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train · 975k rows

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags list	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
nyunai/OpenHathi-7B-Hi-v0.1-Base-AWQ-wikitext	nyunai	2024-03-06T10:14:20Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-06T09:36:44Z	--- library_name: transformers tags: [] --- ## Model Description This model is a compressed version of the OpenHathi-7B-Hi base model, optimized for chat format text data in the Hindi language. It has been quantized using the AWQ technique with calibration data from the samvaad-hi-v1 dataset. The compression process aims to reduce the model size while preserving its performance on chat-oriented tasks. ## Model Usage: The compressed model can be utilized for various natural language processing tasks, particularly those involving chat format text data in Hindi. It can be deployed in conversational AI systems, chatbots, or any application requiring efficient processing of chat-style interactions. ## Performance Metrics: - Model Size: 4.15 GB - Compression Technique: AWQ - Calibration Data: [wikitext](https://huggingface.co/datasets/wikitext) dataset - Tokenization Model Size: 968 KB - Performance: The compressed model's performance has been evaluated on various chat-oriented tasks, demonstrating efficiency in handling conversational text data while maintaining comparable performance to the original base model. Limitations: While the compressed model offers significant reductions in size, there may be slight trade-offs in performance compared to the full-sized base model. It may not perform optimally on tasks outside the scope of chat-oriented text data in Hindi.
Leelakrish/my-pet-lion-xzg	Leelakrish	2024-03-06T10:12:19Z	0	0	null	[ "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-06T10:10:10Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### My-Pet-Lion-XZG Dreambooth model trained by Leelakrish following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 21BRS1638 Sample pictures of this concept: ![0](https://huggingface.co/Leelakrish/my-pet-lion-xzg/resolve/main/sample_images/89776_A_giant_lion_roaring_and_the_knight_is_preparing_t_xl-1024-v1-0.png)
Hemg/Brain-Tumor-Classification	Hemg	2024-03-06T10:11:06Z	38	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-06T05:51:46Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: Brain-Tumor-Classification results: [] --- <!-- 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. --> # Brain-Tumor-Classification 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: - Loss: 0.0872 - Accuracy: 0.9758 ## 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: 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: 16 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.2074 \| 1.0 \| 44 \| 0.8060 \| 0.8128 \| \| 0.4897 \| 2.0 \| 88 \| 0.3008 \| 0.9274 \| \| 0.2462 \| 3.0 \| 132 \| 0.2464 \| 0.9331 \| \| 0.1937 \| 4.0 \| 176 \| 0.1918 \| 0.9502 \| \| 0.1523 \| 5.0 \| 220 \| 0.1699 \| 0.9502 \| \| 0.1371 \| 6.0 \| 264 \| 0.1372 \| 0.9644 \| \| 0.1104 \| 7.0 \| 308 \| 0.1121 \| 0.9708 \| \| 0.1097 \| 8.0 \| 352 \| 0.1220 \| 0.9651 \| \| 0.1015 \| 9.0 \| 396 \| 0.1053 \| 0.9737 \| \| 0.0841 \| 10.0 \| 440 \| 0.1142 \| 0.9708 \| \| 0.0839 \| 11.0 \| 484 \| 0.1073 \| 0.9708 \| \| 0.0771 \| 12.0 \| 528 \| 0.1156 \| 0.9665 \| \| 0.074 \| 13.0 \| 572 \| 0.1203 \| 0.9644 \| \| 0.0652 \| 14.0 \| 616 \| 0.0706 \| 0.9858 \| \| 0.0694 \| 15.0 \| 660 \| 0.0984 \| 0.9744 \| \| 0.0596 \| 16.0 \| 704 \| 0.0872 \| 0.9758 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
slukas99/tex_inv_af_dress	slukas99	2024-03-06T10:07:28Z	10	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "textual_inversion", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T08:47:39Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - textual_inversion inference: true base_model: runwayml/stable-diffusion-v1-5 --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # Textual inversion text2image fine-tuning - slukas99/tex_inv_af_dress These are textual inversion adaption weights for runwayml/stable-diffusion-v1-5. 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) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
YMKiii/output1	YMKiii	2024-03-06T10:04:15Z	19	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2", "base_model:finetune:stabilityai/stable-diffusion-2", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T09:17:04Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2 inference: true instance_prompt: interior design --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - YMKiii/output1 This is a dreambooth model derived from stabilityai/stable-diffusion-2. The weights were trained on interior design using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
hellie/newsroommodel	hellie	2024-03-06T10:02:52Z	5	0	transformers	[ "transformers", "safetensors", "pegasus", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-03-06T10:01:20Z	--- library_name: transformers tags: [] --- # 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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mii-llm/maestrale-chat-v0.3-beta-sft	mii-llm	2024-03-06T10:00:53Z	14	2	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "sft", "it", "chatml", "axolotl", "conversational", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-02-09T09:26:06Z	--- language: - it license: cc-by-nc-4.0 tags: - sft - it - mistral - chatml - axolotl prompt_template: <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant model-index: - name: maestrale-chat-v0.3-beta results: [] --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://i.imgur.com/dgSNbTl.jpg" alt="Mii-LLM" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <div style="display: flex; justify-content: space-between; width: 100%;"> <div style="display: flex; flex-direction: column; align-items: flex-end;"> <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://buy.stripe.com/8wM00Sf3vb3H3pmfYY">Want to contribute? Please donate! This will let us work on better datasets and models!</a></p> </div> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Maestrale chat beta ༄ By @efederici and @mferraretto ## Model description - Language Model: Mistral-7b for the Italian language, continued pre-training for Italian on a curated large-scale high-quality corpus. - Fine-Tuning: SFT performed on convs/instructions for three epochs. v0.3 - Function calling - Reduced default system prompt to avoid wasting tokens (pre-alignment) This model uses ChatML prompt format: ``` <\|im_start\|>system Sei un assistente utile.<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` ## Usage: ```python from transformers import ( AutoTokenizer, AutoModelForCausalLM, GenerationConfig, TextStreamer ) import torch tokenizer = AutoTokenizer.from_pretrained("mii-llm/maestrale-chat-v0.3-beta") model = AutoModelForCausalLM.from_pretrained("mii-llm/maestrale-chat-v0.3-beta", load_in_8bit=True, device_map="auto") gen = GenerationConfig( do_sample=True, temperature=0.7, repetition_penalty=1.2, top_k=50, top_p=0.95, max_new_tokens=500, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.convert_tokens_to_ids("<\|im_end\|>") ) messages = [ {"role": "system", "content": "Sei un assistente utile."}, {"role": "user", "content": "{prompt}"} ] with torch.no_grad(), torch.backends.cuda.sdp_kernel( enable_flash=True, enable_math=False, enable_mem_efficient=False ): temp = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(temp, return_tensors="pt").to("cuda") streamer = TextStreamer(tokenizer, skip_prompt=True) _ = model.generate( **inputs, streamer=streamer, generation_config=gen ) ``` ## Intended uses & limitations It's a beta sft version, but it's not `aligned`. It's a first test. We are working on alignment data and evals. [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
nyunai/OpenHathi-7B-Hi-v0.1-Base-AWQ-samvaad-hi-v1-chat-format	nyunai	2024-03-06T10:00:46Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-06T09:34:58Z	--- library_name: transformers tags: [] --- ## Model Description This model is a compressed version of the OpenHathi-7B-Hi base model, optimized for chat format text data in the Hindi language. It has been quantized using the AWQ technique with calibration data from the samvaad-hi-v1 dataset. The compression process aims to reduce the model size while preserving its performance on chat-oriented tasks. ## Model Usage: The compressed model can be utilized for various natural language processing tasks, particularly those involving chat format text data in Hindi. It can be deployed in conversational AI systems, chatbots, or any application requiring efficient processing of chat-style interactions. ## Performance Metrics: - Model Size: 4.15 GB - Compression Technique: AWQ - Calibration Data: [samvaad-hi-v1 chat format](https://huggingface.co/datasets/shwubham/samvaad-hi-v1-chat-format) dataset - Tokenization Model Size: 968 KB - Performance: The compressed model's performance has been evaluated on various chat-oriented tasks, demonstrating efficiency in handling conversational text data while maintaining comparable performance to the original base model. Limitations: While the compressed model offers significant reductions in size, there may be slight trade-offs in performance compared to the full-sized base model. It may not perform optimally on tasks outside the scope of chat-oriented text data in Hindi.
nyunai/OpenHathi-7B-Hi-v0.1-Base-AWQ-samvaad-hi-v1-tulu-format	nyunai	2024-03-06T09:59:28Z	4	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-06T09:38:22Z	--- library_name: transformers tags: [] --- ## Model Description This model is a compressed version of the OpenHathi-7B-Hi base model, optimized for chat format text data in the Hindi language. It has been quantized using the AWQ technique with calibration data from the samvaad-hi-v1 dataset. The compression process aims to reduce the model size while preserving its performance on chat-oriented tasks. ## Model Usage: The compressed model can be utilized for various natural language processing tasks, particularly those involving chat format text data in Hindi. It can be deployed in conversational AI systems, chatbots, or any application requiring efficient processing of chat-style interactions. ## Performance Metrics: - Model Size: 4.15 GB - Compression Technique: AWQ - Calibration Data: [samvaad-hi-v1 tulu format](https://huggingface.co/datasets/shwubham/samvaad-hi-v1-tulu-format) dataset - Tokenization Model Size: 968 KB - Performance: The compressed model's performance has been evaluated on various chat-oriented tasks, demonstrating efficiency in handling conversational text data while maintaining comparable performance to the original base model. Limitations: While the compressed model offers significant reductions in size, there may be slight trade-offs in performance compared to the full-sized base model. It may not perform optimally on tasks outside the scope of chat-oriented text data in Hindi.
csukuangfj/sherpa-ncnn-toolchains	csukuangfj	2024-03-06T09:58:32Z	0	0	null	[ "region:us" ]	null	2022-12-18T05:53:46Z	# Introduction Please refer to https://k2-fsa.github.io/sherpa/ncnn/index.html# for usage.
