widget": [{"text": "I love AutoTrain because "}]}	text-generation	karthikrathod/llm_repo_v8_10e	[ "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	2024-02-06T07:24:08+00:00	[]	[]	TAGS #safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us	# Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit AutoTrain. # Usage	[ "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]	[ "TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n", "# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. For more information, please visit AutoTrain.", "# Usage" ]	[ 37, 29, 3 ]	[ "passage: TAGS\n#safetensors #autotrain #text-generation #conversational #license-other #endpoints_compatible #region-us \n# Model Trained Using AutoTrain\n\nThis model was trained using AutoTrain. 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text2text-generation	Kishan11/test	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T07:24:47+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 59, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.051328662782907486, 0.16320262849330902, -0.005184381268918514, 0.02251756191253662, 0.0970073789358139, 0.014154063537716866, 0.06843415647745132, 0.1107407882809639, -0.01975325681269169, 0.11416343599557877, 0.03334542736411095, 0.09905029088258743, 0.11230985820293427, 0.15210844576358795, -0.0026883550453931093, -0.22916288673877716, 0.04717505723237991, -0.126259446144104, -0.0391545407474041, 0.11665356904268265, 0.14936181902885437, -0.10113005340099335, 0.07722076028585434, -0.030988991260528564, -0.0076387980952858925, -0.032814256846904755, -0.0584266260266304, -0.04825444892048836, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1	{"library_name": "peft", "base_model": "openai/whisper-small"}	null	unanam/medi_lora_test	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:openai/whisper-small", "region:us" ]	2024-02-06T07:25:08+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.1	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.7.1" ]	[ 37, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-openai/whisper-small #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.7.1" ]	[ -0.1076493114233017, 0.1983276903629303, -0.0030863555148243904, 0.0321795716881752, 0.0915658101439476, 0.015891052782535553, 0.05633755400776863, 0.12364462018013, -0.023953059688210487, 0.10961870849132538, 0.06933917105197906, 0.09555713087320328, 0.10782624781131744, 0.22033579647541046, 0.004158330149948597, -0.20340892672538757, 0.030735481530427933, -0.09517928212881088, -0.0060378434136509895, 0.12312088161706924, 0.14362573623657227, -0.09643791615962982, 0.07792773842811584, -0.013907761313021183, -0.0032701713498681784, -0.03422047570347786, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1_5-finetuned-gsm8k This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "phi-1_5-finetuned-gsm8k", "results": []}]}	null	akashAD/phi-1_5-finetuned-gsm8k	[ "peft", "tensorboard", "safetensors", "phi", "generated_from_trainer", "custom_code", "base_model:microsoft/phi-1_5", "license:mit", "region:us" ]	2024-02-06T07:27:42+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us	# phi-1_5-finetuned-gsm8k This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 1000 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# phi-1_5-finetuned-gsm8k\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us \n", "# phi-1_5-finetuned-gsm8k\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 48, 37, 6, 12, 8, 3, 89, 4, 44 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #phi #generated_from_trainer #custom_code #base_model-microsoft/phi-1_5 #license-mit #region-us \n# phi-1_5-finetuned-gsm8k\n\nThis model is a fine-tuned version of microsoft/phi-1_5 on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 1000### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.1424432396888733, 0.0838576927781105, -0.0014683984918519855, 0.08383697271347046, 0.1324382871389389, 0.012549459002912045, 0.11031016707420349, 0.10926586389541626, -0.10265687108039856, 0.10819031298160553, 0.07279889285564423, 0.04566620662808418, 0.06258460134267807, 0.19331002235412598, -0.01869063638150692, -0.21761859953403473, 0.04274963214993477, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	Karajan42/NeuralMiria-Mistral-7B	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T07:28:15+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05921921506524086, 0.15253323316574097, -0.004925556480884552, 0.01970141939818859, 0.09812989830970764, 0.008722675032913685, 0.07155127823352814, 0.11091651022434235, -0.02038503810763359, 0.11541511863470078, 0.03161177039146423, 0.09504877775907516, 0.11244720220565796, 0.1593349277973175, 0.0006018498679623008, -0.22924894094467163, 0.050943523645401, -0.12565383315086365, -0.028005311265587807, 0.1202453151345253, 0.14323006570339203, -0.10873830318450928, 0.07482945919036865, -0.03924073651432991, -0.006830108352005482, -0.03327549248933792, -0.06254202127456665, -0.05196645110845566, 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null	null	null	# Lora of okita_souji_alter/沖田総司〔オルタ〕/冲田总司〔Alter〕 (Fate/Grand Order) ## What Is This? This is the LoRA model of waifu okita_souji_alter/沖田総司〔オルタ〕/冲田总司〔Alter〕 (Fate/Grand Order). ## How Is It Trained? * This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). * The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs). * The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest). * Dataset used for training is the `stage3-p480-800` in [CyberHarem/okita_souji_alter_fgo](https://huggingface.co/datasets/CyberHarem/okita_souji_alter_fgo), which contains 1271 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets. * Trained for 10000 steps, 40 checkpoints were saved and evaluated. * Trigger word is `okita_souji_alter_fgo`. * Pruned core tags for this waifu are `dark_skin, dark-skinned_female, white_hair, ahoge, bow, breasts, hair_bow, hair_between_eyes, black_bow, hair_ornament, tassel, large_breasts, long_hair, bangs, very_long_hair, yellow_eyes`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. ## How to Use It? ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8250, you need to download [`8250/okita_souji_alter_fgo.pt`](https://huggingface.co/CyberHarem/okita_souji_alter_fgo/resolve/main/8250/okita_souji_alter_fgo.pt) as the embedding and [`8250/okita_souji_alter_fgo.safetensors`](https://huggingface.co/CyberHarem/okita_souji_alter_fgo/resolve/main/8250/okita_souji_alter_fgo.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters. ## Which Step Should I Use? We selected 5 good steps for you to choose. The best one is step 8250. 1960 images (2.04 GiB) were generated for auto-testing. ![Metrics Plot](metrics_plot.png) The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). Here are the preview of the recommended steps: \| Step \| Epoch \| CCIP \| AI Corrupt \| Bikini Plus \| Score \| Download \| pattern_0_0 \| pattern_0_1 \| pattern_1_0 \| pattern_1_1 \| pattern_2 \| pattern_3 \| pattern_4_0 \| pattern_4_1 \| pattern_5 \| pattern_6_0 \| pattern_6_1 \| pattern_7_0 \| pattern_7_1 \| pattern_8 \| portrait_0 \| portrait_1 \| portrait_2 \| full_body_0 \| full_body_1 \| profile_0 \| profile_1 \| free_0 \| free_1 \| shorts \| maid_0 \| maid_1 \| miko \| yukata \| suit \| china \| bikini_0 \| bikini_1 \| bikini_2 \| sit \| squat \| kneel \| jump \| crossed_arms \| angry \| smile \| cry \| grin \| n_lie_0 \| n_lie_1 \| n_stand_0 \| n_stand_1 \| n_stand_2 \| n_sex_0 \| n_sex_1 \| \|-------:\|--------:\|:----------\|:-------------\|:--------------\|:----------\|:----------------------------------------------------------------------------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:------------------------------------------\|:--------------------------------------------\|:--------------------------------------------\|:--------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:--------------------------------\|:------------------------------------\|:--------------------------------\|:----------------------------------\|:----------------------------------------\|:----------------------------------------\|:----------------------------------------\|:------------------------------\|:----------------------------------\|:----------------------------------\|:--------------------------------\|:------------------------------------------------\|:----------------------------------\|:----------------------------------\|:------------------------------\|:--------------------------------\|:--------------------------------------\|:--------------------------------------\|:------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:--------------------------------------\|:--------------------------------------\| \| 8250 \| 26 \| 0.953 \| 0.993 \| 0.831 \| 0.728 \| [Download](https://huggingface.co/CyberHarem/okita_souji_alter_fgo/resolve/main/8250/okita_souji_alter_fgo.zip) \| ![pattern_0_0](8250/previews/pattern_0_0.png) \| ![pattern_0_1](8250/previews/pattern_0_1.png) \| ![pattern_1_0](8250/previews/pattern_1_0.png) \| ![pattern_1_1](8250/previews/pattern_1_1.png) \| ![pattern_2](8250/previews/pattern_2.png) \| ![pattern_3](8250/previews/pattern_3.png) \| ![pattern_4_0](8250/previews/pattern_4_0.png) \| ![pattern_4_1](8250/previews/pattern_4_1.png) \| ![pattern_5](8250/previews/pattern_5.png) \| ![pattern_6_0](8250/previews/pattern_6_0.png) \| ![pattern_6_1](8250/previews/pattern_6_1.png) \| ![pattern_7_0](8250/previews/pattern_7_0.png) \| ![pattern_7_1](8250/previews/pattern_7_1.png) \| ![pattern_8](8250/previews/pattern_8.png) \| ![portrait_0](8250/previews/portrait_0.png) \| ![portrait_1](8250/previews/portrait_1.png) \| ![portrait_2](8250/previews/portrait_2.png) \| ![full_body_0](8250/previews/full_body_0.png) \| ![full_body_1](8250/previews/full_body_1.png) \| ![profile_0](8250/previews/profile_0.png) \| ![profile_1](8250/previews/profile_1.png) \| ![free_0](8250/previews/free_0.png) \| ![free_1](8250/previews/free_1.png) \| ![shorts](8250/previews/shorts.png) \| ![maid_0](8250/previews/maid_0.png) \| ![maid_1](8250/previews/maid_1.png) \| ![miko](8250/previews/miko.png) \| ![yukata](8250/previews/yukata.png) \| ![suit](8250/previews/suit.png) \| ![china](8250/previews/china.png) \| ![bikini_0](8250/previews/bikini_0.png) \| ![bikini_1](8250/previews/bikini_1.png) \| ![bikini_2](8250/previews/bikini_2.png) \| ![sit](8250/previews/sit.png) \| ![squat](8250/previews/squat.png) \| ![kneel](8250/previews/kneel.png) \| ![jump](8250/previews/jump.png) \| ![crossed_arms](8250/previews/crossed_arms.png) \| ![angry](8250/previews/angry.png) \| ![smile](8250/previews/smile.png) \| ![cry](8250/previews/cry.png) \| ![grin](8250/previews/grin.png) \| ![n_lie_0](8250/previews/n_lie_0.png) \| ![n_lie_1](8250/previews/n_lie_1.png) \| ![n_stand_0](8250/previews/n_stand_0.png) \| ![n_stand_1](8250/previews/n_stand_1.png) \| ![n_stand_2](8250/previews/n_stand_2.png) \| ![n_sex_0](8250/previews/n_sex_0.png) \| ![n_sex_1](8250/previews/n_sex_1.png) \| \| 7500 \| 24 \| 0.953 \| 0.990 \| 0.830 \| 0.727 \| [Download](https://huggingface.co/CyberHarem/okita_souji_alter_fgo/resolve/main/7500/okita_souji_alter_fgo.zip) \| ![pattern_0_0](7500/previews/pattern_0_0.png) \| ![pattern_0_1](7500/previews/pattern_0_1.png) \| ![pattern_1_0](7500/previews/pattern_1_0.png) \| ![pattern_1_1](7500/previews/pattern_1_1.png) \| ![pattern_2](7500/previews/pattern_2.png) \| ![pattern_3](7500/previews/pattern_3.png) \| ![pattern_4_0](7500/previews/pattern_4_0.png) \| ![pattern_4_1](7500/previews/pattern_4_1.png) \| ![pattern_5](7500/previews/pattern_5.png) \| ![pattern_6_0](7500/previews/pattern_6_0.png) \| ![pattern_6_1](7500/previews/pattern_6_1.png) \| ![pattern_7_0](7500/previews/pattern_7_0.png) \| ![pattern_7_1](7500/previews/pattern_7_1.png) \| ![pattern_8](7500/previews/pattern_8.png) \| ![portrait_0](7500/previews/portrait_0.png) \| ![portrait_1](7500/previews/portrait_1.png) \| ![portrait_2](7500/previews/portrait_2.png) \| ![full_body_0](7500/previews/full_body_0.png) \| ![full_body_1](7500/previews/full_body_1.png) \| ![profile_0](7500/previews/profile_0.png) \| ![profile_1](7500/previews/profile_1.png) \| ![free_0](7500/previews/free_0.png) \| ![free_1](7500/previews/free_1.png) \| ![shorts](7500/previews/shorts.png) \| ![maid_0](7500/previews/maid_0.png) \| ![maid_1](7500/previews/maid_1.png) \| ![miko](7500/previews/miko.png) \| ![yukata](7500/previews/yukata.png) \| ![suit](7500/previews/suit.png) \| ![china](7500/previews/china.png) \| ![bikini_0](7500/previews/bikini_0.png) \| ![bikini_1](7500/previews/bikini_1.png) \| ![bikini_2](7500/previews/bikini_2.png) \| ![sit](7500/previews/sit.png) \| ![squat](7500/previews/squat.png) \| ![kneel](7500/previews/kneel.png) \| ![jump](7500/previews/jump.png) \| ![crossed_arms](7500/previews/crossed_arms.png) \| ![angry](7500/previews/angry.png) \| ![smile](7500/previews/smile.png) \| ![cry](7500/previews/cry.png) \| ![grin](7500/previews/grin.png) \| ![n_lie_0](7500/previews/n_lie_0.png) \| ![n_lie_1](7500/previews/n_lie_1.png) \| ![n_stand_0](7500/previews/n_stand_0.png) \| ![n_stand_1](7500/previews/n_stand_1.png) \| ![n_stand_2](7500/previews/n_stand_2.png) \| ![n_sex_0](7500/previews/n_sex_0.png) \| ![n_sex_1](7500/previews/n_sex_1.png) \| \| 5000 \| 16 \| 0.951 \| 0.994 \| 0.828 \| 0.720 \| [Download](https://huggingface.co/CyberHarem/okita_souji_alter_fgo/resolve/main/5000/okita_souji_alter_fgo.zip) \| ![pattern_0_0](5000/previews/pattern_0_0.png) \| ![pattern_0_1](5000/previews/pattern_0_1.png) \| ![pattern_1_0](5000/previews/pattern_1_0.png) \| ![pattern_1_1](5000/previews/pattern_1_1.png) \| ![pattern_2](5000/previews/pattern_2.png) \| ![pattern_3](5000/previews/pattern_3.png) \| ![pattern_4_0](5000/previews/pattern_4_0.png) \| ![pattern_4_1](5000/previews/pattern_4_1.png) \| ![pattern_5](5000/previews/pattern_5.png) \| ![pattern_6_0](5000/previews/pattern_6_0.png) \| ![pattern_6_1](5000/previews/pattern_6_1.png) \| ![pattern_7_0](5000/previews/pattern_7_0.png) \| ![pattern_7_1](5000/previews/pattern_7_1.png) \| ![pattern_8](5000/previews/pattern_8.png) \| ![portrait_0](5000/previews/portrait_0.png) \| ![portrait_1](5000/previews/portrait_1.png) \| ![portrait_2](5000/previews/portrait_2.png) \| ![full_body_0](5000/previews/full_body_0.png) \| ![full_body_1](5000/previews/full_body_1.png) \| ![profile_0](5000/previews/profile_0.png) \| ![profile_1](5000/previews/profile_1.png) \| ![free_0](5000/previews/free_0.png) \| ![free_1](5000/previews/free_1.png) \| ![shorts](5000/previews/shorts.png) \| ![maid_0](5000/previews/maid_0.png) \| ![maid_1](5000/previews/maid_1.png) \| ![miko](5000/previews/miko.png) \| ![yukata](5000/previews/yukata.png) \| ![suit](5000/previews/suit.png) \| ![china](5000/previews/china.png) \| ![bikini_0](5000/previews/bikini_0.png) \| ![bikini_1](5000/previews/bikini_1.png) \| ![bikini_2](5000/previews/bikini_2.png) \| ![sit](5000/previews/sit.png) \| ![squat](5000/previews/squat.png) \| ![kneel](5000/previews/kneel.png) \| ![jump](5000/previews/jump.png) \| ![crossed_arms](5000/previews/crossed_arms.png) \| ![angry](5000/previews/angry.png) \| ![smile](5000/previews/smile.png) \| ![cry](5000/previews/cry.png) \| ![grin](5000/previews/grin.png) \| ![n_lie_0](5000/previews/n_lie_0.png) \| ![n_lie_1](5000/previews/n_lie_1.png) \| ![n_stand_0](5000/previews/n_stand_0.png) \| ![n_stand_1](5000/previews/n_stand_1.png) \| ![n_stand_2](5000/previews/n_stand_2.png) \| ![n_sex_0](5000/previews/n_sex_0.png) \| ![n_sex_1](5000/previews/n_sex_1.png) \| \| 4000 \| 13 \| 0.943 \| 0.991 \| 0.833 \| 0.716 \| [Download](https://huggingface.co/CyberHarem/okita_souji_alter_fgo/resolve/main/4000/okita_souji_alter_fgo.zip) \| ![pattern_0_0](4000/previews/pattern_0_0.png) \| ![pattern_0_1](4000/previews/pattern_0_1.png) \| ![pattern_1_0](4000/previews/pattern_1_0.png) \| ![pattern_1_1](4000/previews/pattern_1_1.png) \| ![pattern_2](4000/previews/pattern_2.png) \| ![pattern_3](4000/previews/pattern_3.png) \| ![pattern_4_0](4000/previews/pattern_4_0.png) \| ![pattern_4_1](4000/previews/pattern_4_1.png) \| ![pattern_5](4000/previews/pattern_5.png) \| ![pattern_6_0](4000/previews/pattern_6_0.png) \| ![pattern_6_1](4000/previews/pattern_6_1.png) \| ![pattern_7_0](4000/previews/pattern_7_0.png) \| ![pattern_7_1](4000/previews/pattern_7_1.png) \| ![pattern_8](4000/previews/pattern_8.png) \| ![portrait_0](4000/previews/portrait_0.png) \| ![portrait_1](4000/previews/portrait_1.png) \| ![portrait_2](4000/previews/portrait_2.png) \| ![full_body_0](4000/previews/full_body_0.png) \| ![full_body_1](4000/previews/full_body_1.png) \| ![profile_0](4000/previews/profile_0.png) \| ![profile_1](4000/previews/profile_1.png) \| ![free_0](4000/previews/free_0.png) \| ![free_1](4000/previews/free_1.png) \| ![shorts](4000/previews/shorts.png) \| ![maid_0](4000/previews/maid_0.png) \| ![maid_1](4000/previews/maid_1.png) \| ![miko](4000/previews/miko.png) \| ![yukata](4000/previews/yukata.png) \| ![suit](4000/previews/suit.png) \| ![china](4000/previews/china.png) \| ![bikini_0](4000/previews/bikini_0.png) \| ![bikini_1](4000/previews/bikini_1.png) \| ![bikini_2](4000/previews/bikini_2.png) \| ![sit](4000/previews/sit.png) \| ![squat](4000/previews/squat.png) \| ![kneel](4000/previews/kneel.png) \| ![jump](4000/previews/jump.png) \| ![crossed_arms](4000/previews/crossed_arms.png) \| ![angry](4000/previews/angry.png) \| ![smile](4000/previews/smile.png) \| ![cry](4000/previews/cry.png) \| ![grin](4000/previews/grin.png) \| ![n_lie_0](4000/previews/n_lie_0.png) \| ![n_lie_1](4000/previews/n_lie_1.png) \| ![n_stand_0](4000/previews/n_stand_0.png) \| ![n_stand_1](4000/previews/n_stand_1.png) \| ![n_stand_2](4000/previews/n_stand_2.png) \| ![n_sex_0](4000/previews/n_sex_0.png) \| ![n_sex_1](4000/previews/n_sex_1.png) \| \| 9250 \| 30 \| 0.944 \| 0.990 \| 0.828 \| 0.710 \| [Download](https://huggingface.co/CyberHarem/okita_souji_alter_fgo/resolve/main/9250/okita_souji_alter_fgo.zip) \| ![pattern_0_0](9250/previews/pattern_0_0.png) \| ![pattern_0_1](9250/previews/pattern_0_1.png) \| ![pattern_1_0](9250/previews/pattern_1_0.png) \| ![pattern_1_1](9250/previews/pattern_1_1.png) \| ![pattern_2](9250/previews/pattern_2.png) \| ![pattern_3](9250/previews/pattern_3.png) \| ![pattern_4_0](9250/previews/pattern_4_0.png) \| ![pattern_4_1](9250/previews/pattern_4_1.png) \| ![pattern_5](9250/previews/pattern_5.png) \| ![pattern_6_0](9250/previews/pattern_6_0.png) \| ![pattern_6_1](9250/previews/pattern_6_1.png) \| ![pattern_7_0](9250/previews/pattern_7_0.png) \| ![pattern_7_1](9250/previews/pattern_7_1.png) \| ![pattern_8](9250/previews/pattern_8.png) \| ![portrait_0](9250/previews/portrait_0.png) \| ![portrait_1](9250/previews/portrait_1.png) \| ![portrait_2](9250/previews/portrait_2.png) \| ![full_body_0](9250/previews/full_body_0.png) \| ![full_body_1](9250/previews/full_body_1.png) \| ![profile_0](9250/previews/profile_0.png) \| ![profile_1](9250/previews/profile_1.png) \| ![free_0](9250/previews/free_0.png) \| ![free_1](9250/previews/free_1.png) \| ![shorts](9250/previews/shorts.png) \| ![maid_0](9250/previews/maid_0.png) \| ![maid_1](9250/previews/maid_1.png) \| ![miko](9250/previews/miko.png) \| ![yukata](9250/previews/yukata.png) \| ![suit](9250/previews/suit.png) \| ![china](9250/previews/china.png) \| ![bikini_0](9250/previews/bikini_0.png) \| ![bikini_1](9250/previews/bikini_1.png) \| ![bikini_2](9250/previews/bikini_2.png) \| ![sit](9250/previews/sit.png) \| ![squat](9250/previews/squat.png) \| ![kneel](9250/previews/kneel.png) \| ![jump](9250/previews/jump.png) \| ![crossed_arms](9250/previews/crossed_arms.png) \| ![angry](9250/previews/angry.png) \| ![smile](9250/previews/smile.png) \| ![cry](9250/previews/cry.png) \| ![grin](9250/previews/grin.png) \| ![n_lie_0](9250/previews/n_lie_0.png) \| ![n_lie_1](9250/previews/n_lie_1.png) \| ![n_stand_0](9250/previews/n_stand_0.png) \| ![n_stand_1](9250/previews/n_stand_1.png) \| ![n_stand_2](9250/previews/n_stand_2.png) \| ![n_sex_0](9250/previews/n_sex_0.png) \| ![n_sex_1](9250/previews/n_sex_1.png) \| ## Anything Else? Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. ## All Steps We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * [Steps From 7750 to 10000](all/0.md) * [Steps From 5250 to 7500](all/1.md) * [Steps From 2750 to 5000](all/2.md) * [Steps From 250 to 2500](all/3.md)	{"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/okita_souji_alter_fgo"], "pipeline_tag": "text-to-image"}	text-to-image	CyberHarem/okita_souji_alter_fgo	[ "art", "not-for-all-audiences", "text-to-image", "dataset:CyberHarem/okita_souji_alter_fgo", "license:mit", "region:us" ]	2024-02-06T07:31:31+00:00	[]	[]	TAGS #art #not-for-all-audiences #text-to-image #dataset-CyberHarem/okita_souji_alter_fgo #license-mit #region-us	Lora of okita\_souji\_alter/沖田総司〔オルタ〕/冲田总司〔Alter〕 (Fate/Grand Order) ==================================================================== What Is This? ------------- This is the LoRA model of waifu okita\_souji\_alter/沖田総司〔オルタ〕/冲田总司〔Alter〕 (Fate/Grand Order). How Is It Trained? ------------------ * This model is trained with HCP-Diffusion. * The auto-training framework is maintained by DeepGHS Team. * The base model used for training is deepghs/animefull-latest. * Dataset used for training is the 'stage3-p480-800' in CyberHarem/okita\_souji\_alter\_fgo, which contains 1271 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 1, resolution is 720x720, clustering into 20 buckets. * Trained for 10000 steps, 40 checkpoints were saved and evaluated. * Trigger word is 'okita\_souji\_alter\_fgo'. * Pruned core tags for this waifu are 'dark\_skin, dark-skinned\_female, white\_hair, ahoge, bow, breasts, hair\_bow, hair\_between\_eyes, black\_bow, hair\_ornament, tassel, large\_breasts, long\_hair, bangs, very\_long\_hair, yellow\_eyes'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. How to Use It? -------------- ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 8250, you need to download '8250/okita\_souji\_alter\_fgo.pt' as the embedding and '8250/okita\_souji\_alter\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters. Which Step Should I Use? ------------------------ We selected 5 good steps for you to choose. The best one is step 8250. 1960 images (2.04 GiB) were generated for auto-testing. !Metrics Plot The base model used for generating preview images is Meina/MeinaMix\_V11. Here are the preview of the recommended steps: Anything Else? -------------- Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. All Steps --------- We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * Steps From 7750 to 10000 * Steps From 5250 to 7500 * Steps From 2750 to 5000 * Steps From 250 to 2500	[ "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 8250, you need to download '8250/okita\\_souji\\_alter\\_fgo.pt' as the embedding and '8250/okita\\_souji\\_alter\\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 8250.\n\n\n1960 images (2.04 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500" ]	[ "TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/okita_souji_alter_fgo #license-mit #region-us \n", "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 8250, you need to download '8250/okita\\_souji\\_alter\\_fgo.pt' as the embedding and '8250/okita\\_souji\\_alter\\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 8250.\n\n\n1960 images (2.04 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 7750 to 10000\n* Steps From 5250 to 7500\n* Steps From 2750 to 5000\n* Steps From 250 to 2500" ]	[ 48, 38, 483 ]	[ "passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/okita_souji_alter_fgo #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. 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null	null	stable-baselines3	# ppo Agent playing LunarLander-v2 This is a trained model of a ppo agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "ppo", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "230.97 +/- 32.20", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	Helaly6484/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-06T07:33:18+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# ppo Agent playing LunarLander-v2 This is a trained model of a ppo agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# ppo Agent playing LunarLander-v2\nThis is a trained model of a ppo agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# ppo Agent playing LunarLander-v2\nThis is a trained model of a ppo agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# ppo Agent playing LunarLander-v2\nThis is a trained model of a ppo agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.046836115419864655, 0.029148947447538376, -0.0046101463958621025, 0.0333307646214962, 0.07860215753316879, -0.03898412734270096, 0.10734876990318298, 0.06503576785326004, 0.09938327223062515, 0.026071077212691307, 0.12705640494823456, 0.06314948946237564, 0.022638585418462753, 0.26038658618927, 0.04753579944372177, -0.20013634860515594, 0.05630762502551079, -0.06510073691606522, 0.11960826069116592, 0.07872214913368225, -0.003378120716661215, -0.06956975162029266, 0.022916419431567192, -0.04493887722492218, 0.06570369005203247, 0.04432268068194389, -0.005315820220857859, -0.07050011307001114, 0.09956791251897812, -0.06057116389274597, 0.09341789782047272, 0.04487678036093712, 0.13019762933254242, -0.18420742452144623, 0.03289811313152313, 0.021116411313414574, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "beomi/polyglot-ko-12.8b-safetensors"}	null	yatsby/koalpaca_persona_chat	[ "peft", "arxiv:1910.09700", "base_model:beomi/polyglot-ko-12.8b-safetensors", "region:us" ]	2024-02-06T07:34:40+00:00	[ "1910.09700" ]	[]	TAGS #peft #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 39, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #arxiv-1910.09700 #base_model-beomi/polyglot-ko-12.8b-safetensors #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.10961789637804031, 0.21071495115756989, -0.0027299211360514164, 0.030867598950862885, 0.08404036611318588, 0.023725878447294235, 0.05362171679735184, 0.12906980514526367, -0.0072654057294130325, 0.10364974290132523, 0.07074826210737228, 0.1023382842540741, 0.11220181733369827, 0.22433406114578247, 0.004169307183474302, -0.19532260298728943, 0.027144387364387512, -0.08582145720720291, -0.001951515907421708, 0.12579995393753052, 0.14198173582553864, -0.09728114306926727, 0.08253170549869537, -0.012125578708946705, -0.00739028537645936, -0.03895310312509537, 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null	null	null	# Lora of Dioscuri Pollux (Fate/Grand Order) ## What Is This? This is the LoRA model of waifu Dioscuri Pollux (Fate/Grand Order). ## How Is It Trained? * This model is trained with [HCP-Diffusion](https://github.com/7eu7d7/HCP-Diffusion). * The [auto-training framework](https://github.com/deepghs/cyberharem) is maintained by [DeepGHS Team](https://huggingface.co/deepghs). * The base model used for training is [deepghs/animefull-latest](https://huggingface.co/deepghs/animefull-latest). * Dataset used for training is the `stage3-p480-800` in [CyberHarem/dioscuri_pollux_fgo](https://huggingface.co/datasets/CyberHarem/dioscuri_pollux_fgo), which contains 311 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 12, resolution is 720x720, clustering into 20 buckets. * Trained for 3120 steps, 40 checkpoints were saved and evaluated. * Trigger word is `dioscuri_pollux_fgo`. * Pruned core tags for this waifu are `blonde_hair, bangs, breasts, medium_hair, blue_eyes, small_breasts`. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. ## How to Use It? ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2886, you need to download [`2886/dioscuri_pollux_fgo.pt`](https://huggingface.co/CyberHarem/dioscuri_pollux_fgo/resolve/main/2886/dioscuri_pollux_fgo.pt) as the embedding and [`2886/dioscuri_pollux_fgo.safetensors`](https://huggingface.co/CyberHarem/dioscuri_pollux_fgo/resolve/main/2886/dioscuri_pollux_fgo.safetensors) for loading Lora. By using both files together, you can generate images for the desired characters. ## Which Step Should I Use? We selected 5 good steps for you to choose. The best one is step 2886. 1600 images (1.62 GiB) were generated for auto-testing. ![Metrics Plot](metrics_plot.png) The base model used for generating preview images is [Meina/MeinaMix_V11](https://huggingface.co/Meina/MeinaMix_V11). Here are the preview of the recommended steps: \| Step \| Epoch \| CCIP \| AI Corrupt \| Bikini Plus \| Score \| Download \| pattern_0 \| pattern_1 \| pattern_2 \| pattern_3_0 \| pattern_3_1 \| portrait_0 \| portrait_1 \| portrait_2 \| full_body_0 \| full_body_1 \| profile_0 \| profile_1 \| free_0 \| free_1 \| shorts \| maid_0 \| maid_1 \| miko \| yukata \| suit \| china \| bikini_0 \| bikini_1 \| bikini_2 \| sit \| squat \| kneel \| jump \| crossed_arms \| angry \| smile \| cry \| grin \| n_lie_0 \| n_lie_1 \| n_stand_0 \| n_stand_1 \| n_stand_2 \| n_sex_0 \| n_sex_1 \| \|-------:\|--------:\|:----------\|:-------------\|:--------------\|:----------\|:------------------------------------------------------------------------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:--------------------------------------------\|:--------------------------------------------\|:--------------------------------------------\|:----------------------------------------------\|:----------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:------------------------------------\|:--------------------------------\|:------------------------------------\|:--------------------------------\|:----------------------------------\|:----------------------------------------\|:----------------------------------------\|:----------------------------------------\|:------------------------------\|:----------------------------------\|:----------------------------------\|:--------------------------------\|:------------------------------------------------\|:----------------------------------\|:----------------------------------\|:------------------------------\|:--------------------------------\|:--------------------------------------\|:--------------------------------------\|:------------------------------------------\|:------------------------------------------\|:------------------------------------------\|:--------------------------------------\|:--------------------------------------\| \| 2886 \| 38 \| 0.883 \| 0.856 \| 0.814 \| 0.770 \| [Download](https://huggingface.co/CyberHarem/dioscuri_pollux_fgo/resolve/main/2886/dioscuri_pollux_fgo.zip) \| ![pattern_0](2886/previews/pattern_0.png) \| ![pattern_1](2886/previews/pattern_1.png) \| ![pattern_2](2886/previews/pattern_2.png) \| ![pattern_3_0](2886/previews/pattern_3_0.png) \| ![pattern_3_1](2886/previews/pattern_3_1.png) \| ![portrait_0](2886/previews/portrait_0.png) \| ![portrait_1](2886/previews/portrait_1.png) \| ![portrait_2](2886/previews/portrait_2.png) \| ![full_body_0](2886/previews/full_body_0.png) \| ![full_body_1](2886/previews/full_body_1.png) \| ![profile_0](2886/previews/profile_0.png) \| ![profile_1](2886/previews/profile_1.png) \| ![free_0](2886/previews/free_0.png) \| ![free_1](2886/previews/free_1.png) \| ![shorts](2886/previews/shorts.png) \| ![maid_0](2886/previews/maid_0.png) \| ![maid_1](2886/previews/maid_1.png) \| ![miko](2886/previews/miko.png) \| ![yukata](2886/previews/yukata.png) \| ![suit](2886/previews/suit.png) \| ![china](2886/previews/china.png) \| ![bikini_0](2886/previews/bikini_0.png) \| ![bikini_1](2886/previews/bikini_1.png) \| ![bikini_2](2886/previews/bikini_2.png) \| ![sit](2886/previews/sit.png) \| ![squat](2886/previews/squat.png) \| ![kneel](2886/previews/kneel.png) \| ![jump](2886/previews/jump.png) \| ![crossed_arms](2886/previews/crossed_arms.png) \| ![angry](2886/previews/angry.png) \| ![smile](2886/previews/smile.png) \| ![cry](2886/previews/cry.png) \| ![grin](2886/previews/grin.png) \| ![n_lie_0](2886/previews/n_lie_0.png) \| ![n_lie_1](2886/previews/n_lie_1.png) \| ![n_stand_0](2886/previews/n_stand_0.png) \| ![n_stand_1](2886/previews/n_stand_1.png) \| ![n_stand_2](2886/previews/n_stand_2.png) \| ![n_sex_0](2886/previews/n_sex_0.png) \| ![n_sex_1](2886/previews/n_sex_1.png) \| \| 1872 \| 25 \| 0.884 \| 0.815 \| 0.803 \| 0.748 \| [Download](https://huggingface.co/CyberHarem/dioscuri_pollux_fgo/resolve/main/1872/dioscuri_pollux_fgo.zip) \| ![pattern_0](1872/previews/pattern_0.png) \| ![pattern_1](1872/previews/pattern_1.png) \| ![pattern_2](1872/previews/pattern_2.png) \| ![pattern_3_0](1872/previews/pattern_3_0.png) \| ![pattern_3_1](1872/previews/pattern_3_1.png) \| ![portrait_0](1872/previews/portrait_0.png) \| ![portrait_1](1872/previews/portrait_1.png) \| ![portrait_2](1872/previews/portrait_2.png) \| ![full_body_0](1872/previews/full_body_0.png) \| ![full_body_1](1872/previews/full_body_1.png) \| ![profile_0](1872/previews/profile_0.png) \| ![profile_1](1872/previews/profile_1.png) \| ![free_0](1872/previews/free_0.png) \| ![free_1](1872/previews/free_1.png) \| ![shorts](1872/previews/shorts.png) \| ![maid_0](1872/previews/maid_0.png) \| ![maid_1](1872/previews/maid_1.png) \| ![miko](1872/previews/miko.png) \| ![yukata](1872/previews/yukata.png) \| ![suit](1872/previews/suit.png) \| ![china](1872/previews/china.png) \| ![bikini_0](1872/previews/bikini_0.png) \| ![bikini_1](1872/previews/bikini_1.png) \| ![bikini_2](1872/previews/bikini_2.png) \| ![sit](1872/previews/sit.png) \| ![squat](1872/previews/squat.png) \| ![kneel](1872/previews/kneel.png) \| ![jump](1872/previews/jump.png) \| ![crossed_arms](1872/previews/crossed_arms.png) \| ![angry](1872/previews/angry.png) \| ![smile](1872/previews/smile.png) \| ![cry](1872/previews/cry.png) \| ![grin](1872/previews/grin.png) \| ![n_lie_0](1872/previews/n_lie_0.png) \| ![n_lie_1](1872/previews/n_lie_1.png) \| ![n_stand_0](1872/previews/n_stand_0.png) \| ![n_stand_1](1872/previews/n_stand_1.png) \| ![n_stand_2](1872/previews/n_stand_2.png) \| ![n_sex_0](1872/previews/n_sex_0.png) \| ![n_sex_1](1872/previews/n_sex_1.png) \| \| 2496 \| 33 \| 0.846 \| 0.816 \| 0.814 \| 0.718 \| [Download](https://huggingface.co/CyberHarem/dioscuri_pollux_fgo/resolve/main/2496/dioscuri_pollux_fgo.zip) \| ![pattern_0](2496/previews/pattern_0.png) \| ![pattern_1](2496/previews/pattern_1.png) \| ![pattern_2](2496/previews/pattern_2.png) \| ![pattern_3_0](2496/previews/pattern_3_0.png) \| ![pattern_3_1](2496/previews/pattern_3_1.png) \| ![portrait_0](2496/previews/portrait_0.png) \| ![portrait_1](2496/previews/portrait_1.png) \| ![portrait_2](2496/previews/portrait_2.png) \| ![full_body_0](2496/previews/full_body_0.png) \| ![full_body_1](2496/previews/full_body_1.png) \| ![profile_0](2496/previews/profile_0.png) \| ![profile_1](2496/previews/profile_1.png) \| ![free_0](2496/previews/free_0.png) \| ![free_1](2496/previews/free_1.png) \| ![shorts](2496/previews/shorts.png) \| ![maid_0](2496/previews/maid_0.png) \| ![maid_1](2496/previews/maid_1.png) \| ![miko](2496/previews/miko.png) \| ![yukata](2496/previews/yukata.png) \| ![suit](2496/previews/suit.png) \| ![china](2496/previews/china.png) \| ![bikini_0](2496/previews/bikini_0.png) \| ![bikini_1](2496/previews/bikini_1.png) \| ![bikini_2](2496/previews/bikini_2.png) \| ![sit](2496/previews/sit.png) \| ![squat](2496/previews/squat.png) \| ![kneel](2496/previews/kneel.png) \| ![jump](2496/previews/jump.png) \| ![crossed_arms](2496/previews/crossed_arms.png) \| ![angry](2496/previews/angry.png) \| ![smile](2496/previews/smile.png) \| ![cry](2496/previews/cry.png) \| ![grin](2496/previews/grin.png) \| ![n_lie_0](2496/previews/n_lie_0.png) \| ![n_lie_1](2496/previews/n_lie_1.png) \| ![n_stand_0](2496/previews/n_stand_0.png) \| ![n_stand_1](2496/previews/n_stand_1.png) \| ![n_stand_2](2496/previews/n_stand_2.png) \| ![n_sex_0](2496/previews/n_sex_0.png) \| ![n_sex_1](2496/previews/n_sex_1.png) \| \| 3042 \| 40 \| 0.856 \| 0.861 \| 0.805 \| 0.717 \| [Download](https://huggingface.co/CyberHarem/dioscuri_pollux_fgo/resolve/main/3042/dioscuri_pollux_fgo.zip) \| ![pattern_0](3042/previews/pattern_0.png) \| ![pattern_1](3042/previews/pattern_1.png) \| ![pattern_2](3042/previews/pattern_2.png) \| ![pattern_3_0](3042/previews/pattern_3_0.png) \| ![pattern_3_1](3042/previews/pattern_3_1.png) \| ![portrait_0](3042/previews/portrait_0.png) \| ![portrait_1](3042/previews/portrait_1.png) \| ![portrait_2](3042/previews/portrait_2.png) \| ![full_body_0](3042/previews/full_body_0.png) \| ![full_body_1](3042/previews/full_body_1.png) \| ![profile_0](3042/previews/profile_0.png) \| ![profile_1](3042/previews/profile_1.png) \| ![free_0](3042/previews/free_0.png) \| ![free_1](3042/previews/free_1.png) \| ![shorts](3042/previews/shorts.png) \| ![maid_0](3042/previews/maid_0.png) \| ![maid_1](3042/previews/maid_1.png) \| ![miko](3042/previews/miko.png) \| ![yukata](3042/previews/yukata.png) \| ![suit](3042/previews/suit.png) \| ![china](3042/previews/china.png) \| ![bikini_0](3042/previews/bikini_0.png) \| ![bikini_1](3042/previews/bikini_1.png) \| ![bikini_2](3042/previews/bikini_2.png) \| ![sit](3042/previews/sit.png) \| ![squat](3042/previews/squat.png) \| ![kneel](3042/previews/kneel.png) \| ![jump](3042/previews/jump.png) \| ![crossed_arms](3042/previews/crossed_arms.png) \| ![angry](3042/previews/angry.png) \| ![smile](3042/previews/smile.png) \| ![cry](3042/previews/cry.png) \| ![grin](3042/previews/grin.png) \| ![n_lie_0](3042/previews/n_lie_0.png) \| ![n_lie_1](3042/previews/n_lie_1.png) \| ![n_stand_0](3042/previews/n_stand_0.png) \| ![n_stand_1](3042/previews/n_stand_1.png) \| ![n_stand_2](3042/previews/n_stand_2.png) \| ![n_sex_0](3042/previews/n_sex_0.png) \| ![n_sex_1](3042/previews/n_sex_1.png) \| \| 3120 \| 41 \| 0.838 \| 0.832 \| 0.810 \| 0.701 \| [Download](https://huggingface.co/CyberHarem/dioscuri_pollux_fgo/resolve/main/3120/dioscuri_pollux_fgo.zip) \| ![pattern_0](3120/previews/pattern_0.png) \| ![pattern_1](3120/previews/pattern_1.png) \| ![pattern_2](3120/previews/pattern_2.png) \| ![pattern_3_0](3120/previews/pattern_3_0.png) \| ![pattern_3_1](3120/previews/pattern_3_1.png) \| ![portrait_0](3120/previews/portrait_0.png) \| ![portrait_1](3120/previews/portrait_1.png) \| ![portrait_2](3120/previews/portrait_2.png) \| ![full_body_0](3120/previews/full_body_0.png) \| ![full_body_1](3120/previews/full_body_1.png) \| ![profile_0](3120/previews/profile_0.png) \| ![profile_1](3120/previews/profile_1.png) \| ![free_0](3120/previews/free_0.png) \| ![free_1](3120/previews/free_1.png) \| ![shorts](3120/previews/shorts.png) \| ![maid_0](3120/previews/maid_0.png) \| ![maid_1](3120/previews/maid_1.png) \| ![miko](3120/previews/miko.png) \| ![yukata](3120/previews/yukata.png) \| ![suit](3120/previews/suit.png) \| ![china](3120/previews/china.png) \| ![bikini_0](3120/previews/bikini_0.png) \| ![bikini_1](3120/previews/bikini_1.png) \| ![bikini_2](3120/previews/bikini_2.png) \| ![sit](3120/previews/sit.png) \| ![squat](3120/previews/squat.png) \| ![kneel](3120/previews/kneel.png) \| ![jump](3120/previews/jump.png) \| ![crossed_arms](3120/previews/crossed_arms.png) \| ![angry](3120/previews/angry.png) \| ![smile](3120/previews/smile.png) \| ![cry](3120/previews/cry.png) \| ![grin](3120/previews/grin.png) \| ![n_lie_0](3120/previews/n_lie_0.png) \| ![n_lie_1](3120/previews/n_lie_1.png) \| ![n_stand_0](3120/previews/n_stand_0.png) \| ![n_stand_1](3120/previews/n_stand_1.png) \| ![n_stand_2](3120/previews/n_stand_2.png) \| ![n_sex_0](3120/previews/n_sex_0.png) \| ![n_sex_1](3120/previews/n_sex_1.png) \| ## Anything Else? Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. ## All Steps We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * [Steps From 2418 to 3120](all/0.md) * [Steps From 1638 to 2340](all/1.md) * [Steps From 858 to 1560](all/2.md) * [Steps From 78 to 780](all/3.md)	{"license": "mit", "tags": ["art", "not-for-all-audiences"], "datasets": ["CyberHarem/dioscuri_pollux_fgo"], "pipeline_tag": "text-to-image"}	text-to-image	CyberHarem/dioscuri_pollux_fgo	[ "art", "not-for-all-audiences", "text-to-image", "dataset:CyberHarem/dioscuri_pollux_fgo", "license:mit", "region:us" ]	2024-02-06T07:36:38+00:00	[]	[]	TAGS #art #not-for-all-audiences #text-to-image #dataset-CyberHarem/dioscuri_pollux_fgo #license-mit #region-us	Lora of Dioscuri Pollux (Fate/Grand Order) ========================================== What Is This? ------------- This is the LoRA model of waifu Dioscuri Pollux (Fate/Grand Order). How Is It Trained? ------------------ * This model is trained with HCP-Diffusion. * The auto-training framework is maintained by DeepGHS Team. * The base model used for training is deepghs/animefull-latest. * Dataset used for training is the 'stage3-p480-800' in CyberHarem/dioscuri\_pollux\_fgo, which contains 311 images. * Batch size is 4, resolution is 720x720, clustering into 5 buckets. * Batch size for regularization dataset is 12, resolution is 720x720, clustering into 20 buckets. * Trained for 3120 steps, 40 checkpoints were saved and evaluated. * Trigger word is 'dioscuri\_pollux\_fgo'. * Pruned core tags for this waifu are 'blonde\_hair, bangs, breasts, medium\_hair, blue\_eyes, small\_breasts'. You can add them to the prompt when some features of waifu (e.g. hair color) are not stable. How to Use It? -------------- ### If You Are Using A1111 WebUI v1.7+ Just use it like the classic LoRA. The LoRA we provided are bundled with the embedding file. ### If You Are Using A1111 WebUI v1.6 or Lower After downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora. For example, if you want to use the model from step 2886, you need to download '2886/dioscuri\_pollux\_fgo.pt' as the embedding and '2886/dioscuri\_pollux\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters. Which Step Should I Use? ------------------------ We selected 5 good steps for you to choose. The best one is step 2886. 1600 images (1.62 GiB) were generated for auto-testing. !Metrics Plot The base model used for generating preview images is Meina/MeinaMix\_V11. Here are the preview of the recommended steps: Anything Else? -------------- Because the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret: 1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail. 2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits. 3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm. 4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters. 5. Individuals who finds the generated image content offensive to their values. All Steps --------- We uploaded the files in all steps. you can check the images, metrics and download them in the following links: * Steps From 2418 to 3120 * Steps From 1638 to 2340 * Steps From 858 to 1560 * Steps From 78 to 780	[ "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 2886, you need to download '2886/dioscuri\\_pollux\\_fgo.pt' as the embedding and '2886/dioscuri\\_pollux\\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 2886.\n\n\n1600 images (1.62 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2418 to 3120\n* Steps From 1638 to 2340\n* Steps From 858 to 1560\n* Steps From 78 to 780" ]	[ "TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/dioscuri_pollux_fgo #license-mit #region-us \n", "### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. The LoRA we provided are bundled with the embedding file.", "### If You Are Using A1111 WebUI v1.6 or Lower\n\n\nAfter downloading the pt and safetensors files for the specified step, you need to use them simultaneously. The pt file will be used as an embedding, while the safetensors file will be loaded for Lora.\n\n\nFor example, if you want to use the model from step 2886, you need to download '2886/dioscuri\\_pollux\\_fgo.pt' as the embedding and '2886/dioscuri\\_pollux\\_fgo.safetensors' for loading Lora. By using both files together, you can generate images for the desired characters.\n\n\nWhich Step Should I Use?\n------------------------\n\n\nWe selected 5 good steps for you to choose. The best one is step 2886.\n\n\n1600 images (1.62 GiB) were generated for auto-testing.\n\n\n!Metrics Plot\n\n\nThe base model used for generating preview images is Meina/MeinaMix\\_V11.\n\n\nHere are the preview of the recommended steps:\n\n\n\nAnything Else?\n--------------\n\n\nBecause the automation of LoRA training always annoys some people. So for the following groups, it is not recommended to use this model and we express regret:\n\n\n1. Individuals who cannot tolerate any deviations from the original character design, even in the slightest detail.\n2. Individuals who are facing the application scenarios with high demands for accuracy in recreating character outfits.\n3. Individuals who cannot accept the potential randomness in AI-generated images based on the Stable Diffusion algorithm.\n4. Individuals who are not comfortable with the fully automated process of training character models using LoRA, or those who believe that training character models must be done purely through manual operations to avoid disrespecting the characters.\n5. Individuals who finds the generated image content offensive to their values.\n\n\nAll Steps\n---------\n\n\nWe uploaded the files in all steps. you can check the images, metrics and download them in the following links:\n\n\n* Steps From 2418 to 3120\n* Steps From 1638 to 2340\n* Steps From 858 to 1560\n* Steps From 78 to 780" ]	[ 47, 38, 482 ]	[ "passage: TAGS\n#art #not-for-all-audiences #text-to-image #dataset-CyberHarem/dioscuri_pollux_fgo #license-mit #region-us \n### If You Are Using A1111 WebUI v1.7+\n\n\nJust use it like the classic LoRA. 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # speecht5_finetuned_voxpopuli_nl This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4559 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5165 \| 7.37 \| 1000 \| 0.4733 \| \| 0.4942 \| 14.73 \| 2000 \| 0.4604 \| \| 0.4867 \| 22.1 \| 3000 \| 0.4569 \| \| 0.4882 \| 29.47 \| 4000 \| 0.4559 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.2 - Datasets 2.14.7 - Tokenizers 0.15.0	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "speecht5_finetuned_voxpopuli_nl", "results": []}]}	text-to-audio	magus4450/speecht5_finetuned_voxpopuli_nl	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	2024-02-06T07:38:54+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us	speecht5\_finetuned\_voxpopuli\_nl ================================== This model is a fine-tuned version of microsoft/speecht5\_tts on the facebook/voxpopuli dataset. It achieves the following results on the evaluation set: * Loss: 0.4559 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 4 * eval\_batch\_size: 2 * seed: 42 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 4000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.2 * Datasets 2.14.7 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.14.7\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.2\n* Datasets 2.14.7\n* Tokenizers 0.15.0" ]	[ 74, 158, 4, 32 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 2\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### 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null	null	null	<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-0.5B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/second-state/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-0.5B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-0.5B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Qwen1.5-0.5B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q2_K.gguf) \| Q2_K \| 2 \| 298 MB\| smallest, significant quality loss - not recommended for most purposes \| \| [Qwen1.5-0.5B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 364 MB\| small, substantial quality loss \| \| [Qwen1.5-0.5B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 350 MB\| very small, high quality loss \| \| [Qwen1.5-0.5B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 333 MB\| very small, high quality loss \| \| [Qwen1.5-0.5B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q4_0.gguf) \| Q4_0 \| 4 \| 395 MB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Qwen1.5-0.5B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 407 MB\| medium, balanced quality - recommended \| \| [Qwen1.5-0.5B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 397 MB\| small, greater quality loss \| \| [Qwen1.5-0.5B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q5_0.gguf) \| Q5_0 \| 5 \| 453 MB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Qwen1.5-0.5B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 459 MB\| large, very low quality loss - recommended \| \| [Qwen1.5-0.5B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 453 MB\| large, low quality loss - recommended \| \| [Qwen1.5-0.5B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q6_K.gguf) \| Q6_K \| 6 \| 515 MB\| very large, extremely low quality loss \| \| [Qwen1.5-0.5B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-0.5B-Chat-GGUF/blob/main/Qwen1.5-0.5B-Chat-Q8_0.gguf) \| Q8_0 \| 8 \| 665 MB\| very large, extremely low quality loss - not recommended \|	{"language": ["en"], "license": "other", "tags": ["chat"], "model_name": "Qwen1.5 0.5B Chat", "base_model": "Qwen/Qwen1.5-0.5B-Chat", "license_name": "tongyi-qianwen-research", "license_link": "https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE", "model_creator": "Qwen", "quantized_by": "Second State Inc.", "pipeline_tag": "text-generation"}	text-generation	second-state/Qwen1.5-0.5B-Chat-GGUF	[ "gguf", "chat", "text-generation", "en", "base_model:Qwen/Qwen1.5-0.5B-Chat", "license:other", "region:us" ]	2024-02-06T07:38:55+00:00	[]	[ "en" ]	TAGS #gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-0.5B-Chat #license-other #region-us	![](URL style=) --- Qwen1.5-0.5B-Chat-GGUF ====================== Original Model -------------- Qwen/Qwen1.5-0.5B-Chat Run with LlamaEdge ------------------ * LlamaEdge version: v0.2.15 and above * Prompt template + Prompt type: 'chatml' + Prompt string * Run as LlamaEdge service * Run as LlamaEdge command app Quantized GGUF Models ---------------------	[]	[ "TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-0.5B-Chat #license-other #region-us \n" ]	[ 39 ]	[ "passage: TAGS\n#gguf #chat #text-generation #en #base_model-Qwen/Qwen1.5-0.5B-Chat #license-other #region-us \n" ]	[ -0.012140069156885147, -0.008528387174010277, -0.003774038515985012, 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null	null	null	## Exporting LoRas for use in other tools Documentation on how to use Eden concepts in Automatic1111 or ComfyUI is here: https://docs.eden.art/docs/guides/concepts/#exporting-loras-for-use-in-other-tools	{}	null	eden-art/Elvara	[ "region:us" ]	2024-02-06T07:40:44+00:00	[]	[]	TAGS #region-us	## Exporting LoRas for use in other tools Documentation on how to use Eden concepts in Automatic1111 or ComfyUI is here: URL	[ "## Exporting LoRas for use in other tools\nDocumentation on how to use Eden concepts in Automatic1111 or ComfyUI is here:\nURL" ]	[ "TAGS\n#region-us \n", "## Exporting LoRas for use in other tools\nDocumentation on how to use Eden concepts in Automatic1111 or ComfyUI is here:\nURL" ]	[ 6, 31 ]	[ "passage: TAGS\n#region-us \n## Exporting LoRas for use in other tools\nDocumentation on how to use Eden concepts in Automatic1111 or ComfyUI is here:\nURL" ]	[ -0.04433843493461609, 0.045720089226961136, -0.00047354400157928467, 0.030475227162241936, 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null	null	transformers	# Definition [phi-2] for [P]ersonal [I]dentifiable [I]nformation with [B]anking [B]anking [I]nsurance Dataset # How to use model ## Load model and tokenizer ``` import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer torch.set_default_device("cuda") model_name = "dcipheranalytics/phi-2-pii-bbi" quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4", ) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", # torch_dtype="auto", torch_dtype=torch.bfloat16, trust_remote_code=True, quantization_config=quantization_config, ) tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) ``` ## Call generate method ``` def generate(msg: str, max_new_tokens = 300, temperature=0.3): chat_template = "<\|im_start\|>user\n{msg}<\|im_end\|><\|im_start\|>assistant\n" prompt = chat_template.format(msg=msg) with torch.no_grad(): token_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt") output_ids = model.generate( token_ids.to(model.device), max_new_tokens=max_new_tokens, do_sample=True, temperature=temperature, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, ) output = tokenizer.decode(output_ids[0][token_ids.size(1):-1]).strip() return output instruction_template = "List the personally identifiable information in the given text below.\nText:########\n{text}\n########" text_with_pii = "My passport number is 123456789." generate(instruction_template.format(text=text_with_pii)) ``` ## Batch predictions ``` from transformers import TextGenerationPipeline def get_prompt(text): instruction_template = "List the personally identifiable information in the given text below.\nText:########\n{text}\n########" msg = instruction_template.format(text=text) chat_template = "<\|im_start\|>user\n{msg}<\|im_end\|><\|im_start\|>assistant\n" prompt = chat_template.format(msg=msg) return prompt generator = TextGenerationPipeline( model=model, tokenizer=tokenizer, max_new_tokens=300, do_sample=True, temperature=0.3, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, ) texts = ["My passport number is 123456789.", "My name is John Smith.", ] prompts = list(map(get_prompt, texts)) outputs = generator(prompts, return_full_text=False, batch_size=2) ``` # Train Data GPT4 generated customer service conversations. 