joshus/esg_base_pos_3	joshus	2024-03-06T09:57:24Z	5	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "feature-extraction", "sentence-similarity", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-06T09:57:07Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # joshus/esg_base_pos_3 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('joshus/esg_base_pos_3') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=joshus/esg_base_pos_3) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 360 with parameters: ``` {'batch_size': 5, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 108, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
hiiamsid/mistral_yt_transcribe_classification_opt_train	hiiamsid	2024-03-06T09:44:38Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "generated_from_trainer", "conversational", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T17:49:44Z	--- license: apache-2.0 base_model: mistralai/Mistral-7B-Instruct-v0.2 tags: - generated_from_trainer model-index: - name: mistral_yt_transcribe_classification_opt_train results: [] --- <!-- 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. --> # mistral_yt_transcribe_classification_opt_train This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0335 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.0393 \| 1.0 \| 640 \| 0.0381 \| \| 0.0334 \| 2.0 \| 1281 \| 0.0340 \| \| 0.0226 \| 3.0 \| 1921 \| 0.0343 \| \| 0.0275 \| 4.0 \| 2560 \| 0.0335 \| ### Framework versions - Transformers 4.36.0 - Pytorch 2.0.1+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
jelldps/malaysian-mistral-7b-32k-instructions-v4-gguf	jelldps	2024-03-06T09:41:56Z	6	3	transformers	[ "transformers", "gguf", "mistral", "text-generation", "conversational", "ms", "base_model:mesolitica/malaysian-mistral-7b-32k-instructions-v3.5", "base_model:quantized:mesolitica/malaysian-mistral-7b-32k-instructions-v3.5", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-02-27T10:32:08Z	--- base_model: mesolitica/malaysian-mistral-7b-32k-instructions-v3.5 language: - ms --- # malaysian-mistral-7b-32k-instructions-v4 - GGUF - Model creator: [Mesolitica](https://huggingface.co/mesolitica) - Original model: [malaysian-mistral-7b-32k-instructions-v4](https://huggingface.co/mesolitica/malaysian-mistral-7b-32k-instructions-v4)
vidhi0206/setfit-paraphrase-mpnet-emotion	vidhi0206	2024-03-06T09:41:22Z	4	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-02-28T12:34:57Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer metrics: - accuracy widget: - text: i honestly thought impossible at this point i feel pretty - text: i feel convinced that im going to shy away from whatever is really good for me - text: i feel guilt that i should be more caring and im not - text: i found myself feeling nostalgic as i thought about the temporarily abandoned little bishop chronicles - text: i am feeling very indecisive and spontaneous pipeline_tag: text-classification inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: Unknown type: unknown split: test metrics: - type: accuracy value: 0.5225 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 6 classes <!-- - Training Dataset: [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1 \| <ul><li>'i feel so much better about that number'</li><li>'i feel like i have reached a plateau where im not buying as much as i use to and feeling more satisfied with my wardrobe and personal style'</li><li>'i feel especially thankful'</li></ul> \| \| 3 \| <ul><li>'i feel so violent just want to break some glass'</li><li>'i always feel rushed on the way to visit no comments'</li><li>'i think maybe about how strongly she feels about him and being there for him but brad looks really distracted'</li></ul> \| \| 5 \| <ul><li>'i feel like when i was a kid it was constantly impressed upon me how awesome ants are'</li><li>'i feel like it s a boy i would be pretty shocked if it was so somewhere in there my gut or my brain is saying girl'</li><li>'i feel like every day i walk around with so much stress and sadness that im literally amazed im still here that i still function that im still basically a friendly stable person'</li></ul> \| \| 0 \| <ul><li>'i would feel that a few words would be not only inadequate but a travesty'</li><li>'i attributed this depression to feeling inadequate against the unrealistic ideals of the lds church and while i still hold those ideals somewhat responsible i recognize this pattern of behavior'</li><li>'ive been resting and feeling generally unpleasant and queasy but in that frustrating background way where you dont feel right but cant place an exact cause'</li></ul> \| \| 4 \| <ul><li>'i was starting to feel scared for both of their safety and i wish those officers hadn t left no matter how much i hated them'</li><li>'i am already feeling frantic'</li><li>'i believe in you moment we all feel til then it s one more skeptical song'</li></ul> \| \| 2 \| <ul><li>'i do feel sympathetic to the parties involved now that their careers are down the drain'</li><li>'i like frappes and shit when im feeling naughty but i drink tea daily'</li><li>'i will pay a month for months and feel shame every time i grill a hot dog from that point on'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.5225 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("vidhi0206/setfit-paraphrase-mpnet-emotion") # Run inference preds = model("i am feeling very indecisive and spontaneous") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 4 \| 19.3333 \| 48 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 8 \| \| 1 \| 8 \| \| 2 \| 8 \| \| 3 \| 8 \| \| 4 \| 8 \| \| 5 \| 8 \| ### Training Hyperparameters - batch_size: (8, 8) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:------:\|:----:\|:-------------:\|:---------------:\| \| 0.0042 \| 1 \| 0.3009 \| - \| \| 0.2083 \| 50 \| 0.1916 \| - \| \| 0.4167 \| 100 \| 0.0393 \| - \| \| 0.625 \| 150 \| 0.0129 \| - \| \| 0.8333 \| 200 \| 0.0034 \| - \| ### Framework Versions - Python: 3.8.10 - SetFit: 1.0.3 - Sentence Transformers: 2.3.1 - Transformers: 4.37.2 - PyTorch: 2.2.0+cu121 - Datasets: 2.17.0 - Tokenizers: 0.15.1 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
s14pe/ppo-LunarLander-v2	s14pe	2024-03-06T09:23:50Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-03-05T14:14:49Z	--- 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: 279.00 +/- 15.83 name: mean_reward verified: false --- # 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 ... ```
alfredplpl/gemma-2b-it-ja-poc-2	alfredplpl	2024-03-06T09:21:13Z	2	2	peft	[ "peft", "safetensors", "ja", "en", "license:other", "region:us" ]	null	2024-03-05T12:17:24Z	--- language: - ja - en license: other library_name: peft license_name: gemma-terms-of-use license_link: https://www.kaggle.com/models/google/gemma/license/consent --- # はじめになんか日本語が話せる商用利用可能なAIです。 [Google Colab](https://colab.research.google.com/drive/1AZ3oW1RJ8JDi4DGh3_z__aAd1lUVlswi?usp=sharing) # Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch from peft import PeftModel # トークナイザーとモデルの準備 tokenizer = AutoTokenizer.from_pretrained("alfredplpl/ja-aozora-wikipedia-gemmba-2b") model = AutoModelForCausalLM.from_pretrained("alfredplpl/ja-aozora-wikipedia-gemmba-2b") model = PeftModel.from_pretrained(model = model, model_id = "alfredplpl/gemma-2b-it-ja-poc-2") # プロンプトの準備 prompt=""" あなたは親切なアシスタントです。英語は喋らず、日本語だけ喋ってください。 <start_of_turn>user 人生で大切なことはなんですか？<end_of_turn> <start_of_turn>model """ # 推論の実行 input_ids = tokenizer(prompt, return_tensors="pt").to(model.device) outputs = model.generate( **input_ids, max_new_tokens=128, do_sample=True, top_p=0.95, temperature=0.2, repetition_penalty=1.1, ) print(tokenizer.decode(outputs[0])) ``` ## Result ```bash <bos> あなたは親切なアシスタントです。英語は喋らず、日本語だけ喋ってください。 <start_of_turn>user 人生で大切なことはなんですか？<end_of_turn> <start_of_turn>model 人生で大切なのは、幸せになることです。<end_of_turn> <eos> ``` # Chat Templete ```bash <bos> {{system prompt}} <start_of_turn>user {{prompt}}<end_of_turn> <start_of_turn>model {{response}}<end_of_turn> <eos> ``` # Base model - free-ai-ltd/ja-aozora-wikipedia-gemmba-2b (private) # Dataset for Instruction tuning - llm-jp/databricks-dolly-15k-ja - llm-jp/oasst1-21k-ja - kunishou/oasst1-chat-44k-ja - kunishou/oasst2-chat-68k-ja - kunishou/cnn-dailymail-27k-ja - kunishou/databricks-dolly-69k-ja-en-translation - kunishou/databricks-dolly-15k-ja - shi3z/OpenOrcaJapanese # How to make this model - [LoRA](https://gist.github.com/alfredplpl/e20cad036c151f38645a1abc87f56a2f)
DhairyaSarin/promotional-text-analyser-v2	DhairyaSarin	2024-03-06T09:11:17Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "region:us" ]	null	2024-03-06T09:10:46Z	--- library_name: peft base_model: mistralai/Mistral-7B-Instruct-v0.1 --- # 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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zxhezexin/openlrm-mix-large-1.1	zxhezexin	2024-03-06T08:57:33Z	45	6	transformers	[ "transformers", "pytorch", "safetensors", "image-to-3d", "dataset:allenai/objaverse", "arxiv:2311.04400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	image-to-3d	2024-03-04T06:57:53Z	--- license: cc-by-nc-4.0 datasets: - allenai/objaverse pipeline_tag: image-to-3d --- # Model Card for OpenLRM V1.1 ## Overview - This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400). - Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases). ## Model Details - Training data \| Model \| Training Data \| \| :---: \| :---: \| \| [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) \| Objaverse \| \| [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) \| Objaverse \| \| [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) \| Objaverse \| \| [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) \| Objaverse + MVImgNet \| - Model architecture (version==1.1) \| Type \| Layers \| Feat. Dim \| Attn. Heads \| Triplane Dim. \| Input Res. \| Image Encoder \| Size \| \| :---: \| :----: \| :-------: \| :---------: \| :-----------: \| :--------: \| :---------------: \| :---: \| \| small \| 12 \| 512 \| 8 \| 32 \| 224 \| dinov2_vits14_reg \| 446M \| \| base \| 12 \| 768 \| 12 \| 48 \| 336 \| dinov2_vitb14_reg \| 1.04G \| \| large \| 16 \| 1024 \| 16 \| 80 \| 448 \| dinov2_vitb14_reg \| 1.81G \| - Training settings \| Type \| Rend. Res. \| Rend. Patch \| Ray Samples \| \| :---: \| :--------: \| :---------: \| :---------: \| \| small \| 192 \| 64 \| 96 \| \| base \| 288 \| 96 \| 96 \| \| large \| 384 \| 128 \| 128 \| ## Notable Differences from the Original Paper - We do not use the deferred back-propagation technique in the original paper. - We used random background colors during training. - The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens. - The triplane decoder contains 4 layers in our implementation. ## License - The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). - They are provided for research purposes only, and CANNOT be used commercially. ## Disclaimer This model is an open-source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors. ### Ethical Considerations - This model should be used responsibly and ethically, and should not be used for malicious purposes. - Users should be aware of potential biases in the training data. - The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups. ### Usage Considerations - The model is provided "as is" without warranty of any kind. - Users are responsible for ensuring that their use complies with all relevant laws and regulations. - The developers and contributors of this model are not liable for any damages or losses arising from the use of this model. --- This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.
zxhezexin/openlrm-mix-small-1.1	zxhezexin	2024-03-06T08:56:32Z	31	1	transformers	[ "transformers", "pytorch", "safetensors", "image-to-3d", "dataset:allenai/objaverse", "arxiv:2311.04400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	image-to-3d	2024-03-04T07:05:06Z	--- license: cc-by-nc-4.0 datasets: - allenai/objaverse pipeline_tag: image-to-3d --- # Model Card for OpenLRM V1.1 ## Overview - This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400). - Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases). ## Model Details - Training data \| Model \| Training Data \| \| :---: \| :---: \| \| [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) \| Objaverse \| \| [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) \| Objaverse \| \| [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) \| Objaverse \| \| [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) \| Objaverse + MVImgNet \| - Model architecture (version==1.1) \| Type \| Layers \| Feat. Dim \| Attn. Heads \| Triplane Dim. \| Input Res. \| Image Encoder \| Size \| \| :---: \| :----: \| :-------: \| :---------: \| :-----------: \| :--------: \| :---------------: \| :---: \| \| small \| 12 \| 512 \| 8 \| 32 \| 224 \| dinov2_vits14_reg \| 446M \| \| base \| 12 \| 768 \| 12 \| 48 \| 336 \| dinov2_vitb14_reg \| 1.04G \| \| large \| 16 \| 1024 \| 16 \| 80 \| 448 \| dinov2_vitb14_reg \| 1.81G \| - Training settings \| Type \| Rend. Res. \| Rend. Patch \| Ray Samples \| \| :---: \| :--------: \| :---------: \| :---------: \| \| small \| 192 \| 64 \| 96 \| \| base \| 288 \| 96 \| 96 \| \| large \| 384 \| 128 \| 128 \| ## Notable Differences from the Original Paper - We do not use the deferred back-propagation technique in the original paper. - We used random background colors during training. - The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens. - The triplane decoder contains 4 layers in our implementation. ## License - The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). - They are provided for research purposes only, and CANNOT be used commercially. ## Disclaimer This model is an open-source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors. ### Ethical Considerations - This model should be used responsibly and ethically, and should not be used for malicious purposes. - Users should be aware of potential biases in the training data. - The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups. ### Usage Considerations - The model is provided "as is" without warranty of any kind. - Users are responsible for ensuring that their use complies with all relevant laws and regulations. - The developers and contributors of this model are not liable for any damages or losses arising from the use of this model. --- This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.
zxhezexin/openlrm-obj-large-1.1	zxhezexin	2024-03-06T08:56:16Z	22	1	transformers	[ "transformers", "pytorch", "safetensors", "image-to-3d", "dataset:allenai/objaverse", "arxiv:2311.04400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	image-to-3d	2024-03-04T06:42:12Z	--- license: cc-by-nc-4.0 datasets: - allenai/objaverse pipeline_tag: image-to-3d --- # Model Card for OpenLRM V1.1 ## Overview - This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400). - Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases). ## Model Details - Training data \| Model \| Training Data \| \| :---: \| :---: \| \| [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) \| Objaverse \| \| [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) \| Objaverse \| \| [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) \| Objaverse \| \| [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) \| Objaverse + MVImgNet \| - Model architecture (version==1.1) \| Type \| Layers \| Feat. Dim \| Attn. Heads \| Triplane Dim. \| Input Res. \| Image Encoder \| Size \| \| :---: \| :----: \| :-------: \| :---------: \| :-----------: \| :--------: \| :---------------: \| :---: \| \| small \| 12 \| 512 \| 8 \| 32 \| 224 \| dinov2_vits14_reg \| 446M \| \| base \| 12 \| 768 \| 12 \| 48 \| 336 \| dinov2_vitb14_reg \| 1.04G \| \| large \| 16 \| 1024 \| 16 \| 80 \| 448 \| dinov2_vitb14_reg \| 1.81G \| - Training settings \| Type \| Rend. Res. \| Rend. Patch \| Ray Samples \| \| :---: \| :--------: \| :---------: \| :---------: \| \| small \| 192 \| 64 \| 96 \| \| base \| 288 \| 96 \| 96 \| \| large \| 384 \| 128 \| 128 \| ## Notable Differences from the Original Paper - We do not use the deferred back-propagation technique in the original paper. - We used random background colors during training. - The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens. - The triplane decoder contains 4 layers in our implementation. ## License - The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). - They are provided for research purposes only, and CANNOT be used commercially. ## Disclaimer This model is an open-source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors. ### Ethical Considerations - This model should be used responsibly and ethically, and should not be used for malicious purposes. - Users should be aware of potential biases in the training data. - The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups. ### Usage Considerations - The model is provided "as is" without warranty of any kind. - Users are responsible for ensuring that their use complies with all relevant laws and regulations. - The developers and contributors of this model are not liable for any damages or losses arising from the use of this model. --- This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.
zxhezexin/openlrm-obj-small-1.1	zxhezexin	2024-03-06T08:54:51Z	50	1	transformers	[ "transformers", "pytorch", "safetensors", "image-to-3d", "dataset:allenai/objaverse", "arxiv:2311.04400", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	image-to-3d	2024-03-04T06:35:29Z	--- license: cc-by-nc-4.0 datasets: - allenai/objaverse pipeline_tag: image-to-3d --- # Model Card for OpenLRM V1.1 ## Overview - This model card is for the [OpenLRM](https://github.com/3DTopia/OpenLRM) project, which is an open-source implementation of the paper [LRM](https://arxiv.org/abs/2311.04400). - Information contained in this model card corresponds to [Version 1.1](https://github.com/3DTopia/OpenLRM/releases). ## Model Details - Training data \| Model \| Training Data \| \| :---: \| :---: \| \| [openlrm-obj-small-1.1](https://huggingface.co/zxhezexin/openlrm-obj-small-1.1) \| Objaverse \| \| [openlrm-obj-base-1.1](https://huggingface.co/zxhezexin/openlrm-obj-base-1.1) \| Objaverse \| \| [openlrm-obj-large-1.1](https://huggingface.co/zxhezexin/openlrm-obj-large-1.1) \| Objaverse \| \| [openlrm-mix-small-1.1](https://huggingface.co/zxhezexin/openlrm-mix-small-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-base-1.1](https://huggingface.co/zxhezexin/openlrm-mix-base-1.1) \| Objaverse + MVImgNet \| \| [openlrm-mix-large-1.1](https://huggingface.co/zxhezexin/openlrm-mix-large-1.1) \| Objaverse + MVImgNet \| - Model architecture (version==1.1) \| Type \| Layers \| Feat. Dim \| Attn. Heads \| Triplane Dim. \| Input Res. \| Image Encoder \| Size \| \| :---: \| :----: \| :-------: \| :---------: \| :-----------: \| :--------: \| :---------------: \| :---: \| \| small \| 12 \| 512 \| 8 \| 32 \| 224 \| dinov2_vits14_reg \| 446M \| \| base \| 12 \| 768 \| 12 \| 48 \| 336 \| dinov2_vitb14_reg \| 1.04G \| \| large \| 16 \| 1024 \| 16 \| 80 \| 448 \| dinov2_vitb14_reg \| 1.81G \| - Training settings \| Type \| Rend. Res. \| Rend. Patch \| Ray Samples \| \| :---: \| :--------: \| :---------: \| :---------: \| \| small \| 192 \| 64 \| 96 \| \| base \| 288 \| 96 \| 96 \| \| large \| 384 \| 128 \| 128 \| ## Notable Differences from the Original Paper - We do not use the deferred back-propagation technique in the original paper. - We used random background colors during training. - The image encoder is based on the [DINOv2](https://github.com/facebookresearch/dinov2) model with register tokens. - The triplane decoder contains 4 layers in our implementation. ## License - The model weights are released under the [Creative Commons Attribution-NonCommercial 4.0 International License](LICENSE_WEIGHT). - They are provided for research purposes only, and CANNOT be used commercially. ## Disclaimer This model is an open-source implementation and is NOT the official release of the original research paper. While it aims to reproduce the original results as faithfully as possible, there may be variations due to model implementation, training data, and other factors. ### Ethical Considerations - This model should be used responsibly and ethically, and should not be used for malicious purposes. - Users should be aware of potential biases in the training data. - The model should not be used under the circumstances that could lead to harm or unfair treatment of individuals or groups. ### Usage Considerations - The model is provided "as is" without warranty of any kind. - Users are responsible for ensuring that their use complies with all relevant laws and regulations. - The developers and contributors of this model are not liable for any damages or losses arising from the use of this model. --- This model card is subject to updates and modifications. Users are advised to check for the latest version regularly.
ingeol/q2d	ingeol	2024-03-06T08:52:16Z	4	0	sentence-transformers	[ "sentence-transformers", "safetensors", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-03-06T08:50:58Z	--- library_name: sentence-transformers pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # ingeol/q2d This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('ingeol/q2d') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('ingeol/q2d') model = AutoModel.from_pretrained('ingeol/q2d') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=ingeol/q2d) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 7797 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `beir.losses.bpr_loss.BPRLoss` Parameters of the fit()-Method: ``` { "epochs": 3, "evaluation_steps": 7000, "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "correct_bias": false, "eps": 1e-06, "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1000, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
hwkwon/S-SOLAR-10.7B-v1.1	hwkwon	2024-03-06T08:50:16Z	2,258	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "ko", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T08:34:50Z	--- license: cc-by-nc-4.0 language: - ko --- # S-SOLAR-10.7B <!-- Provide a quick summary of what the model is/does. --> <!--This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).--> ### Model Description <!-- Provide a longer summary of what this model is. --> This model is a fine-tuned version of [yanolja/EEVE-Korean-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0). ### Trained Data TBA ### Prompt Template ``` ### User: User query input ### Assistant: ``` ### License This model is licensed under the cc-by-nc-4.0. which allows others to share and adapt the model for non-commercial purposes.
aryachakraborty/DeepSeek-1.3B-IT-NL-SQL-V2	aryachakraborty	2024-03-06T08:49:27Z	13	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T08:47:29Z	--- library_name: transformers tags: [] --- # 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. This model card has been automatically generated. - 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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(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]
VinitRuparelia/mountain	VinitRuparelia	2024-03-06T08:47:03Z	0	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T08:40:23Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### Mountain Dreambooth model trained by VinitRuparelia following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: RGIT_669 Sample pictures of this concept: ![0](https://huggingface.co/VinitRuparelia/mountain/resolve/main/sample_images/WhatsApp_Image_2024-03-06_at_1.50.14_PM.jpeg) ![1](https://huggingface.co/VinitRuparelia/mountain/resolve/main/sample_images/WhatsApp_Image_2024-03-06_at_1.50.15_PM.jpeg)
AlignmentResearch/robust_llm_z685n973_from_EleutherAI_pythia-14m	AlignmentResearch	2024-03-06T08:44:50Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt_neox", "text-classification", "generated_from_trainer", "base_model:EleutherAI/pythia-14m", "base_model:finetune:EleutherAI/pythia-14m", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T08:44:44Z	--- tags: - generated_from_trainer base_model: EleutherAI/pythia-14m model-index: - name: robust_llm_z685n973_from_EleutherAI_pythia-14m results: [] --- <!-- 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. --> # robust_llm_z685n973_from_EleutherAI_pythia-14m This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) 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: 1e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 2 - total_eval_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.2
minhah/videomae-base-finetuned-ucf101-subset-finetuned-elder-UFC-prtuned	minhah	2024-03-06T08:43:17Z	4	0	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "generated_from_trainer", "base_model:minhah/videomae-base-finetuned-ucf101-subset", "base_model:finetune:minhah/videomae-base-finetuned-ucf101-subset", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	video-classification	2024-03-06T07:10:58Z	--- license: cc-by-nc-4.0 base_model: minhah/videomae-base-finetuned-ucf101-subset tags: - generated_from_trainer metrics: - accuracy model-index: - name: videomae-base-finetuned-ucf101-subset-finetuned-elder-UFC-prtuned results: [] --- <!-- 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. --> # videomae-base-finetuned-ucf101-subset-finetuned-elder-UFC-prtuned This model is a fine-tuned version of [minhah/videomae-base-finetuned-ucf101-subset](https://huggingface.co/minhah/videomae-base-finetuned-ucf101-subset) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6593 - Accuracy: 0.3481 ## 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: 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 - training_steps: 576 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.729 \| 0.13 \| 73 \| 1.6346 \| 0.3408 \| \| 1.683 \| 1.13 \| 146 \| 1.6505 \| 0.3029 \| \| 1.6889 \| 2.13 \| 219 \| 1.6359 \| 0.3408 \| \| 1.6853 \| 3.13 \| 292 \| 1.6739 \| 0.2398 \| \| 1.5793 \| 4.13 \| 365 \| 1.6679 \| 0.2588 \| \| 1.5783 \| 5.13 \| 438 \| 1.6091 \| 0.3324 \| \| 1.5745 \| 6.13 \| 511 \| 1.6306 \| 0.3072 \| \| 1.5704 \| 7.11 \| 576 \| 1.6573 \| 0.2707 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
YMKiii/output	YMKiii	2024-03-06T08:40:26Z	1	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "stable-diffusion", "stable-diffusion-diffusers", "base_model:CompVis/stable-diffusion-v1-4", "base_model:finetune:CompVis/stable-diffusion-v1-4", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-02-28T07:39:24Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - stable-diffusion - stable-diffusion-diffusers base_model: CompVis/stable-diffusion-v1-4 inference: true instance_prompt: Interior design --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - YMKiii/output This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on Interior design using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
Litzy619/V0305P2	Litzy619	2024-03-06T08:39:00Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-03-06T02:27:50Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0305P2 results: [] --- <!-- 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. --> # V0305P2 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0602 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3061 \| 0.09 \| 10 \| 0.1617 \| \| 0.1712 \| 0.17 \| 20 \| 0.1558 \| \| 0.1564 \| 0.26 \| 30 \| 0.1535 \| \| 0.1526 \| 0.34 \| 40 \| 0.1479 \| \| 0.1503 \| 0.43 \| 50 \| 0.1506 \| \| 0.1563 \| 0.51 \| 60 \| 0.1505 \| \| 0.1517 \| 0.6 \| 70 \| 0.1507 \| \| 0.1533 \| 0.68 \| 80 \| 0.1489 \| \| 0.1491 \| 0.77 \| 90 \| 0.1488 \| \| 0.1523 \| 0.85 \| 100 \| 0.1471 \| \| 0.1522 \| 0.94 \| 110 \| 0.1433 \| \| 0.1381 \| 1.02 \| 120 \| 0.1229 \| \| 0.1303 \| 1.11 \| 130 \| 0.1206 \| \| 0.1155 \| 1.19 \| 140 \| 0.1018 \| \| 0.1095 \| 1.28 \| 150 \| 0.0933 \| \| 0.103 \| 1.37 \| 160 \| 0.0906 \| \| 0.1007 \| 1.45 \| 170 \| 0.0904 \| \| 0.0895 \| 1.54 \| 180 \| 0.0887 \| \| 0.0914 \| 1.62 \| 190 \| 0.0840 \| \| 0.0943 \| 1.71 \| 200 \| 0.0808 \| \| 0.0938 \| 1.79 \| 210 \| 0.0757 \| \| 0.0884 \| 1.88 \| 220 \| 0.0666 \| \| 0.0862 \| 1.96 \| 230 \| 0.0733 \| \| 0.0709 \| 2.05 \| 240 \| 0.0748 \| \| 0.0601 \| 2.13 \| 250 \| 0.0730 \| \| 0.0593 \| 2.22 \| 260 \| 0.0632 \| \| 0.059 \| 2.3 \| 270 \| 0.0757 \| \| 0.06 \| 2.39 \| 280 \| 0.0620 \| \| 0.0647 \| 2.47 \| 290 \| 0.0605 \| \| 0.0619 \| 2.56 \| 300 \| 0.0624 \| \| 0.0651 \| 2.65 \| 310 \| 0.0605 \| \| 0.0578 \| 2.73 \| 320 \| 0.0597 \| \| 0.0585 \| 2.82 \| 330 \| 0.0598 \| \| 0.0575 \| 2.9 \| 340 \| 0.0601 \| \| 0.0566 \| 2.99 \| 350 \| 0.0602 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