1. 100 unique banking topics, 8 examples per each, 2. New 100 banking topics, 4 examples per each, 3. 100 insurance topics, 4 examples per each. # Evaluation Results ## Average ``` precision 0.836223 recall 0.781132 f1 0.801837 ``` ## Per topic: ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63ea400bb1d9c4ef71ebb962/wUfwR-dmmyxF4pCYoebCX.png) ## On TAB test split: ``` precision 0.506118 recall 0.350976 f1 0.391614 ```	{"language": ["en"]}	text-generation	dcipheranalytics/phi-2-pii-bbi	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T07:42:38+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #phi #text-generation #custom_code #en #autotrain_compatible #endpoints_compatible #region-us	# Definition [phi-2] for [P]ersonal [I]dentifiable [I]nformation with [B]anking [B]anking [I]nsurance Dataset # How to use model ## Load model and tokenizer ## Call generate method ## Batch predictions # Train Data GPT4 generated customer service conversations. 1. 100 unique banking topics, 8 examples per each, 2. New 100 banking topics, 4 examples per each, 3. 100 insurance topics, 4 examples per each. # Evaluation Results ## Average ## Per topic: !image/png ## On TAB test split:	[ "# Definition\n\n[phi-2] for [P]ersonal [I]dentifiable [I]nformation with [B]anking [B]anking [I]nsurance Dataset", "## Call generate method", "## Batch predictions", "# Train Data\n\nGPT4 generated customer service conversations. \n1. 100 unique banking topics, 8 examples per each,\n2. New 100 banking topics, 4 examples per each,\n3. 100 insurance topics, 4 examples per each.", "# Evaluation Results", "## Average", "## Per topic:\n!image/png", "## On TAB test split:" ]	[ "TAGS\n#transformers #safetensors #phi #text-generation #custom_code #en #autotrain_compatible #endpoints_compatible #region-us \n", "# Definition\n\n[phi-2] for [P]ersonal [I]dentifiable [I]nformation with [B]anking [B]anking [I]nsurance Dataset", "## Call generate method", "## Batch predictions", "# Train Data\n\nGPT4 generated customer service conversations. \n1. 100 unique banking topics, 8 examples per each,\n2. New 100 banking topics, 4 examples per each,\n3. 100 insurance topics, 4 examples per each.", "# Evaluation Results", "## Average", "## Per topic:\n!image/png", "## On TAB test split:" ]	[ 44, 43, 4, 5, 53, 4, 3, 8, 6 ]	[ "passage: TAGS\n#transformers #safetensors #phi #text-generation #custom_code #en #autotrain_compatible #endpoints_compatible #region-us \n# Definition\n\n[phi-2] for [P]ersonal [I]dentifiable [I]nformation with [B]anking [B]anking [I]nsurance Dataset## Call generate method## Batch predictions# Train Data\n\nGPT4 generated customer service conversations. \n1. 100 unique banking topics, 8 examples per each,\n2. New 100 banking topics, 4 examples per each,\n3. 100 insurance topics, 4 examples per each.# Evaluation Results## Average## Per topic:\n!image/png## On TAB test split:" ]	[ -0.12731735408306122, -0.03894650191068649, -0.0030404776334762573, 0.020259719341993332, 0.14718995988368988, -0.02025759220123291, 0.1381717324256897, 0.07237771898508072, 0.0015043173916637897, 0.08610434830188751, 0.1060556173324585, 0.1206495612859726, 0.03225032985210419, 0.30965906381607056, -0.06942097842693329, -0.16741128265857697, 0.08213035017251968, 0.0026240795850753784, 0.1425410360097885, 0.13246549665927887, 0.12342750281095505, -0.0989311933517456, 0.07649843394756317, -0.03149803727865219, -0.1433057337999344, -0.023530008271336555, 0.045491017401218414, -0.12261955440044403, 0.13201795518398285, 0.024609224870800972, 0.11076324433088303, -0.007603875827044249, 0.020476100966334343, -0.16706565022468567, 0.016470326110720634, -0.004821428097784519, -0.02532896399497986, 0.02578120492398739, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # emotion_classification This model is a fine-tuned version of [dennisjooo/emotion_classification](https://huggingface.co/dennisjooo/emotion_classification) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.7891 - Accuracy: 0.7575 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.7123 \| 1.0 \| 25 \| 0.8681 \| 0.735 \| \| 0.6349 \| 2.0 \| 50 \| 0.8721 \| 0.73 \| \| 0.6354 \| 3.0 \| 75 \| 0.8732 \| 0.725 \| \| 0.6189 \| 4.0 \| 100 \| 0.8406 \| 0.735 \| \| 0.6364 \| 5.0 \| 125 \| 0.8456 \| 0.74 \| \| 0.5833 \| 6.0 \| 150 \| 0.8503 \| 0.725 \| \| 0.5384 \| 7.0 \| 175 \| 0.8023 \| 0.755 \| \| 0.5297 \| 8.0 \| 200 \| 0.8002 \| 0.7525 \| \| 0.5487 \| 9.0 \| 225 \| 0.8253 \| 0.745 \| \| 0.5068 \| 10.0 \| 250 \| 0.7891 \| 0.7575 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "dennisjooo/emotion_classification", "model-index": [{"name": "emotion_classification", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.7575, "name": "Accuracy"}]}]}]}	image-classification	mhdiqbalpradipta/emotion_classification	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:dennisjooo/emotion_classification", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T07:46:12+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-dennisjooo/emotion_classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	emotion\_classification ======================= This model is a fine-tuned version of dennisjooo/emotion\_classification on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.7891 * Accuracy: 0.7575 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine\_with\_restarts * num\_epochs: 10 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-dennisjooo/emotion_classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 82, 122, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vit #image-classification #generated_from_trainer #dataset-imagefolder #base_model-dennisjooo/emotion_classification #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\\_with\\_restarts\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.1424366533756256, 0.16143907606601715, -0.002839229768142104, 0.1075059175491333, 0.1452186107635498, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # OndeviceAI-base This model is a fine-tuned version of [paust/pko-t5-base](https://huggingface.co/paust/pko-t5-base) on the None dataset. ## How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM from typing import List tokenizer = AutoTokenizer.from_pretrained("yeye776/OndeviceAI-base") model = AutoModelForSeq2SeqLM.from_pretrained("yeye776/OndeviceAI-base") prompt = "분류 및 인식해줘 :" def prepare_input(question: str): inputs = f"{prompt} {question}" input_ids = tokenizer(inputs, max_length=700, return_tensors="pt").input_ids return input_ids def inference(question: str) -> str: input_data = prepare_input(question=question) input_data = input_data.to(model.device) outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=1024) result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True) return result inference("안방 조명 켜줘") ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0007 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "cc-by-4.0", "tags": ["generated_from_trainer"], "base_model": "paust/pko-t5-base", "model-index": [{"name": "OndeviceAI-base", "results": []}]}	text2text-generation	yeye776/OndeviceAI-base	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:paust/pko-t5-base", "license:cc-by-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T08:03:28+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# OndeviceAI-base This model is a fine-tuned version of paust/pko-t5-base on the None dataset. ## How to use ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0007 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.06 - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# OndeviceAI-base\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.", "## How to use", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# OndeviceAI-base\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.", "## How to use", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 10", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 83, 33, 4, 129, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-paust/pko-t5-base #license-cc-by-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# OndeviceAI-base\n\nThis model is a fine-tuned version of paust/pko-t5-base on the None dataset.## How to use### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0007\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 32\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.06\n- num_epochs: 10### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.11100435256958008, 0.12467678636312485, -0.003916221205145121, 0.09340550005435944, 0.10593501478433609, 0.04339111968874931, 0.15257678925991058, 0.13470271229743958, -0.0571369007229805, 0.11118064820766449, 0.09331067651510239, 0.05356017127633095, 0.06066760793328285, 0.15487125515937805, -0.04272353649139404, -0.19019551575183868, 0.025309376418590546, -0.04900951683521271, -0.031025469303131104, 0.10300090909004211, 0.08301004767417908, -0.09864816069602966, 0.06926967203617096, -0.00708976574242115, -0.1368670016527176, -0.0441756397485733, -0.02951812371611595, -0.04573310911655426, 0.09049572050571442, 0.005087961442768574, 0.1185031458735466, 0.04108961299061775, 0.1082935631275177, -0.1860588937997818, 0.0026685548946261406, 0.07819616794586182, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-cased-lora-1.58M-snli-model3 This model is a fine-tuned version of [bert-large-cased](https://huggingface.co/bert-large-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8051 - Accuracy: 0.6975 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 256 - eval_batch_size: 256 - seed: 74 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.5037 \| 1.0 \| 2146 \| 0.4157 \| 0.8407 \| \| 0.4587 \| 2.0 \| 4292 \| 0.3823 \| 0.8574 \| \| 0.446 \| 3.0 \| 6438 \| 0.3734 \| 0.8612 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "bert-large-cased", "model-index": [{"name": "bert-large-cased-lora-1.58M-snli-model3", "results": []}]}	text-classification	varun-v-rao/bert-large-cased-lora-1.58M-snli-model3	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-large-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T08:04:36+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	bert-large-cased-lora-1.58M-snli-model3 ======================================= This model is a fine-tuned version of bert-large-cased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.8051 * Accuracy: 0.6975 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 256 * eval\_batch\_size: 256 * seed: 74 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.1+cu121 * Datasets 2.15.0 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 74\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 74\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ 68, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-large-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 256\n* eval\\_batch\\_size: 256\n* seed: 74\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.1+cu121\n* Datasets 2.15.0\n* Tokenizers 0.15.0" ]	[ -0.08567966520786285, 0.09621097147464752, -0.0021669145207852125, 0.1123308539390564, 0.14731737971305847, 0.0235870610922575, 0.1576298177242279, 0.10887430608272552, -0.07177284359931946, 0.04974283277988434, 0.12772446870803833, 0.125077486038208, 0.012325609102845192, 0.12008026242256165, -0.0652211457490921, -0.22886139154434204, 0.0040700482204556465, 0.03652415797114372, -0.06846872717142105, 0.11444934457540512, 0.09119358658790588, 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null	null	transformers	# merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) * [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) ### Configuration The following YAML configuration was used to produce this model: ```yaml merge_method: slerp base_model: mistralai/Mistral-7B-Instruct-v0.2 slices: - sources: - model: mistralai/Mistral-7B-Instruct-v0.2 layer_range: [0, 32] - model: teknium/OpenHermes-2.5-Mistral-7B layer_range: [0, 32] parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors dtype: bfloat16 ```	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["teknium/OpenHermes-2.5-Mistral-7B", "mistralai/Mistral-7B-Instruct-v0.2"]}	text-generation	chanwit/flux-7b-base-stage-00	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "conversational", "base_model:teknium/OpenHermes-2.5-Mistral-7B", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T08:04:40+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-teknium/OpenHermes-2.5-Mistral-7B #base_model-mistralai/Mistral-7B-Instruct-v0.2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# merge This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * teknium/OpenHermes-2.5-Mistral-7B * mistralai/Mistral-7B-Instruct-v0.2 ### Configuration The following YAML configuration was used to produce this model:	[ "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* teknium/OpenHermes-2.5-Mistral-7B\n* mistralai/Mistral-7B-Instruct-v0.2", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-teknium/OpenHermes-2.5-Mistral-7B #base_model-mistralai/Mistral-7B-Instruct-v0.2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# merge\n\nThis is a merge of pre-trained language models created using mergekit.", "## Merge Details", "### Merge Method\n\nThis model was merged using the SLERP merge method.", "### Models Merged\n\nThe following models were included in the merge:\n* teknium/OpenHermes-2.5-Mistral-7B\n* mistralai/Mistral-7B-Instruct-v0.2", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ 95, 18, 4, 18, 44, 17 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #base_model-teknium/OpenHermes-2.5-Mistral-7B #base_model-mistralai/Mistral-7B-Instruct-v0.2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# merge\n\nThis is a merge of pre-trained language models created using mergekit.