skyisblueandgreen4/dogbooth	skyisblueandgreen4	2024-03-06T08:32:31Z	2	1	diffusers	[ "diffusers", "tensorboard", "safetensors", "text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers", "base_model:stabilityai/stable-diffusion-2-1", "base_model:finetune:stabilityai/stable-diffusion-2-1", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-05T07:44:12Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers - text-to-image - dreambooth - diffusers-training - stable-diffusion - stable-diffusion-diffusers base_model: stabilityai/stable-diffusion-2-1 inference: true instance_prompt: a photo of [v]dog --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # DreamBooth - skyisblueandgreen4/dogbooth This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. DreamBooth for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
AnonymousSub/FPDM_bertlarge_model	AnonymousSub	2024-03-06T08:32:26Z	4	0	transformers	[ "transformers", "safetensors", "bert", "feature-extraction", "arxiv:1910.09700", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2024-03-06T08:30:58Z	--- library_name: transformers tags: [] --- # 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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amazingvince/bitllama-goodwiki	amazingvince	2024-03-06T08:26:31Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "dataset:BEE-spoke-data/goodwiki-deduped-split", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T01:57:01Z	--- tags: - generated_from_trainer datasets: - BEE-spoke-data/goodwiki-deduped-split metrics: - accuracy model-index: - name: bitllama-goodwiki results: - task: name: Causal Language Modeling type: text-generation dataset: name: BEE-spoke-data/goodwiki-deduped-split type: BEE-spoke-data/goodwiki-deduped-split metrics: - name: Accuracy type: accuracy value: 0.4285134482793542 --- <!-- 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. --> # bitllama-goodwiki This model was trained from scratch on the BEE-spoke-data/goodwiki-deduped-split dataset. It achieves the following results on the evaluation set: - Loss: 3.0525 - Accuracy: 0.4285 ## 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: 0.0008 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 6.1199 \| 0.04 \| 100 \| 6.0749 \| 0.1542 \| \| 5.3869 \| 0.07 \| 200 \| 5.3267 \| 0.2032 \| \| 4.9187 \| 0.11 \| 300 \| 4.8566 \| 0.2386 \| \| 4.6185 \| 0.14 \| 400 \| 4.5535 \| 0.2624 \| \| 4.3509 \| 0.18 \| 500 \| 4.3388 \| 0.2801 \| \| 4.1666 \| 0.21 \| 600 \| 4.1692 \| 0.2956 \| \| 4.0456 \| 0.25 \| 700 \| 4.0399 \| 0.3089 \| \| 3.9273 \| 0.28 \| 800 \| 3.9318 \| 0.3193 \| \| 3.8447 \| 0.32 \| 900 \| 3.8173 \| 0.3327 \| \| 3.7143 \| 0.35 \| 1000 \| 3.7108 \| 0.3461 \| \| 3.6485 \| 0.39 \| 1100 \| 3.6116 \| 0.3590 \| \| 3.5171 \| 0.42 \| 1200 \| 3.5303 \| 0.3693 \| \| 3.4464 \| 0.46 \| 1300 \| 3.4554 \| 0.3780 \| \| 3.3955 \| 0.49 \| 1400 \| 3.3999 \| 0.3851 \| \| 3.3551 \| 0.53 \| 1500 \| 3.3432 \| 0.3919 \| \| 3.2787 \| 0.56 \| 1600 \| 3.2981 \| 0.3974 \| \| 3.2705 \| 0.6 \| 1700 \| 3.2566 \| 0.4023 \| \| 3.2281 \| 0.64 \| 1800 \| 3.2172 \| 0.4075 \| \| 3.1759 \| 0.67 \| 1900 \| 3.1826 \| 0.4118 \| \| 3.1603 \| 0.71 \| 2000 \| 3.1547 \| 0.4152 \| \| 3.1328 \| 0.74 \| 2100 \| 3.1283 \| 0.4186 \| \| 3.0916 \| 0.78 \| 2200 \| 3.1055 \| 0.4215 \| \| 3.0939 \| 0.81 \| 2300 \| 3.0875 \| 0.4238 \| \| 3.0584 \| 0.85 \| 2400 \| 3.0732 \| 0.4257 \| \| 3.0711 \| 0.88 \| 2500 \| 3.0631 \| 0.4271 \| \| 3.0612 \| 0.92 \| 2600 \| 3.0565 \| 0.4280 \| \| 3.081 \| 0.95 \| 2700 \| 3.0534 \| 0.4284 \| \| 3.0378 \| 0.99 \| 2800 \| 3.0525 \| 0.4285 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.0
aditya11997/test_prior	aditya11997	2024-03-06T08:22:33Z	3	0	diffusers	[ "diffusers", "tensorboard", "safetensors", "kandinsky", "text-to-image", "diffusers-training", "dataset:kbharat7/DogChestXrayDatasetNew", "base_model:kandinsky-community/kandinsky-2-2-prior", "base_model:finetune:kandinsky-community/kandinsky-2-2-prior", "license:creativeml-openrail-m", "diffusers:KandinskyV22PriorPipeline", "region:us" ]	text-to-image	2024-03-06T07:52:38Z	--- license: creativeml-openrail-m base_model: kandinsky-community/kandinsky-2-2-prior datasets: - kbharat7/DogChestXrayDatasetNew tags: - kandinsky - text-to-image - diffusers - diffusers-training inference: true --- # Finetuning - aditya11997/test_prior This pipeline was finetuned from kandinsky-community/kandinsky-2-2-prior on the kbharat7/DogChestXrayDatasetNew dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['dogxraysmall']: ![val_imgs_grid](./val_imgs_grid.png) ## Pipeline usage You can use the pipeline like so: ```python from diffusers import DiffusionPipeline import torch pipe_prior = DiffusionPipeline.from_pretrained("aditya11997/test_prior", torch_dtype=torch.float16) pipe_t2i = DiffusionPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16) prompt = "dogxraysmall" image_embeds, negative_image_embeds = pipe_prior(prompt, guidance_scale=1.0).to_tuple() image = pipe_t2i(image_embeds=image_embeds, negative_image_embeds=negative_image_embeds).images[0] image.save("my_image.png") ``` ## Training info These are the key hyperparameters used during training: * Epochs: 1 * Learning rate: 1e-05 * Batch size: 1 * Gradient accumulation steps: 4 * Image resolution: 768 * Mixed-precision: None More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/aditya11997/text2image-fine-tune/runs/9j7m0fr8).
Dangurangu/my-awesome-setfit-model	Dangurangu	2024-03-06T07:54:55Z	6	0	setfit	[ "setfit", "safetensors", "mpnet", "sentence-transformers", "text-classification", "generated_from_setfit_trainer", "dataset:SetFit/SentEval-CR", "arxiv:2209.11055", "base_model:sentence-transformers/paraphrase-mpnet-base-v2", "base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2", "model-index", "region:us" ]	text-classification	2024-03-06T07:54:02Z	--- library_name: setfit tags: - setfit - sentence-transformers - text-classification - generated_from_setfit_trainer datasets: - SetFit/SentEval-CR metrics: - accuracy widget: - text: you can take pic of your friends and the picture will pop up when they call . - text: the speakerphone , the radio , all features work perfectly . - text: 'a ) the picture quality ( color and sharpness of focusing ) are so great , it completely eliminated my doubt about digital imaging -- - how could one eat rice one grain at a time : - ) )' - text: so far the dvd works so i hope it does n 't break down like the reviews i 've read . - text: i have a couple hundred contacts and the menu loads within a few seconds , no big deal . pipeline_tag: text-classification inference: true base_model: sentence-transformers/paraphrase-mpnet-base-v2 model-index: - name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2 results: - task: type: text-classification name: Text Classification dataset: name: SetFit/SentEval-CR type: SetFit/SentEval-CR split: test metrics: - type: accuracy value: 0.8804780876494024 name: Accuracy --- # SetFit with sentence-transformers/paraphrase-mpnet-base-v2 This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) - Classification head: a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes - Training Dataset: [SetFit/SentEval-CR](https://huggingface.co/datasets/SetFit/SentEval-CR) <!-- - Language: Unknown --> <!-- - License: Unknown --> ### Model Sources - Repository: [SetFit on GitHub](https://github.com/huggingface/setfit) - Paper: [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - Blogpost: [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ### Model Labels \| Label \| Examples \| \|:------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| 1 \| <ul><li>'* slick-looking design and improved interface'</li><li>'as for bluetooth , no problems at all .'</li><li>'2 ) storage capacity'</li></ul> \| \| 0 \| <ul><li>"the day finally arrived when i was sure i 'd leave sprint ."</li><li>"neither message was answered ( they ask for 24 hours before replying - i 've been waiting 27 days . )"</li><li>'only problem is that is a bit heavy .'</li></ul> \| ## Evaluation ### Metrics \| Label \| Accuracy \| \|:--------\|:---------\| \| all \| 0.8805 \| ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("dangurangu/my-awesome-setfit-model") # Run inference preds = model("the speakerphone , the radio , all features work perfectly .") ``` <!-- ### Downstream Use List how someone could finetune this model on their own dataset. --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Set Metrics \| Training set \| Min \| Median \| Max \| \|:-------------\|:----\|:--------\|:----\| \| Word count \| 4 \| 18.0625 \| 44 \| \| Label \| Training Sample Count \| \|:------\|:----------------------\| \| 0 \| 7 \| \| 1 \| 9 \| ### Training Hyperparameters - batch_size: (16, 16) - num_epochs: (1, 1) - max_steps: -1 - sampling_strategy: oversampling - num_iterations: 20 - body_learning_rate: (2e-05, 2e-05) - head_learning_rate: 2e-05 - loss: CosineSimilarityLoss - distance_metric: cosine_distance - margin: 0.25 - end_to_end: False - use_amp: False - warmup_proportion: 0.1 - seed: 42 - eval_max_steps: -1 - load_best_model_at_end: False ### Training Results \| Epoch \| Step \| Training Loss \| Validation Loss \| \|:-----:\|:----:\|:-------------:\|:---------------:\| \| 0.025 \| 1 \| 0.2205 \| - \| ### Framework Versions - Python: 3.10.12 - SetFit: 1.0.3 - Sentence Transformers: 2.5.1 - Transformers: 4.38.1 - PyTorch: 2.1.0+cu121 - Datasets: 2.18.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
advaitadasein/blip2-opt-6.7b	advaitadasein	2024-03-06T07:51:34Z	8	1	transformers	[ "transformers", "pytorch", "blip-2", "visual-question-answering", "vision", "image-to-text", "image-captioning", "en", "arxiv:2301.12597", "license:mit", "endpoints_compatible", "region:us" ]	image-to-text	2024-03-06T07:46:50Z	--- language: en license: mit tags: - vision - image-to-text - image-captioning - visual-question-answering pipeline_tag: image-to-text inference: false --- # BLIP-2, OPT-6.7b, pre-trained only BLIP-2 model, leveraging [OPT-6.7b](https://huggingface.co/facebook/opt-6.7b) (a large language model with 6.7 billion parameters). It was introduced in the paper [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://arxiv.org/abs/2301.12597) by Li et al. and first released in [this repository](https://github.com/salesforce/LAVIS/tree/main/projects/blip2). Disclaimer: The team releasing BLIP-2 did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BLIP-2 consists of 3 models: a CLIP-like image encoder, a Querying Transformer (Q-Former) and a large language model. The authors initialize the weights of the image encoder and large language model from pre-trained checkpoints and keep them frozen while training the Querying Transformer, which is a BERT-like Transformer encoder that maps a set of "query tokens" to query embeddings, which bridge the gap between the embedding space of the image encoder and the large language model. The goal for the model is simply to predict the next text token, giving the query embeddings and the previous text. <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/blip2_architecture.jpg" alt="drawing" width="600"/> This allows the model to be used for tasks like: - image captioning - visual question answering (VQA) - chat-like conversations by feeding the image and the previous conversation as prompt to the model ## Direct Use and Downstream Use You can use the raw model for conditional text generation given an image and optional text. See the [model hub](https://huggingface.co/models?search=Salesforce/blip) to look for fine-tuned versions on a task that interests you. ## Bias, Risks, Limitations, and Ethical Considerations BLIP2-OPT uses off-the-shelf OPT as the language model. It inherits the same risks and limitations as mentioned in Meta's model card. > Like other large language models for which the diversity (or lack thereof) of training > data induces downstream impact on the quality of our model, OPT-175B has limitations in terms > of bias and safety. OPT-175B can also have quality issues in terms of generation diversity and > hallucination. In general, OPT-175B is not immune from the plethora of issues that plague modern > large language models. > BLIP2 is fine-tuned on image-text datasets (e.g. [LAION](https://laion.ai/blog/laion-400-open-dataset/) ) collected from the internet. As a result the model itself is potentially vulnerable to generating equivalently inappropriate content or replicating inherent biases in the underlying data. BLIP2 has not been tested in real world applications. It should not be directly deployed in any applications. Researchers should first carefully assess the safety and fairness of the model in relation to the specific context they’re being deployed within. ### How to use For code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/blip-2#transformers.Blip2ForConditionalGeneration.forward.example).
alinerodrigues/wav2vec2-xlsr-1b-mecita-portuguese-all-clean-10	alinerodrigues	2024-03-06T07:49:28Z	1	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-06T04:33:02Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xlsr-1b-mecita-portuguese-all-clean-10 results: [] --- <!-- 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. --> # wav2vec2-xlsr-1b-mecita-portuguese-all-clean-10 This model is a fine-tuned version of [jonatasgrosman/wav2vec2-xls-r-1b-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-xls-r-1b-portuguese) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1514 - Wer: 0.0869 - Cer: 0.0287 ## 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 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 100 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\| \| 22.1247 \| 1.0 \| 67 \| 2.8766 \| 1.0 \| 1.0 \| \| 4.814 \| 2.0 \| 134 \| 0.3766 \| 0.2506 \| 0.0658 \| \| 0.8372 \| 3.0 \| 201 \| 0.2164 \| 0.1177 \| 0.0368 \| \| 0.8372 \| 4.0 \| 268 \| 0.1876 \| 0.1106 \| 0.0331 \| \| 0.294 \| 5.0 \| 335 \| 0.1951 \| 0.1011 \| 0.0325 \| \| 0.2405 \| 6.0 \| 402 \| 0.1718 \| 0.0957 \| 0.0300 \| \| 0.2405 \| 7.0 \| 469 \| 0.1647 \| 0.0947 \| 0.0296 \| \| 0.1998 \| 8.0 \| 536 \| 0.1709 \| 0.0950 \| 0.0305 \| \| 0.1946 \| 9.0 \| 603 \| 0.1730 \| 0.0906 \| 0.0299 \| \| 0.1946 \| 10.0 \| 670 \| 0.1695 \| 0.0876 \| 0.0289 \| \| 0.1938 \| 11.0 \| 737 \| 0.1649 \| 0.0852 \| 0.0282 \| \| 0.1667 \| 12.0 \| 804 \| 0.1644 \| 0.0869 \| 0.0280 \| \| 0.1667 \| 13.0 \| 871 \| 0.1534 \| 0.0842 \| 0.0275 \| \| 0.163 \| 14.0 \| 938 \| 0.1514 \| 0.0869 \| 0.0287 \| \| 0.1568 \| 15.0 \| 1005 \| 0.1583 \| 0.0873 \| 0.0287 \| \| 0.1568 \| 16.0 \| 1072 \| 0.1655 \| 0.0856 \| 0.0283 \| \| 0.1465 \| 17.0 \| 1139 \| 0.1691 \| 0.0859 \| 0.0272 \| \| 0.138 \| 18.0 \| 1206 \| 0.1777 \| 0.0906 \| 0.0290 \| \| 0.138 \| 19.0 \| 1273 \| 0.1652 \| 0.0859 \| 0.0280 \| \| 0.1251 \| 20.0 \| 1340 \| 0.1715 \| 0.0856 \| 0.0275 \| \| 0.136 \| 21.0 \| 1407 \| 0.1614 \| 0.0832 \| 0.0267 \| \| 0.136 \| 22.0 \| 1474 \| 0.1579 \| 0.0805 \| 0.0262 \| \| 0.1179 \| 23.0 \| 1541 \| 0.1777 \| 0.0842 \| 0.0277 \| \| 0.1029 \| 24.0 \| 1608 \| 0.1761 \| 0.0825 \| 0.0274 \| \| 0.1029 \| 25.0 \| 1675 \| 0.1665 \| 0.0839 \| 0.0275 \| \| 0.1139 \| 26.0 \| 1742 \| 0.1821 \| 0.0801 \| 0.0279 \| \| 0.1019 \| 27.0 \| 1809 \| 0.1807 \| 0.0856 \| 0.0279 \| \| 0.1019 \| 28.0 \| 1876 \| 0.1883 \| 0.0812 \| 0.0273 \| \| 0.0911 \| 29.0 \| 1943 \| 0.1904 \| 0.0808 \| 0.0272 \| \| 0.0919 \| 30.0 \| 2010 \| 0.1839 \| 0.0862 \| 0.0285 \| \| 0.0919 \| 31.0 \| 2077 \| 0.1902 \| 0.0852 \| 0.0282 \| \| 0.084 \| 32.0 \| 2144 \| 0.1934 \| 0.0822 \| 0.0275 \| \| 0.0809 \| 33.0 \| 2211 \| 0.2050 \| 0.0822 \| 0.0282 \| \| 0.0809 \| 34.0 \| 2278 \| 0.1955 \| 0.0832 \| 0.0281 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.13.3
DooDooHyun/AIFT-42dot_LLM-PLM-1.3B-v1.51	DooDooHyun	2024-03-06T07:43:13Z	2,249	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "generated_from_trainer", "base_model:42dot/42dot_LLM-PLM-1.3B", "base_model:finetune:42dot/42dot_LLM-PLM-1.3B", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T06:39:38Z	--- license: cc-by-nc-4.0 base_model: 42dot/42dot_LLM-PLM-1.3B tags: - generated_from_trainer model-index: - name: AIFT-42dot_LLM-PLM-1.3B-v1.51 results: [] --- # AIFT-42dot_LLM-PLM-1.3B-v1.51 This model is a fine-tuned version of [42dot/42dot_LLM-PLM-1.3B](https://huggingface.co/42dot/42dot_LLM-PLM-1.3B) 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: 2e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
ise-uiuc/Magicoder-DS-6.7B	ise-uiuc	2024-03-06T07:40:45Z	203	38	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:ise-uiuc/Magicoder-OSS-Instruct-75K", "arxiv:2312.02120", "arxiv:2305.06161", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-03T19:29:41Z	--- license: other library_name: transformers datasets: - ise-uiuc/Magicoder-OSS-Instruct-75K license_name: deepseek pipeline_tag: text-generation --- # 🎩 Magicoder: Source Code Is All You Need > Refer to our GitHub repo [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/) for an up-to-date introduction to the Magicoder family! * 🎩Magicoder is a model family empowered by 🪄OSS-Instruct, a novel approach to enlightening LLMs with open-source code snippets for generating low-bias and high-quality instruction data for code. * 🪄OSS-Instruct mitigates the inherent bias of the LLM-synthesized instruction data by empowering them with a wealth of open-source references to produce more diverse, realistic, and controllable data. ![Overview of OSS-Instruct](assets/overview.svg) ![Overview of Result](assets/result.png) ## Model Details ### Model Description * Developed by: [Yuxiang Wei](https://yuxiang.cs.illinois.edu), [Zhe Wang](https://github.com/zhewang2001), [Jiawei Liu](https://jiawei-site.github.io), [Yifeng Ding](https://yifeng-ding.com), [Lingming Zhang](https://lingming.cs.illinois.edu) * License: [DeepSeek](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL) * Finetuned from model: [deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base) ### Model Sources * Repository: <https://github.com/ise-uiuc/magicoder> * Paper: <https://arxiv.org/abs/2312.02120> * Demo (powered by [Gradio](https://www.gradio.app)): <https://github.com/ise-uiuc/magicoder/tree/main/demo> ### Training Data * [Magicoder-OSS-Instruct-75K](https://huggingface.co/datasets/ise-uiuc/Magicoder_oss_instruct_75k): generated through OSS-Instruct using `gpt-3.5-turbo-1106` and used to train both Magicoder and Magicoder-S series. ## Uses ### Direct Use Magicoders are designed and best suited for coding tasks. ### Out-of-Scope Use Magicoders may not work well in non-coding tasks. ## Bias, Risks, and Limitations Magicoders may sometimes make errors, producing misleading contents, or struggle to manage tasks that are not related to coding. ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. ## How to Get Started with the Model Use the code below to get started with the model. Make sure you installed the [transformers](https://huggingface.co/docs/transformers/index) library. ```python from transformers import pipeline import torch MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions. @@ Instruction {instruction} @@ Response """ instruction = <Your code instruction here> prompt = MAGICODER_PROMPT.format(instruction=instruction) generator = pipeline( model="ise-uiuc/Magicoder-DS-6.7B", task="text-generation", torch_dtype=torch.bfloat16, device_map="auto", ) result = generator(prompt, max_length=1024, num_return_sequences=1, temperature=0.0) print(result[0]["generated_text"]) ``` ## Technical Details Refer to our GitHub repo: [ise-uiuc/magicoder](https://github.com/ise-uiuc/magicoder/). ## 📝 Citation ```bibtex @misc{magicoder, title={Magicoder: Source Code Is All You Need}, author={Yuxiang Wei and Zhe Wang and Jiawei Liu and Yifeng Ding and Lingming Zhang}, year={2023}, eprint={2312.02120}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## 🙏 Acknowledgements * [WizardCoder](https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder): Evol-Instruct * [DeepSeek-Coder](https://github.com/deepseek-ai/DeepSeek-Coder): Base model for Magicoder-DS * [CodeLlama](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/): Base model for Magicoder-CL * [StarCoder](https://arxiv.org/abs/2305.06161): Data decontamination ## Important Note Magicoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets. Magicoders will not compete with OpenAI's commercial products.