## Merge Details### Merge Method\n\nThis model was merged using the SLERP merge method.### Models Merged\n\nThe following models were included in the merge:\n* teknium/OpenHermes-2.5-Mistral-7B\n* mistralai/Mistral-7B-Instruct-v0.2### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ -0.09678915143013, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # aragpt2-base-saadeh-full2 This model is a fine-tuned version of [aubmindlab/aragpt2-base](https://huggingface.co/aubmindlab/aragpt2-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.9358 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 266 \| 4.2973 \| \| 5.5626 \| 2.0 \| 532 \| 4.0108 \| \| 5.5626 \| 3.0 \| 798 \| 3.9358 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "base_model": "aubmindlab/aragpt2-base", "model-index": [{"name": "aragpt2-base-saadeh-full2", "results": []}]}	text-generation	ammarzaarour/aragpt2-base-saadeh-full2	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:aubmindlab/aragpt2-base", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T08:05:26+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-aubmindlab/aragpt2-base #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	aragpt2-base-saadeh-full2 ========================= This model is a fine-tuned version of aubmindlab/aragpt2-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 3.9358 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 3.0 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-aubmindlab/aragpt2-base #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 74, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-aubmindlab/aragpt2-base #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3.0### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.09904191642999649, 0.07089155912399292, -0.0025037259329110384, 0.09765670448541641, 0.13949760794639587, 0.012866944074630737, 0.16406048834323883, 0.11347787827253342, -0.06742548197507858, 0.06454664468765259, 0.14321795105934143, 0.10407330095767975, 0.027088608592748642, 0.14990778267383575, -0.07258570939302444, -0.22652459144592285, 0.013288456946611404, 0.030081771314144135, -0.04652031138539314, 0.11907365173101425, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "pipeline_tag": "conversational"}	text-generation	apatidar0/chat_style_phi-2	[ "transformers", "safetensors", "phi", "text-generation", "conversational", "custom_code", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "region:us" ]	2024-02-06T08:05:59+00:00	[ "1910.09700" ]	[ "en" ]	TAGS #transformers #safetensors #phi #text-generation #conversational #custom_code #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #phi #text-generation #conversational #custom_code #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 68, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #phi #text-generation #conversational #custom_code #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05304252728819847, 0.21067693829536438, -0.005285065621137619, 0.015017993748188019, 0.09488703310489655, 0.00841852743178606, 0.06399186700582504, 0.11650046706199646, -0.056802961975336075, 0.1257191300392151, 0.044529084116220474, 0.11682984977960587, 0.1185559555888176, 0.15182296931743622, -0.009572125971317291, -0.21556279063224792, 0.049783479422330856, -0.09544818103313446, -0.010143846273422241, 0.12479330599308014, 0.15112926065921783, -0.10353261977434158, 0.06991244852542877, -0.02506031095981598, -0.0291392020881176, -0.035314299166202545, -0.054662950336933136, -0.046247292309999466, 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null	null	transformers	Thanks to @Epiculous for the dope model/ help with llm backends and support overall. Id like to also thank @kalomaze for the dope sampler additions to ST. @SanjiWatsuki Thank you very much for the help, and the model! ST users can find the TextGenPreset in the folder labeled so. ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642265bc01c62c1e4102dc36/9obNSalcJqCilQwr_4ssM.jpeg) Quants:Thank you @bartowski, @jeiku, @konz00. https://huggingface.co/bartowski/Kunocchini-exl2 https://huggingface.co/jeiku/Konocchini-7B_GGUF https://huggingface.co/konz00/Kunocchini-7b-GGUF The following models were included in the merge: * [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B) * [Epiculous/Fett-uccine-7B](https://huggingface.co/Epiculous/Fett-uccine-7B) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: SanjiWatsuki/Kunoichi-DPO-v2-7B layer_range: [0, 32] - model: Epiculous/Fett-uccine-7B layer_range: [0, 32] merge_method: slerp base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```	{"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral"], "base_model": ["SanjiWatsuki/Kunoichi-DPO-v2-7B", "Epiculous/Fett-uccine-7B"]}	text-generation	Test157t/Kunocchini-7b	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "alpaca", "base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B", "base_model:Epiculous/Fett-uccine-7B", "license:other", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T08:08:29+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Thanks to @Epiculous for the dope model/ help with llm backends and support overall. Id like to also thank @kalomaze for the dope sampler additions to ST. @SanjiWatsuki Thank you very much for the help, and the model! ST users can find the TextGenPreset in the folder labeled so. !image/jpeg Quants:Thank you @bartowski, @jeiku, @konz00. URL URL URL The following models were included in the merge: * SanjiWatsuki/Kunoichi-DPO-v2-7B * Epiculous/Fett-uccine-7B ### Configuration The following YAML configuration was used to produce this model:	[ "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ 101, 17 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #alpaca #base_model-SanjiWatsuki/Kunoichi-DPO-v2-7B #base_model-Epiculous/Fett-uccine-7B #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Configuration\n\nThe following YAML configuration was used to produce this model:" ]	[ -0.04827842116355896, -0.02711334638297558, -0.0018630074337124825, 0.01664290390908718, 0.11920790374279022, 0.03723376244306564, 0.21972453594207764, 0.06883828341960907, 0.007329242303967476, -0.00759770767763257, 0.09230688214302063, 0.08492182940244675, 0.047438375651836395, 0.13973461091518402, -0.03369012475013733, -0.22017674148082733, 0.07378402352333069, 0.025632141157984734, -0.07307296246290207, 0.07651963829994202, 0.12004659324884415, -0.04043719545006752, 0.1106174886226654, 0.013773120939731598, -0.17258219420909882, 0.05627387389540672, -0.05822443589568138, -0.042880889028310776, 0.0683760717511177, 0.12694860994815826, 0.09840621799230576, 0.10027120262384415, 0.040738143026828766, -0.12937432527542114, 0.035205185413360596, -0.028859740123152733, -0.06325878202915192, 0.010682976804673672, 0.0640692189335823, -0.008216544054448605, 0.07740597426891327, 0.036575157195329666, -0.054907478392124176, -0.007223848253488541, 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null	null	transformers	# yuj-v1 The yuj-v1 model is a blend of advanced models strategically crafted to enhance Hindi Language Models (LLMs) effectively and democratically. Its primary goals include catalyzing the development of Hindi and its communities, making significant contributions to linguistic knowledge. The term "yuj," from Sanskrit, signifies fundamental unity, highlighting the integration of sophisticated technologies to improve the language experience for users in the Hindi-speaking community. Official GGUF version: [shuvom/yuj-v1-GGUF](https://huggingface.co/shuvom/yuj-v1-GGUF) Below are the model which are leverage to build this yuj-v1: * [ai4bharat/Airavata](https://huggingface.co/ai4bharat/Airavata) * [BhabhaAI/Gajendra-v0.1](https://huggingface.co/BhabhaAI/Gajendra-v0.1) ## 🧩 Configuration ```yaml models: - model: sarvamai/OpenHathi-7B-Hi-v0.1-Base # no parameters necessary for base model - model: ai4bharat/Airavata parameters: density: 0.5 weight: 0.5 - model: BhabhaAI/Gajendra-v0.1 parameters: density: 0.5 weight: 0.3 merge_method: ties base_model: sarvamai/OpenHathi-7B-Hi-v0.1-Base parameters: normalize: true dtype: float16 ``` ## 💻 Usage First, you need to install some of below packages: 1. Bits and bytes ```python !pip install bitsandbytes ``` 2. Accelerate (to install the latest version) ```python !pip install git+https://github.com/huggingface/accelerate.git ``` 3. Usage ```python # Usage import torch # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM # load the model in 4-bit quantization tokenizer = AutoTokenizer.from_pretrained("shuvom/yuj-v1") model = AutoModelForCausalLM.from_pretrained("shuvom/yuj-v1",torch_dtype=torch.bfloat16,load_in_4bit=True) prompt = "युज शीर्ष द्विभाषी मॉडल में से एक है" inputs = tokenizer(prompt, return_tensors="pt") # Generate generate_ids = model.generate(inputs.input_ids, max_length=65) tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] ``` 4. Output ```python युज शीर्ष द्विभाषी मॉडल में से एक है। यह एक उत्पादक मॉडल है जो एक साथ एक ट्रांसफॉर्मर और एक आत्म-ध्यान तंत्रिका नेटवर्क को जोड़ता है। यह एक ट्रांसफॉर्मर वास्तुकला का उपयोग करता है जो एक ट्रांसफॉर्मर मॉडल की तुलना में बहुत अधिक जटिल है। ```	{"license": "apache-2.0", "tags": ["merge", "hindi", "english", "Llama2", "ai4bharat/Airavata", "BhabhaAI/Gajendra-v0.1"]}	text-generation	shuvom/yuj-v1	[ "transformers", "safetensors", "llama", "text-generation", "merge", "hindi", "english", "Llama2", "ai4bharat/Airavata", "BhabhaAI/Gajendra-v0.1", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T08:11:57+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #hindi #english #Llama2 #ai4bharat/Airavata #BhabhaAI/Gajendra-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# yuj-v1 The yuj-v1 model is a blend of advanced models strategically crafted to enhance Hindi Language Models (LLMs) effectively and democratically. Its primary goals include catalyzing the development of Hindi and its communities, making significant contributions to linguistic knowledge. The term "yuj," from Sanskrit, signifies fundamental unity, highlighting the integration of sophisticated technologies to improve the language experience for users in the Hindi-speaking community. Official GGUF version: shuvom/yuj-v1-GGUF Below are the model which are leverage to build this yuj-v1: * ai4bharat/Airavata * BhabhaAI/Gajendra-v0.1 ## Configuration ## Usage First, you need to install some of below packages: 1. Bits and bytes 2. Accelerate (to install the latest version) 3. Usage 4. Output	[ "# yuj-v1\n\nThe yuj-v1 model is a blend of advanced models strategically crafted to enhance Hindi Language Models (LLMs) effectively and democratically. Its primary goals include catalyzing the development of Hindi and its communities, making significant contributions to linguistic knowledge. The term \"yuj,\" from Sanskrit, signifies fundamental unity, highlighting the integration of sophisticated technologies to improve the language experience for users in the Hindi-speaking community.\n\nOfficial GGUF version: shuvom/yuj-v1-GGUF\n\nBelow are the model which are leverage to build this yuj-v1:\n* ai4bharat/Airavata\n* BhabhaAI/Gajendra-v0.1", "## Configuration", "## Usage\n\nFirst, you need to install some of below packages:\n\n1. Bits and bytes\n\n2. Accelerate (to install the latest version)\n\n3. Usage\n\n4. Output" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #hindi #english #Llama2 #ai4bharat/Airavata #BhabhaAI/Gajendra-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# yuj-v1\n\nThe yuj-v1 model is a blend of advanced models strategically crafted to enhance Hindi Language Models (LLMs) effectively and democratically. Its primary goals include catalyzing the development of Hindi and its communities, making significant contributions to linguistic knowledge. The term \"yuj,\" from Sanskrit, signifies fundamental unity, highlighting the integration of sophisticated technologies to improve the language experience for users in the Hindi-speaking community.\n\nOfficial GGUF version: shuvom/yuj-v1-GGUF\n\nBelow are the model which are leverage to build this yuj-v1:\n* ai4bharat/Airavata\n* BhabhaAI/Gajendra-v0.1", "## Configuration", "## Usage\n\nFirst, you need to install some of below packages:\n\n1. Bits and bytes\n\n2. Accelerate (to install the latest version)\n\n3. Usage\n\n4. Output" ]	[ 88, 161, 4, 38 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #hindi #english #Llama2 #ai4bharat/Airavata #BhabhaAI/Gajendra-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# yuj-v1\n\nThe yuj-v1 model is a blend of advanced models strategically crafted to enhance Hindi Language Models (LLMs) effectively and democratically. Its primary goals include catalyzing the development of Hindi and its communities, making significant contributions to linguistic knowledge. The term \"yuj,\" from Sanskrit, signifies fundamental unity, highlighting the integration of sophisticated technologies to improve the language experience for users in the Hindi-speaking community.