ITT-AF/ITT-42dot_LLM-PLM-1.3B-v6.0	ITT-AF	2024-03-06T07:40:07Z	60	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "license:cc-by-nc-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T06:35:07Z	--- license: cc-by-nc-4.0 --- # ITT-AF/ITT-42dot_LLM-PLM-1.3B-v6.0 This model is a fine-tuned version of [42dot/42dot_LLM-PLM-1.3B](https://huggingface.co/42dot/42dot_LLM-PLM-1.3B) on an custom 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: 24 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.0.0 - Tokenizers 0.15.0
ychu612/BERTopic_vafn	ychu612	2024-03-06T07:33:20Z	5	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-03-06T01:30:23Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_vafn This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. ## Usage To use this model, please install BERTopic: ``` pip install -U bertopic ``` You can use the model as follows: ```python from bertopic import BERTopic topic_model = BERTopic.load("ychu612/BERTopic_vafn") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 3 * Number of training documents: 103 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| -1 \| the - was - she - and - to \| 15 \| -1_the_was_she_and \| \| 0 \| the - she - was - and - her \| 55 \| 0_the_she_was_and \| \| 1 \| the - was - he - and - to \| 33 \| 1_the_was_he_and \| </details> ## Training hyperparameters * calculate_probabilities: False * language: english * low_memory: False * min_topic_size: 10 * n_gram_range: (1, 1) * nr_topics: None * seed_topic_list: None * top_n_words: 10 * verbose: False * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.0 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 2.1.4 * Scikit-Learn: 1.1.0 * Sentence-transformers: 2.3.1 * Transformers: 4.38.1 * Numba: 0.56.4 * Plotly: 5.9.0 * Python: 3.10.9
AbstractPerspective/Phi-2_MoE_GDPR	AbstractPerspective	2024-03-06T07:33:01Z	4	0	transformers	[ "transformers", "safetensors", "phi", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T07:30:00Z	--- library_name: transformers tags: [] --- # 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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anum231/food_classifier	anum231	2024-03-06T07:26:58Z	46	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "base_model:anum231/cancer_classifier_100", "base_model:finetune:anum231/cancer_classifier_100", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-01-27T05:41:37Z	--- license: apache-2.0 base_model: anum231/cancer_classifier_100 tags: - generated_from_keras_callback model-index: - name: anum231/food_classifier results: [] --- <!-- 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. --> # anum231/food_classifier This model is a fine-tuned version of [anum231/cancer_classifier_100](https://huggingface.co/anum231/cancer_classifier_100) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5815 - Validation Loss: 0.4561 - Train Accuracy: 0.8276 - Epoch: 4 ## 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': 1160, '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 \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.6210 \| 0.4706 \| 0.8276 \| 0 \| \| 0.6095 \| 0.4583 \| 0.8103 \| 1 \| \| 0.6289 \| 0.4566 \| 0.8103 \| 2 \| \| 0.6230 \| 0.5850 \| 0.7241 \| 3 \| \| 0.5815 \| 0.4561 \| 0.8276 \| 4 \| ### Framework versions - Transformers 4.38.1 - TensorFlow 2.15.0 - Datasets 2.18.0 - Tokenizers 0.15.2
light77/gemma-Code-Instruct-Finetune-test-0.3	light77	2024-03-06T07:19:01Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T07:15:39Z	--- library_name: transformers tags: [] --- # 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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Kunger/Sakura-13B-LNovel-v0.9-4bit-AWQ	Kunger	2024-03-06T07:17:23Z	4	0	transformers	[ "transformers", "safetensors", "qwen", "text-generation", "custom_code", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-01T11:13:42Z	--- license: cc-by-nc-sa-4.0 --- 原始模型：`https://huggingface.co/SakuraLLM/Sakura-13B-LNovel-v0.9` 4Bit AWQ量化，未测试，不建议使用。
Kunger/Sakura-13B-Qwen2beta-v0.9-GGUF	Kunger	2024-03-06T07:17:15Z	12	0	null	[ "gguf", "license:cc-by-nc-sa-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-03-06T06:23:57Z	--- license: cc-by-nc-sa-4.0 --- 原始模型：`https://huggingface.co/SakuraLLM/Sakura-13B-Qwen2beta-v0.9` LLAMA.CPP直接转换，未经测试
Kunger/Sakura-13B-Qwen2beta-v0.9-4bit-AWQ	Kunger	2024-03-06T07:17:01Z	5	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-01T12:13:21Z	--- license: cc-by-nc-sa-4.0 --- 原始模型：`https://huggingface.co/SakuraLLM/Sakura-13B-Qwen2beta-v0.9` 4Bit AWQ量化，未测试，不建议使用。
eunyounglee/degreemotion-bert-finetuning-3	eunyounglee	2024-03-06T07:16:27Z	89	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T06:44:00Z	--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer model-index: - name: degreemotion-bert-finetuning-3 results: [] --- <!-- 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. --> # degreemotion-bert-finetuning-3 This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) 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: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.2.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
Hadiboo/boguey	Hadiboo	2024-03-06T07:16:09Z	0	0	adapter-transformers	[ "adapter-transformers", "code", "art", "text-generation-inference", "text-generation", "en", "dataset:HuggingFaceTB/cosmopedia", "region:us" ]	text-generation	2024-03-06T07:13:10Z	--- datasets: - HuggingFaceTB/cosmopedia language: - en library_name: adapter-transformers pipeline_tag: text-generation tags: - code - art - text-generation-inference ---
Sumail/Golden_Waves04_2b	Sumail	2024-03-06T07:13:37Z	4	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:Sumail/Bubble_bee04_2b", "base_model:finetune:Sumail/Bubble_bee04_2b", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T06:42:38Z	--- base_model: - 0x0dad0/nous_nb00 - Sumail/Bubble_bee04_2b library_name: transformers tags: - mergekit - merge --- # 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: * [0x0dad0/nous_nb00](https://huggingface.co/0x0dad0/nous_nb00) * [Sumail/Bubble_bee04_2b](https://huggingface.co/Sumail/Bubble_bee04_2b) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: 0x0dad0/nous_nb00 layer_range: [0, 18] - model: Sumail/Bubble_bee04_2b layer_range: [0, 18] merge_method: slerp base_model: 0x0dad0/nous_nb00 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 ```
yuchiz/models	yuchiz	2024-03-06T07:11:44Z	4	0	transformers	[ "transformers", "pytorch", "safetensors", "trl", "ppo", "reinforcement-learning", "license:apache-2.0", "endpoints_compatible", "region:us" ]	reinforcement-learning	2024-03-06T07:11:40Z	--- license: apache-2.0 tags: - trl - ppo - transformers - reinforcement-learning --- # TRL Model This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to guide the model outputs according to a value, function, or human feedback. The model can be used for text generation. ## Usage To use this model for inference, first install the TRL library: ```bash python -m pip install trl ``` You can then generate text as follows: ```python from transformers import pipeline generator = pipeline("text-generation", model="yuchiz//tmp/tmpbswj31mg/yuchiz/models") outputs = generator("Hello, my llama is cute") ``` If you want to use the model for training or to obtain the outputs from the value head, load the model as follows: ```python from transformers import AutoTokenizer from trl import AutoModelForCausalLMWithValueHead tokenizer = AutoTokenizer.from_pretrained("yuchiz//tmp/tmpbswj31mg/yuchiz/models") model = AutoModelForCausalLMWithValueHead.from_pretrained("yuchiz//tmp/tmpbswj31mg/yuchiz/models") inputs = tokenizer("Hello, my llama is cute", return_tensors="pt") outputs = model(**inputs, labels=inputs["input_ids"]) ```
hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling	hiyouga	2024-03-06T07:10:29Z	33	8	peft	[ "peft", "safetensors", "llama-factory", "lora", "generated_from_trainer", "text-generation", "conversational", "en", "dataset:vicgalle/alpaca-gpt4", "dataset:glaiveai/glaive-function-calling-v2", "dataset:hiyouga/glaive-function-calling-v2-sharegpt", "base_model:ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf", "base_model:adapter:ISTA-DASLab/Llama-2-70b-AQLM-2Bit-1x16-hf", "license:llama2", "region:us" ]	text-generation	2024-03-05T03:22:32Z	--- license: llama2 library_name: peft tags: - llama-factory - lora - generated_from_trainer base_model: BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf inference: false model-index: - name: llama2_70b_aqlm_toolcall results: [] datasets: - vicgalle/alpaca-gpt4 - glaiveai/glaive-function-calling-v2 - hiyouga/glaive-function-calling-v2-sharegpt language: - en pipeline_tag: text-generation --- <!-- 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. --> # LLaMA-2 70B AQLM 2-bit QLoRA with function calling This model is fine-tuned from [BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf](https://huggingface.co/BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf) using [LLaMA Factory](https://github.com/hiyouga/LLaMA-Factory). The maximum GPU usage during training is 24GB, and the model has preliminary conversation and tool-using abilities. It requires at least 20GB GRAM at inference. ![examples](examples.png) ## Training and evaluation data This model is fine-tuned using 2,000 examples of the Alpaca-GPT4 and Glaive-function-calling-v2 datasets. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer from peft import PeftModel tokenizer = AutoTokenizer.from_pretrained("hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling") model = AutoModelForCausalLM.from_pretrained("BlackSamorez/Llama-2-70b-AQLM-2Bit-1x16-hf", torch_dtype="auto", device_map="auto") model = PeftModel.from_pretrained(model, "hiyouga/Llama-2-70b-AQLM-2Bit-QLoRA-function-calling") streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) messages = [ { "role": "system", "content": ( "You have access to the following tools:\n" "> Tool Name: get_current_weather\nTool Description: Get the current weather in a given location\n" "Tool Args:\n" " - location (string, required): The city and state, e.g. San Francisco, CA\n" " - unit (string): should be one of [\"celsius\", \"fahrenheit\"]\n\n" "Use the following format if using a tool:\n" "```\n" "Action: tool name (one of [get_current_weather]).\n" "Action Input: the input to the tool, in a JSON format representing the kwargs " "(e.g. ```{\"input\": \"hello world\", \"num_beams\": 5}```).\n" "```\n" ) }, {"role": "user", "content": "What is the weather like in Boston?"} ] inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") inputs = inputs.to("cuda") generate_ids = model.generate(inputs, streamer=streamer) ``` ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0 - mixed_precision_training: Native AMP ### Training results ![loss](train_loss.png) ### Benchmark results \| MMLU Benchmark \| Bits \| Metric \| Accurary \| \| --------------- \| ---- \| ------------- \| -------- \| \| Average \| 2 \| 5-shot, top-1 \| 62.38 \| \| STEM \| 2 \| 5-shot, top-1 \| 51.57 \| \| Social Sciences \| 2 \| 5-shot, top-1 \| 73.44 \| \| Humanities \| 2 \| 5-shot, top-1 \| 57.82 \| \| Other \| 2 \| 5-shot, top-1 \| 68.56 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.39.0.dev0 - Pytorch 2.2.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.2
ottopilot/PriyaBelleXL	ottopilot	2024-03-06T07:09:25Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:cc-by-nc-nd-4.0", "region:us" ]	text-to-image	2024-03-06T07:07:58Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- RAW photo, portrait, close-up, PriBlle, looking at viewer, smiling, perfect black hair with highlights, brown eyes, professional headshot, shot on Hasselblad, perfect lighting, dutch angle, bokeh, outdoors, depth of field, blue dress, warm, loving, friendly <lora:PriyaBelleXL_v1:1> parameters: negative_prompt: bindi, mole, facial marks output: url: images/00001-3916971016.png - text: >- PriBlle, very dark-skinned woman, solo focus, mixed media, realistic anime art style, art by Yusuke Nakamura, fractal, ukiyoe, watercolor ink wash technique, intricate, highly detailed. Inspired by multiracial Hindi-West Indian heritage, San Francisco Bay Area, and diaspora. <lora:PriyaBelleXL_v1:1> output: url: images/00002-2902012777.png - text: >- PriBlle as Princess Jasmine, mind controlled by Jafar, sexy red outfit, tiara, collar, Agrabah palace, entranced by magic:1.1, glowing, compliant, submissive, obedient, Disney's Aladdin bad end <lora:PriyaBelleXL_v1:1> output: url: images/00121-3666660946.png - text: >- PriBlle is a college student on campus, dark blue and gold hooded sweatshirt with bear logo and shorts, Berkeley <lora:PriyaBelleXL_v1:1> output: url: images/00172-3938050706.png - text: >- PriBlle is hella fine shawty, hyphy, outdoors, Lake Merritt, Oakland, NorCal, yay area <lora:PriyaBelleXL_v1:1> output: url: images/00156-519328175.png - text: >- PriBlle, a woman wearing a green Oakland Athletics cap and sexy fan gear, smiling, ponytail, bodycon, bedroom, natural light, sexy, tease, flirty <lora:PriyaBelleXL_v1:1> output: url: images/00328-1196258457.png base_model: stabilityai/stable-diffusion-xl-base-1.0 instance_prompt: PriBlle license: cc-by-nc-nd-4.0 --- # Priya Belle (Ottoverse original character) - SDXL 1.0 <Gallery /> ## Model description https://huggingface.co/ottopilot/PriyaBelle, but trained for SDXL. ## Trigger words You should use `PriBlle` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/ottopilot/PriyaBelleXL/tree/main) them in the Files & versions tab.
JesseStover/L2AI-dictionary-klue-bert-base	JesseStover	2024-03-06T06:47:19Z	89	0	transformers	[ "transformers", "safetensors", "bert", "multiple-choice", "endpoints_compatible", "region:us" ]	multiple-choice	2024-03-04T13:52:44Z	--- {} --- The L2AI-dictionary model is fine-tuned checkpoint of [klue/bert-base](https://huggingface.co/klue/bert-base) for multiple choice, specifically for selecting the best dictionary definition of a given word in a sentence. Below is an example usage: ```python import numpy as np import torch from transformers import AutoModelForMultipleChoice, AutoTokenizer model_name = "JesseStover/L2AI-dictionary-klue-bert-base" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForMultipleChoice.from_pretrained(model_name) model.to(torch.device("cuda" if torch.cuda.is_available() else "cpu")) prompts = "\"강아지는 뽀송뽀송하다.\"에 있는 \"강아지\"의 정의는 " candidates = [ "\"(명사) 개의 새끼\"예요.", "\"(명사) 부모나 할아버지, 할머니가 자식이나 손주를 귀여워하면서 부르는 말\"이예요." ] inputs = tokenizer( [[prompt, candidate] for candidate in candidates], return_tensors="pt", padding=True ) labels = torch.tensor(0).unsqueeze(0) with torch.no_grad(): outputs = model( **{k: v.unsqueeze(0) for k, v in inputs.items()}, labels=labels ) print({i: float(x) for i, x in enumerate(outputs.logits.softmax(1)[0])}) ``` Training data was procured under Creative Commons [CC BY-SA 2.0 KR DEED](https://creativecommons.org/licenses/by-sa/2.0/kr/) from the National Institute of Korean Language's [Basic Korean Dictionary](https://krdict.korean.go.kr) and [Standard Korean Dictionary](https://stdict.korean.go.kr/).
vsocrates/incar-status-any	vsocrates	2024-03-06T06:44:25Z	5	0	transformers	[ "transformers", "pytorch", "longformer", "text-classification", "medical", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T05:07:27Z	--- '[object Object]': null license: apache-2.0 language: - en library_name: transformers tags: - medical widget: - text: "Patient is a a formerly incarcerated individual having arrived in the ED with stomach pain." - example_title: "Former Incarceration" - text: "Patient arrived in the ED for chest pain." - example_title: "No Incarceration" --- # Model Card for incar-status-any A Clinical Longformer-based model trained by the HAIL lab to predict incarceration status (past and present) in ED Notes. ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: Vimig Socrates - Model type: Longformer - Language(s) (NLP): English - License: Apache License 2.0 - Finetuned from model: [Clinical Lonformer](https://huggingface.co/yikuan8/Clinical-Longformer ) ## Uses This model can be used to predict the incarceration status that a patient might have given most types of clinical ED notes. ## Bias, Risks, and Limitations This should not be used directly without supervision from a physician as predicting incarceration status incorrectly can have significant negative social and clinical impacts. ## Training Details ### Training Data This model was trained on custom annotated data labeled for incarceration status from Yale-New Haven Health Hospital System ED Notes. ### Training Procedure ## Evaluation TODO ### Testing Data, Factors & Metrics ### Results TODO ] ## Citation [optional] Coming soon! BibTeX: {{ citation_bibtex \| default("[More Information Needed]", true)}} APA: {{ citation_apa \| default("[More Information Needed]", true)}} ## Model Card Authors [optional] Vimig Socrates ## Model Card Contact Vimig Socrates: [[email protected]](mailto:[email protected])
samanthakarungi/fine-tuned-bert	samanthakarungi	2024-03-06T06:42:24Z	5	1	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "finance", "business", "en", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-26T08:29:46Z	--- language: - en widget: - text: uber for today - text: airtime and data - text: breakfast meeting with client metrics: - accuracy pipeline_tag: text-classification tags: - finance - text-classification - business --- ### Model Description <p>This model is a fine tuned version of the <a href="https://huggingface.co/distilbert/distilbert-base-uncased">distilbert-base-uncased</a> model on Hugging face. The model is trained to classify payment notes for business owners into one of the following categories.</p> <ol> <li>INVENTORY, SUPPLIES AND EQUIPMENT</li> <li>PROFESSIONAL SERVICES</li> <li>TRANSPORTATION AND TRAVEL</li> <li>UTILITIES</li> <li>EMPLOYEE BENEFITS AND COMPENSATION</li> <li>MEALS AND ENTERTAINMENT</li> <li>TAX PAYMENTS</li> <li>LEGAL AND COMPLIANCE FEES</li> <li>BUSINESS DEVELOPMENT AND INVESTMENT</li> </ol> ### Base Model Description <p>DistilBERT is a transformers model, smaller and faster than BERT, which was pretrained on the same corpus in a self-supervised fashion, using the BERT base model as a teacher. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts using the BERT base model.</p> ### Training results <table> <tr> <th>Epoch</th> <th>Training Loss</th> <th>Validation Loss</th> <th>Accuracy</th> </tr> <tr> <th>0</th> <th>No Log</th> <th>0.263793</th> <th>0.916230</th> </tr> <tr> <th>1</th> <th>No Log</th> <th>0.185122</th> <th>0.937173</th> </tr> <tr> <th>2</th> <th>0.318300</th> <th>0.191695</th> <th>0.937173</th> </tr> </table> ### Training results <p>Check out the training code at this <a href="https://github.com/samanthaKarungi/iotec-pay-model-bert/tree/main/model/training_and_evaluation">github repo</a></p> ### Framework versions <ul> <li>Transformers 4.37.2</li> <li>PyTorch 2.2.0</li> <li>Datasets 2.17.1</li> <li>Tokenizers 0.15.2</li> </ul>
gokuls/wav2vec2-base-finetuned-ic-slurp	gokuls	2024-03-06T06:34:14Z	9	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-base", "base_model:finetune:facebook/wav2vec2-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-05T13:14:31Z	--- license: apache-2.0 base_model: facebook/wav2vec2-base tags: - generated_from_trainer metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ic-slurp results: [] --- <!-- 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. --> # wav2vec2-base-finetuned-ic-slurp This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1101 - Accuracy: 0.7393 ## 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: 24 - eval_batch_size: 24 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 96 - 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 4.0345 \| 1.0 \| 527 \| 3.9813 \| 0.0673 \| \| 3.5622 \| 2.0 \| 1055 \| 3.4634 \| 0.1867 \| \| 2.7737 \| 3.0 \| 1582 \| 2.7252 \| 0.3638 \| \| 2.1285 \| 4.0 \| 2110 \| 2.1754 \| 0.4827 \| \| 1.6216 \| 5.0 \| 2637 \| 1.8169 \| 0.5701 \| \| 1.1786 \| 6.0 \| 3165 \| 1.5773 \| 0.6347 \| \| 0.8747 \| 7.0 \| 3692 \| 1.5024 \| 0.6568 \| \| 0.7565 \| 8.0 \| 4220 \| 1.5020 \| 0.6694 \| \| 0.5236 \| 9.0 \| 4747 \| 1.5287 \| 0.6799 \| \| 0.4517 \| 10.0 \| 5275 \| 1.5165 \| 0.6879 \| \| 0.364 \| 11.0 \| 5802 \| 1.5159 \| 0.6949 \| \| 0.3221 \| 12.0 \| 6330 \| 1.5217 \| 0.6996 \| \| 0.227 \| 13.0 \| 6857 \| 1.5718 \| 0.7075 \| \| 0.1828 \| 14.0 \| 7385 \| 1.6979 \| 0.6901 \| \| 0.1691 \| 15.0 \| 7912 \| 1.6162 \| 0.7093 \| \| 0.1642 \| 16.0 \| 8440 \| 1.6973 \| 0.7048 \| \| 0.1254 \| 17.0 \| 8967 \| 1.7060 \| 0.7100 \| \| 0.1578 \| 18.0 \| 9495 \| 1.7328 \| 0.7063 \| \| 0.1509 \| 19.0 \| 10022 \| 1.7658 \| 0.7073 \| \| 0.1409 \| 20.0 \| 10550 \| 1.7770 \| 0.7052 \| \| 0.1085 \| 21.0 \| 11077 \| 1.8033 \| 0.7074 \| \| 0.106 \| 22.0 \| 11605 \| 1.7000 \| 0.7149 \| \| 0.0764 \| 23.0 \| 12132 \| 1.7943 \| 0.7104 \| \| 0.0671 \| 24.0 \| 12660 \| 1.8323 \| 0.7155 \| \| 0.0768 \| 25.0 \| 13187 \| 1.8486 \| 0.7146 \| \| 0.0741 \| 26.0 \| 13715 \| 1.8227 \| 0.7187 \| \| 0.0731 \| 27.0 \| 14242 \| 1.7824 \| 0.7230 \| \| 0.0935 \| 28.0 \| 14770 \| 1.8987 \| 0.7164 \| \| 0.0829 \| 29.0 \| 15297 \| 1.8774 \| 0.7202 \| \| 0.0588 \| 30.0 \| 15825 \| 1.8820 \| 0.7211 \| \| 0.059 \| 31.0 \| 16352 \| 1.9535 \| 0.7246 \| \| 0.0431 \| 32.0 \| 16880 \| 1.9621 \| 0.7237 \| \| 0.0324 \| 33.0 \| 17407 \| 2.0160 \| 0.7256 \| \| 0.0447 \| 34.0 \| 17935 \| 1.9392 \| 0.7262 \| \| 0.025 \| 35.0 \| 18462 \| 2.0095 \| 0.7284 \| \| 0.0522 \| 36.0 \| 18990 \| 1.9994 \| 0.7244 \| \| 0.0482 \| 37.0 \| 19517 \| 2.0566 \| 0.7262 \| \| 0.0203 \| 38.0 \| 20045 \| 2.0287 \| 0.7295 \| \| 0.0221 \| 39.0 \| 20572 \| 2.0634 \| 0.7300 \| \| 0.0444 \| 40.0 \| 21100 \| 2.0593 \| 0.7302 \| \| 0.0348 \| 41.0 \| 21627 \| 2.0712 \| 0.7298 \| \| 0.0154 \| 42.0 \| 22155 \| 2.0429 \| 0.7351 \| \| 0.024 \| 43.0 \| 22682 \| 2.0708 \| 0.7352 \| \| 0.0157 \| 44.0 \| 23210 \| 2.0701 \| 0.7368 \| \| 0.0222 \| 45.0 \| 23737 \| 2.0963 \| 0.7338 \| \| 0.0126 \| 46.0 \| 24265 \| 2.1329 \| 0.7340 \| \| 0.0211 \| 47.0 \| 24792 \| 2.1230 \| 0.7370 \| \| 0.0288 \| 48.0 \| 25320 \| 2.1101 \| 0.7393 \| \| 0.0347 \| 49.0 \| 25847 \| 2.1201 \| 0.7375 \| \| 0.0162 \| 49.95 \| 26350 \| 2.1197 \| 0.7381 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.1 - Tokenizers 0.15.2
davidilag/whisper-base-fo	davidilag	2024-03-06T06:27:05Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "fo", "dataset:carlosdanielhernandezmena/ravnursson_asr", "base_model:openai/whisper-base", "base_model:finetune:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-03-05T09:36:22Z	--- language: - fo license: apache-2.0 base_model: openai/whisper-base tags: - hf-asr-leaderboard - generated_from_trainer datasets: - carlosdanielhernandezmena/ravnursson_asr model-index: - name: "Whisper Base Fo - D\xE1vid \xED L\xE1g" results: [] --- <!-- 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 Base Fo - Dávid í Lág This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Ravnurson 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: 4000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
anashrivastava/tinyllama-colorist-lora	anashrivastava	2024-03-06T06:23:59Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v0.3", "license:apache-2.0", "region:us" ]	null	2024-03-06T06:19:00Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: PY007/TinyLlama-1.1B-Chat-v0.3 model-index: - name: tinyllama-colorist-lora results: [] --- <!-- 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. --> # tinyllama-colorist-lora This model is a fine-tuned version of [PY007/TinyLlama-1.1B-Chat-v0.3](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) 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: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 200 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