\n\nOfficial GGUF version: shuvom/yuj-v1-GGUF\n\nBelow are the model which are leverage to build this yuj-v1:\n* ai4bharat/Airavata\n* BhabhaAI/Gajendra-v0.1## Configuration## Usage\n\nFirst, you need to install some of below packages:\n\n1. Bits and bytes\n\n2. Accelerate (to install the latest version)\n\n3. Usage\n\n4. Output" ]	[ -0.05991892144083977, -0.013398412615060806, -0.0013414706336334348, 0.020102599635720253, 0.05986668914556503, 0.02006431855261326, 0.02989206649363041, 0.11537489295005798, -0.04559772461652756, 0.033357422798871994, 0.09335760027170181, 0.03814932331442833, 0.07868017256259918, 0.21958912909030914, 0.024861659854650497, -0.16082246601581573, 0.040586866438388824, -0.029023000970482826, -0.08284232765436172, 0.11853723227977753, 0.11632195115089417, -0.030726449564099312, 0.0312567800283432, 0.0037807892076671124, -0.13886232674121857, -0.046683646738529205, -0.03674580156803131, -0.0293875802308321, 0.04296555742621422, 0.027549609541893005, 0.043341461569070816, 0.028498586267232895, -0.00021594441204797477, -0.19905206561088562, 0.02502557262778282, -0.009648817591369152, -0.03272077813744545, 0.005967752076685429, -0.0007182893459685147, 0.06842908263206482, 0.19877664744853973, -0.07229312509298325, -0.06086399406194687, 0.07598876953125, -0.07585897296667099, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # whisper-tiny-ft-verbatim-cy This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7686 - Wer: 53.9846 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.9656 \| 1.41 \| 1000 \| 0.9762 \| 67.1825 \| \| 0.7198 \| 2.83 \| 2000 \| 0.8223 \| 59.9990 \| \| 0.5869 \| 4.24 \| 3000 \| 0.7813 \| 54.7898 \| \| 0.5413 \| 5.66 \| 4000 \| 0.7686 \| 53.9846 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "whisper-tiny-ft-verbatim-cy", "results": []}]}	automatic-speech-recognition	DewiBrynJones/whisper-tiny-ft-verbatim-cy	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "base_model:openai/whisper-tiny", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-06T08:14:16+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us	whisper-tiny-ft-verbatim-cy =========================== This model is a fine-tuned version of openai/whisper-tiny on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.7686 * Wer: 53.9846 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 4 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 4000 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 68, 143, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Large V2 This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3247 - Wer: 13.4709 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 20 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.5388 \| 0.49 \| 30 \| 0.3297 \| 12.2434 \| \| 0.2858 \| 0.98 \| 60 \| 0.2893 \| 23.3419 \| \| 0.143 \| 1.48 \| 90 \| 0.2922 \| 13.5327 \| \| 0.1337 \| 1.97 \| 120 \| 0.2838 \| 10.7065 \| \| 0.0606 \| 2.46 \| 150 \| 0.2905 \| 10.3765 \| \| 0.0557 \| 2.95 \| 180 \| 0.2915 \| 10.0258 \| \| 0.0265 \| 3.44 \| 210 \| 0.3139 \| 10.8613 \| \| 0.0207 \| 3.93 \| 240 \| 0.3094 \| 10.0670 \| \| 0.0098 \| 4.43 \| 270 \| 0.3188 \| 12.0578 \| \| 0.0098 \| 4.92 \| 300 \| 0.3247 \| 13.4709 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0	{"language": ["nl"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-large-v2", "model-index": [{"name": "Whisper Large V2", "results": []}]}	automatic-speech-recognition	golesheed/whisper-native-elderly-4-dutch	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "nl", "base_model:openai/whisper-large-v2", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-06T08:15:34+00:00	[]	[ "nl" ]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us	Whisper Large V2 ================ This model is a fine-tuned version of openai/whisper-large-v2 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.3247 * Wer: 13.4709 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 20 * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.14.6 * Tokenizers 0.15.0	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ 74, 116, 4, 38 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #nl #base_model-openai/whisper-large-v2 #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 20\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.6\n* Tokenizers 0.15.0" ]	[ -0.13621632754802704, 0.15968677401542664, -0.001858389237895608, 0.08833666890859604, 0.08782155066728592, -0.012314315885305405, 0.1681707203388214, 0.12549269199371338, -0.0416557751595974, 0.10454203933477402, 0.11453307420015335, 0.07736121118068695, 0.04561321437358856, 0.19964902102947235, 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null	null	transformers	# IUPAC2SMILES-canonical-base IUPAC2SMILES-canonical-base was designed to accurately translate IUPAC chemical names to SMILES. ## Model Details ### Model Description IUPAC2SMILES-canonical-base is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder. - Developed by: Knowladgator Engineering - Model type: Encoder-Decoder with attention mechanism - Language(s) (NLP): SMILES, IUPAC (English) - License: Apache License 2.0 ### Model Sources - Paper: coming soon - Demo: [ChemicalConverters](https://huggingface.co/spaces/knowledgator/ChemicalConverters) ## Quickstart Firstly, install the library: ```commandline pip install chemical-converters ``` ### IUPAC to SMILES #### To perform simple translation, follow the example: ```python from chemicalconverters import NamesConverter converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base") print(converter.iupac_to_smiles('ethanol')) print(converter.iupac_to_smiles(['ethanol', 'ethanol', 'ethanol'])) ``` ```text ['CCO'] ['CCO', 'CCO', 'CCO'] ``` #### Processing in batches: ```python from chemicalconverters import NamesConverter converter = NamesConverter(model_name="knowledgator/IUPAC2SMILES-canonical-base") print(converter.iupac_to_smiles(["buta-1,3-diene" for _ in range(10)], num_beams=1, process_in_batch=True, batch_size=1000)) ``` ```text ['<SYST>C=CC=C', '<SYST>C=CC=C'...] ``` Our models also predict IUPAC styles from the table: \| Style Token \| Description \| \|-------------\|----------------------------------------------------------------------------------------------------\| \| `<BASE>` \| The most known name of the substance, sometimes is the mixture of traditional and systematic style \| \| `<SYST>` \| The totally systematic style without trivial names \| \| `<TRAD>` \| The style is based on trivial names of the parts of substances \| ## Bias, Risks, and Limitations This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES. ### Training Procedure <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.00001, batch_size=512 for 2 epochs. ## Evaluation \| Model \| Accuracy \| BLEU-4 score \| Size(MB) \| \|-------------------------------------\|---------\|------------------\|----------\| \| IUPAC2SMILES-canonical-small \|88.9% \|0.966 \|23 \| \| IUPAC2SMILES-canonical-base \|93.7% \|0.974 \|180 \| \| STOUT V2.0\* \|68.47% \|0.92 \|128 \| *According to the original paper https://jcheminf.biomedcentral.com/articles/10.1186/s13321-021-00512-4 ## Citation Coming soon. ## Model Card Authors [Mykhailo Shtopko](https://huggingface.co/BioMike) ## Model Card Contact [email protected]	{"license": "apache-2.0", "tags": ["chemistry", "biology", "medical", "smiles", "iupac", "text-generation-inference"], "metrics": ["accuracy", "bleu"], "pipeline_tag": "text2text-generation", "widget": [{"text": "ethanol", "example_title": "CCO"}]}	text2text-generation	knowledgator/IUPAC2SMILES-canonical-base	[ "transformers", "pytorch", "mt5", "text2text-generation", "chemistry", "biology", "medical", "smiles", "iupac", "text-generation-inference", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2024-02-06T08:15:49+00:00	[]	[]	TAGS #transformers #pytorch #mt5 #text2text-generation #chemistry #biology #medical #smiles #iupac #text-generation-inference #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us	IUPAC2SMILES-canonical-base =========================== IUPAC2SMILES-canonical-base was designed to accurately translate IUPAC chemical names to SMILES. Model Details ------------- ### Model Description IUPAC2SMILES-canonical-base is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder. * Developed by: Knowladgator Engineering * Model type: Encoder-Decoder with attention mechanism * Language(s) (NLP): SMILES, IUPAC (English) * License: Apache License 2.0 ### Model Sources * Paper: coming soon * Demo: ChemicalConverters Quickstart ---------- Firstly, install the library: ### IUPAC to SMILES #### To perform simple translation, follow the example: #### Processing in batches: Our models also predict IUPAC styles from the table: Bias, Risks, and Limitations ---------------------------- This model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES. ### Training Procedure The model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.00001, batch\_size=512 for 2 epochs. Evaluation ---------- Coming soon. Model Card Authors ------------------ Mykhailo Shtopko Model Card Contact ------------------ info@URL	[ "### Model Description\n\n\nIUPAC2SMILES-canonical-base is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder.\n\n\n* Developed by: Knowladgator Engineering\n* Model type: Encoder-Decoder with attention mechanism\n* Language(s) (NLP): SMILES, IUPAC (English)\n* License: Apache License 2.0", "### Model Sources\n\n\n* Paper: coming soon\n* Demo: ChemicalConverters\n\n\nQuickstart\n----------\n\n\nFirstly, install the library:", "### IUPAC to SMILES", "#### To perform simple translation, follow the example:", "#### Processing in batches:\n\n\nOur models also predict IUPAC styles from the table:\n\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nThis model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.", "### Training Procedure\n\n\nThe model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.00001, batch\\_size=512 for 2 epochs.\n\n\nEvaluation\n----------\n\n\n\nComing soon.\n\n\nModel Card Authors\n------------------\n\n\nMykhailo Shtopko\n\n\nModel Card Contact\n------------------\n\n\ninfo@URL" ]	[ "TAGS\n#transformers #pytorch #mt5 #text2text-generation #chemistry #biology #medical #smiles #iupac #text-generation-inference #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "### Model Description\n\n\nIUPAC2SMILES-canonical-base is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder.\n\n\n* Developed by: Knowladgator Engineering\n* Model type: Encoder-Decoder with attention mechanism\n* Language(s) (NLP): SMILES, IUPAC (English)\n* License: Apache License 2.0", "### Model Sources\n\n\n* Paper: coming soon\n* Demo: ChemicalConverters\n\n\nQuickstart\n----------\n\n\nFirstly, install the library:", "### IUPAC to SMILES", "#### To perform simple translation, follow the example:", "#### Processing in batches:\n\n\nOur models also predict IUPAC styles from the table:\n\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nThis model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.", "### Training Procedure\n\n\nThe model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.00001, batch\\_size=512 for 2 epochs.\n\n\nEvaluation\n----------\n\n\n\nComing soon.\n\n\nModel Card Authors\n------------------\n\n\nMykhailo Shtopko\n\n\nModel Card Contact\n------------------\n\n\ninfo@URL" ]	[ 78, 94, 30, 9, 11, 63, 73 ]	[ "passage: TAGS\n#transformers #pytorch #mt5 #text2text-generation #chemistry #biology #medical #smiles #iupac #text-generation-inference #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n### Model Description\n\n\nIUPAC2SMILES-canonical-base is based on the MT5 model with optimizations in implementing different tokenizers for the encoder and decoder.\n\n\n* Developed by: Knowladgator Engineering\n* Model type: Encoder-Decoder with attention mechanism\n* Language(s) (NLP): SMILES, IUPAC (English)\n* License: Apache License 2.0### Model Sources\n\n\n* Paper: coming soon\n* Demo: ChemicalConverters\n\n\nQuickstart\n----------\n\n\nFirstly, install the library:### IUPAC to SMILES#### To perform simple translation, follow the example:#### Processing in batches:\n\n\nOur models also predict IUPAC styles from the table:\n\n\n\nBias, Risks, and Limitations\n----------------------------\n\n\nThis model has limited accuracy in processing large molecules and currently, doesn't support isomeric and isotopic SMILES.### Training Procedure\n\n\nThe model was trained on 100M examples of SMILES-IUPAC pairs with lr=0.00001, batch\\_size=512 for 2 epochs.\n\n\nEvaluation\n----------\n\n\n\nComing soon.\n\n\nModel Card Authors\n------------------\n\n\nMykhailo Shtopko\n\n\nModel Card Contact\n------------------\n\n\ninfo@URL" ]	[ -0.07426460832357407, 0.1717124730348587, -0.0030647756066173315, -0.015530804172158241, 0.045839592814445496, 0.013356316834688187, 0.12014619261026382, 0.14481109380722046, 0.03867899626493454, 0.14497612416744232, 0.06625079363584518, 0.03162157163023949, 0.10001114010810852, 0.18725384771823883, 0.03873114660382271, -0.2249518632888794, 0.07299216836690903, -0.10684007406234741, -0.07457195222377777, 0.10600138455629349, 0.10340218245983124, -0.05776910483837128, 0.06953675299882889, -0.01883113570511341, -0.0257231704890728, -0.012817972339689732, -0.06813885271549225, -0.08043279498815536, 0.043540846556425095, 0.01134704053401947, 0.031750500202178955, 0.021995581686496735, 0.060616374015808105, -0.27903956174850464, 0.014493661932647228, 0.047046586871147156, 0.04771025478839874, 0.06065120920538902, 0.11986016482114792, -0.04959113895893097, 0.1418299376964569, -0.15403224527835846, 0.07260207086801529, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mbart-sahit5rans This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"language": ["sa", "hi"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/mt5-base", "model-index": [{"name": "mbart-sahit5rans", "results": []}]}	text2text-generation	balaramas/mbart-sahit5rans	[ "transformers", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "sa", "hi", "base_model:google/mt5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T08:16:17+00:00	[]	[ "sa", "hi" ]	TAGS #transformers #safetensors #mt5 #text2text-generation #generated_from_trainer #sa #hi #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# mbart-sahit5rans This model is a fine-tuned version of google/mt5-base on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# mbart-sahit5rans\n\nThis model is a fine-tuned version of google/mt5-base on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 8.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #mt5 #text2text-generation #generated_from_trainer #sa #hi #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# mbart-sahit5rans\n\nThis model is a fine-tuned version of google/mt5-base on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 8.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 80, 33, 6, 12, 8, 3, 91, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #mt5 #text2text-generation #generated_from_trainer #sa #hi #base_model-google/mt5-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# mbart-sahit5rans\n\nThis model is a fine-tuned version of google/mt5-base on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 4\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 8.0### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.2+cu121\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.08668193221092224, 0.13919423520565033, -0.0022630987223237753, 0.10175016522407532, 0.14703667163848877, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # lmind_nq_train10000_eval6489_v1_qa_tyzhu_lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl This model is a fine-tuned version of [tyzhu/lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl](https://huggingface.co/tyzhu/lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl) on the tyzhu/lmind_nq_train10000_eval6489_v1_qa dataset. It achieves the following results on the evaluation set: - Loss: 3.0531 - Accuracy: 0.5457 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 20.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 2.109 \| 1.0 \| 625 \| 2.0393 \| 0.5570 \| \| 1.5029 \| 2.0 \| 1250 \| 2.0911 \| 0.5576 \| \| 1.0521 \| 3.0 \| 1875 \| 2.2873 \| 0.5567 \| \| 0.8245 \| 4.0 \| 2500 \| 2.4180 \| 0.5545 \| \| 0.7322 \| 5.0 \| 3125 \| 2.4951 \| 0.5484 \| \| 0.6771 \| 6.0 \| 3750 \| 2.5769 \| 0.5539 \| \| 0.6445 \| 7.0 \| 4375 \| 2.6174 \| 0.5541 \| \| 0.614 \| 8.0 \| 5000 \| 2.6924 \| 0.5539 \| \| 0.5966 \| 9.0 \| 5625 \| 2.7009 \| 0.5529 \| \| 0.58 \| 10.0 \| 6250 \| 2.7747 \| 0.5515 \| \| 0.5657 \| 11.0 \| 6875 \| 2.7767 \| 0.5507 \| \| 0.5585 \| 12.0 \| 7500 \| 2.8466 \| 0.5503 \| \| 0.5432 \| 13.0 \| 8125 \| 2.8841 \| 0.5502 \| \| 0.5284 \| 14.0 \| 8750 \| 2.9405 \| 0.5498 \| \| 0.5242 \| 15.0 \| 9375 \| 2.8969 \| 0.5491 \| \| 0.511 \| 16.0 \| 10000 \| 2.9666 \| 0.5480 \| \| 0.5051 \| 17.0 \| 10625 \| 2.9805 \| 0.5487 \| \| 0.4947 \| 18.0 \| 11250 \| 2.9896 \| 0.5476 \| \| 0.4832 \| 19.0 \| 11875 \| 2.9937 \| 0.5473 \| \| 0.4803 \| 20.0 \| 12500 \| 3.0531 \| 0.5457 \| ### Framework versions - Transformers 4.34.0 - Pytorch 2.1.0+cu121 - Datasets 2.14.5 - Tokenizers 0.14.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["tyzhu/lmind_nq_train10000_eval6489_v1_qa"], "metrics": ["accuracy"], "base_model": "tyzhu/lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl", "model-index": [{"name": "lmind_nq_train10000_eval6489_v1_qa_tyzhu_lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "tyzhu/lmind_nq_train10000_eval6489_v1_qa", "type": "tyzhu/lmind_nq_train10000_eval6489_v1_qa"}, "metrics": [{"type": "accuracy", "value": 0.5456769049002058, "name": "Accuracy"}]}]}]}	text-generation	tyzhu/lmind_nq_train10000_eval6489_v1_qa_tyzhu_lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "dataset:tyzhu/lmind_nq_train10000_eval6489_v1_qa", "base_model:tyzhu/lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-06T08:23:13+00:00	[]	[]	TAGS #transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_nq_train10000_eval6489_v1_qa #base_model-tyzhu/lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	lmind\_nq\_train10000\_eval6489\_v1\_qa\_tyzhu\_lmind\_nq\_train10000\_eval6489\_v1\_docidx\_gpt2-xl ==================================================================================================== This model is a fine-tuned version of tyzhu/lmind\_nq\_train10000\_eval6489\_v1\_docidx\_gpt2-xl on the tyzhu/lmind\_nq\_train10000\_eval6489\_v1\_qa dataset. It achieves the following results on the evaluation set: * Loss: 3.0531 * Accuracy: 0.5457 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * num\_epochs: 20.0 ### Training results ### Framework versions * Transformers 4.34.0 * Pytorch 2.1.0+cu121 * Datasets 2.14.5 * Tokenizers 0.14.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1" ]	[ "TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_nq_train10000_eval6489_v1_qa #base_model-tyzhu/lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0", "### Training results", "### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1" ]	[ 125, 99, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #gpt2 #text-generation #generated_from_trainer #dataset-tyzhu/lmind_nq_train10000_eval6489_v1_qa #base_model-tyzhu/lmind_nq_train10000_eval6489_v1_docidx_gpt2-xl #license-mit #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: constant\n* num\\_epochs: 20.0### Training results### Framework versions\n\n\n* Transformers 4.34.0\n* Pytorch 2.1.0+cu121\n* Datasets 2.14.5\n* Tokenizers 0.14.1" ]	[ -0.08655204623937607, 0.06521283090114594, -0.0037300502881407738, 0.08564288914203644, 0.14113466441631317, 0.04371725022792816, 0.14187544584274292, 0.13558922708034515, -0.133244588971138, 0.060869261622428894, 0.12653748691082, 0.1292819380760193, 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null	null	peft	## Training procedure ### Framework versions - PEFT 0.4.0	{"library_name": "peft"}	null	lourenswal/bloom_prompt_tuning_1707207951.865479	[ "peft", "safetensors", "region:us" ]	2024-02-06T08:25:18+00:00	[]	[]	TAGS #peft #safetensors #region-us	## Training procedure ### Framework versions - PEFT 0.4.0	[ "## Training procedure", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ "TAGS\n#peft #safetensors #region-us \n", "## Training procedure", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ 14, 3, 11 ]	[ "passage: TAGS\n#peft #safetensors #region-us \n## Training procedure### Framework versions\n\n\n- PEFT 0.4.0" ]	[ -0.11905625462532043, 0.0345856249332428, -0.005941327195614576, 0.021338168531656265, 0.1318943053483963, -0.04500391334295273, 0.11790177971124649, 0.044265083968639374, 0.02826196327805519, 0.04287289083003998, 0.14522042870521545, 0.012527059763669968, -0.002473803237080574, 0.23268604278564453, -0.02491838112473488, -0.24689897894859314, 0.0963108167052269, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PXAudio Whisper For user_476da26872df492f830a65925d422651 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ja 0.1 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0+cu118 - Datasets 2.16.1 - Tokenizers 0.13.3	{"language": ["ja"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["pxaudio/user_476da26872df492f830a65925d422651_model"], "base_model": "openai/whisper-small", "model-index": [{"name": "PXAudio Whisper For user_476da26872df492f830a65925d422651", "results": []}]}	automatic-speech-recognition	hoangvanvietanh/user_476da26872df492f830a65925d422651_model	[ "transformers", "pytorch", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "ja", "dataset:pxaudio/user_476da26872df492f830a65925d422651_model", "base_model:openai/whisper-small", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-06T08:27:35+00:00	[]	[ "ja" ]	TAGS #transformers #pytorch #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #ja #dataset-pxaudio/user_476da26872df492f830a65925d422651_model #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us	# PXAudio Whisper For user_476da26872df492f830a65925d422651 This model is a fine-tuned version of openai/whisper-small on the ja 0.1 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 50 ### Training results ### Framework versions - Transformers 4.33.3 - Pytorch 2.2.0+cu118 - Datasets 2.16.1 - Tokenizers 0.13.3	[ "# PXAudio Whisper For user_476da26872df492f830a65925d422651\n\nThis model is a fine-tuned version of openai/whisper-small on the ja 0.1 dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 50", "### Training results", "### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #ja #dataset-pxaudio/user_476da26872df492f830a65925d422651_model #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n", "# PXAudio Whisper For user_476da26872df492f830a65925d422651\n\nThis model is a fine-tuned version of openai/whisper-small on the ja 0.1 dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 50", "### Training results", "### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.13.3" ]	[ 106, 54, 6, 12, 8, 3, 104, 4, 33 ]	[ "passage: TAGS\n#transformers #pytorch #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #ja #dataset-pxaudio/user_476da26872df492f830a65925d422651_model #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us \n# PXAudio Whisper For user_476da26872df492f830a65925d422651\n\nThis model is a fine-tuned version of openai/whisper-small on the ja 0.1 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 1e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- training_steps: 50### Training results### Framework versions\n\n- Transformers 4.33.3\n- Pytorch 2.2.0+cu118\n- Datasets 2.16.1\n- Tokenizers 0.13.3" ]	[ -0.12377674877643585, 0.18442083895206451, -0.0038589853793382645, 0.09028810262680054, 0.10626311600208282, 0.0285260621458292, 0.10860702395439148, 0.17101843655109406, -0.038902658969163895, 0.10248441249132156, 0.06980402767658234, 0.040130238980054855, 0.07975504547357559, 0.1689399629831314, -0.03384440019726753, -0.2609032392501831, -0.008135715499520302, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	keisar/publick1NVIDIAL4GPULeo	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-06T08:28:20+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SciBERT_25K_steps_bs64 This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0177 - Accuracy: 0.9941 - Precision: 0.7990 - Recall: 0.5288 - F1: 0.6364 - Hamming: 0.0059 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 25000 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| Hamming \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\|:-------:\| \| 0.0467 \| 0.16 \| 5000 \| 0.0416 \| 0.9902 \| 0.0 \| 0.0 \| 0.0 \| 0.0098 \| \| 0.0236 \| 0.32 \| 10000 \| 0.0223 \| 0.9932 \| 0.8192 \| 0.3929 \| 0.5311 \| 0.0068 \| \| 0.0198 \| 0.47 \| 15000 \| 0.0190 \| 0.9939 \| 0.8015 \| 0.4934 \| 0.6108 \| 0.0061 \| \| 0.0185 \| 0.63 \| 20000 \| 0.0180 \| 0.9940 \| 0.7974 \| 0.5220 \| 0.6310 \| 0.0060 \| \| 0.0181 \| 0.79 \| 25000 \| 0.0177 \| 0.9941 \| 0.7990 \| 0.5288 \| 0.6364 \| 0.0059 \| ### Framework versions - Transformers 4.35.0.dev0 - Pytorch 2.0.1+cu118 - Datasets 2.7.1 - Tokenizers 0.14.1	{"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "allenai/scibert_scivocab_uncased", "model-index": [{"name": "SciBERT_25K_steps_bs64", "results": []}]}	text-classification	bdpc/SciBERT_25K_steps_bs64	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:allenai/scibert_scivocab_uncased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-06T08:32:03+00:00	[]	[]	TAGS #transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us	SciBERT\_25K\_steps\_bs64 ========================= This model is a fine-tuned version of allenai/scibert\_scivocab\_uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0177 * Accuracy: 0.9941 * Precision: 0.7990 * Recall: 0.5288 * F1: 0.6364 * Hamming: 0.0059 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 25000 ### Training results ### Framework versions * Transformers 4.35.0.dev0 * Pytorch 2.0.1+cu118 * Datasets 2.7.1 * Tokenizers 0.14.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1" ]	[ "TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1" ]	[ 61, 116, 4, 36 ]	[ "passage: TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #base_model-allenai/scibert_scivocab_uncased #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* training\\_steps: 25000### Training results### Framework versions\n\n\n* Transformers 4.35.0.dev0\n* Pytorch 2.0.1+cu118\n* Datasets 2.7.1\n* Tokenizers 0.14.1" ]	[ -0.12256453931331635, 0.06859574466943741, -0.0022490574046969414, 0.13081152737140656, 0.17255432903766632, 0.03548503667116165, 0.11826042830944061, 0.13099230825901031, -0.06966569274663925, 0.03890903666615486, 0.12485005706548691, 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