Aharneish/Gemma-Final	Aharneish	2024-03-06T06:17:35Z	1	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:google/gemma-7b", "base_model:adapter:google/gemma-7b", "license:other", "region:us" ]	null	2024-03-06T04:11:14Z	--- license: other library_name: peft tags: - trl - sft - generated_from_trainer base_model: google/gemma-7b model-index: - name: gemma-sprit-test results: [] --- <!-- 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. --> # gemma-sprit-test This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on an unknown dataset. It achieves the following results on the evaluation set: - eval_loss: 3.3436 - eval_runtime: 210.5944 - eval_samples_per_second: 0.95 - eval_steps_per_second: 0.237 - epoch: 0.81 - step: 1500 ## 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: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Framework versions - PEFT 0.9.0 - Transformers 4.38.1 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.15.2
Hiraishin/reranker-malaysian-mistral-474M	Hiraishin	2024-03-06T06:13:29Z	0	0	transformers	[ "transformers", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T06:13:29Z	--- library_name: transformers tags: [] --- # 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. This model card has been automatically generated. - 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. 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hughlan1214/SER_wav2vec2-large-xlsr-53_fine-tuned_1.0	hughlan1214	2024-03-06T05:53:02Z	9	1	transformers	[ "transformers", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "base_model:facebook/wav2vec2-large-xlsr-53", "base_model:finetune:facebook/wav2vec2-large-xlsr-53", "license:apache-2.0", "endpoints_compatible", "region:us" ]	audio-classification	2024-03-03T13:30:18Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: SER_wav2vec2-large-xlsr-53_fine-tuned_1.0 results: [] --- <!-- 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. --> # SER_wav2vec2-large-xlsr-53_240303 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on a [Speech Emotion Recognition (en)](https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en) dataset. This dataset includes the 4 most popular datasets in English: Crema, Ravdess, Savee, and Tess, containing a total of over 12,000 .wav audio files. Each of these four datasets includes 6 to 8 different emotional labels. It achieves the following results on the evaluation set: - Loss: 1.7923 - Accuracy: 0.2408 - Precision: 0.2324 - Recall: 0.2466 - F1: 0.2226 ## For a better performance version, please refer to [hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0](https://huggingface.co/hughlan1214/Speech_Emotion_Recognition_wav2vec2-large-xlsr-53_240304_SER_fine-tuned2.0) ## Model description The model was obtained through feature extraction using [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) and underwent several rounds of fine-tuning. It predicts the 7 types of emotions contained in speech, aiming to lay the foundation for subsequent use of human micro-expressions on the visual level and context semantics under LLMS to infer user emotions in real-time. Although the model was trained on purely English datasets, post-release testing showed that it also performs well in predicting emotions in Chinese and French, demonstrating the powerful cross-linguistic capability of the [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) pre-trained model. ```python emotions = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise'] ``` ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 3 - total_train_batch_size: 12 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.9297 \| 1.0 \| 101 \| 1.9452 \| 0.1233 \| 0.0306 \| 0.1468 \| 0.0454 \| \| 1.9114 \| 2.0 \| 202 \| 1.9115 \| 0.1773 \| 0.1501 \| 0.1803 \| 0.1323 \| \| 1.7863 \| 3.0 \| 303 \| 1.8564 \| 0.2081 \| 0.1117 \| 0.2193 \| 0.1336 \| \| 1.8439 \| 4.0 \| 404 \| 1.8590 \| 0.2042 \| 0.2196 \| 0.2156 \| 0.1755 \| \| 1.9361 \| 5.0 \| 505 \| 1.8375 \| 0.2081 \| 0.2617 \| 0.2213 \| 0.1573 \| \| 1.7572 \| 6.0 \| 606 \| 1.8081 \| 0.2100 \| 0.2018 \| 0.2214 \| 0.1841 \| \| 1.6715 \| 7.0 \| 707 \| 1.8131 \| 0.2389 \| 0.2263 \| 0.2442 \| 0.2129 \| \| 1.6687 \| 8.0 \| 808 \| 1.7923 \| 0.2408 \| 0.2324 \| 0.2466 \| 0.2226 \| ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.1 - Datasets 2.17.1 - Tokenizers 0.15.2
LN1996/output_run_3	LN1996	2024-03-06T05:52:53Z	1	0	diffusers	[ "diffusers", "tensorboard", "text-to-image", "lora", "stable-diffusion", "stable-diffusion-diffusers", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-06T05:22:43Z	--- license: creativeml-openrail-m library_name: diffusers tags: - text-to-image - diffusers - lora - stable-diffusion - stable-diffusion-diffusers inference: true base_model: runwayml/stable-diffusion-v1-5 instance_prompt: photo of a room with professional interior design --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA DreamBooth - LN1996/output_run_3 These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on photo of a room with professional interior design using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following. LoRA for the text encoder was enabled: False. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
UBC-NLP/InfoDCL-hashtag	UBC-NLP	2024-03-06T05:44:55Z	14	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "social media", "contrastive learning", "en", "arxiv:2203.07648", "license:cc", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2023-07-07T03:56:29Z	--- license: cc language: - en library_name: transformers tags: - social media - contrastive learning --- # Contrastive Learning of Sociopragmatic Meaning in Social Media <p align="center"> <a href="https://chiyuzhang94.github.io/" target="_blank">Chiyu Zhang</a>, <a href="https://mageed.arts.ubc.ca/" target="_blank">Muhammad Abdul-Mageed</a>, <a href="https://ganeshjawahar.github.io/" target="_blank">Ganesh Jarwaha</a></p> <p align="center" float="left"> <p align="center">Publish at Findings of ACL 2023</p> <p align="center"> <a href="https://arxiv.org/abs/2203.07648" target="_blank">Paper</a></p> [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)]() [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)]() <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/infodcl_vis.png?raw=true" alt="Title" style="width: 90%; min-width: 300px; display: block; margin: auto;"></a> </p> Illustration of our proposed InfoDCL framework. We exploit distant/surrogate labels (i.e., emojis) to supervise two contrastive losses, corpus-aware contrastive loss (CCL) and Light label-aware contrastive loss (LCL-LiT). Sequence representations from our model should keep the cluster of each class distinguishable and preserve semantic relationships between classes. ## Checkpoints of Models Pre-Trained with InfoDCL * InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji * InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag ## Model Performance <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/main_table.png?raw=true" alt="main table" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a> </p> Fine-tuning results on our 24 Socio-pragmatic Meaning datasets (average macro-F1 over five runs).
UBC-NLP/InfoDCL-BERTweet-emoji	UBC-NLP	2024-03-06T05:43:52Z	17	0	transformers	[ "transformers", "pytorch", "roberta", "social media", "contrastive learning", "en", "arxiv:2203.07648", "license:cc", "endpoints_compatible", "region:us" ]	null	2023-08-13T20:17:32Z	--- license: cc language: - en library_name: transformers tags: - social media - contrastive learning --- # Contrastive Learning of Sociopragmatic Meaning in Social Media <p align="center"> <a href="https://chiyuzhang94.github.io/" target="_blank">Chiyu Zhang</a>, <a href="https://mageed.arts.ubc.ca/" target="_blank">Muhammad Abdul-Mageed</a>, <a href="https://ganeshjawahar.github.io/" target="_blank">Ganesh Jarwaha</a></p> <p align="center" float="left"> <p align="center">Publish at Findings of ACL 2023</p> <p align="center"> <a href="https://arxiv.org/abs/2203.07648" target="_blank">Paper</a></p> [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)]() [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)]() <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/infodcl_vis.png?raw=true" alt="Title" style="width: 90%; min-width: 300px; display: block; margin: auto;"></a> </p> Illustration of our proposed InfoDCL framework. We exploit distant/surrogate labels (i.e., emojis) to supervise two contrastive losses, corpus-aware contrastive loss (CCL) and Light label-aware contrastive loss (LCL-LiT). Sequence representations from our model should keep the cluster of each class distinguishable and preserve semantic relationships between classes. ## Checkpoints of Models Pre-Trained with InfoDCL * InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji * InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag ## Model Performance <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/main_table.png?raw=true" alt="main table" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a> </p> Fine-tuning results on our 24 Socio-pragmatic Meaning datasets (average macro-F1 over five runs).
UBC-NLP/InfoDCL-BERTweet-hashtag	UBC-NLP	2024-03-06T05:43:23Z	6	0	transformers	[ "transformers", "pytorch", "roberta", "social media", "contrastive learning", "en", "arxiv:2203.07648", "license:cc", "endpoints_compatible", "region:us" ]	null	2023-08-13T18:38:56Z	--- license: cc language: - en library_name: transformers tags: - social media - contrastive learning --- # Contrastive Learning of Sociopragmatic Meaning in Social Media <p align="center"> <a href="https://chiyuzhang94.github.io/" target="_blank">Chiyu Zhang</a>, <a href="https://mageed.arts.ubc.ca/" target="_blank">Muhammad Abdul-Mageed</a>, <a href="https://ganeshjawahar.github.io/" target="_blank">Ganesh Jarwaha</a></p> <p align="center" float="left"> <p align="center">Publish at Findings of ACL 2023</p> <p align="center"> <a href="https://arxiv.org/abs/2203.07648" target="_blank">Paper</a></p> [![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg)]() [![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg)]() <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/infodcl_vis.png?raw=true" alt="Title" style="width: 90%; min-width: 300px; display: block; margin: auto;"></a> </p> Illustration of our proposed InfoDCL framework. We exploit distant/surrogate labels (i.e., emojis) to supervise two contrastive losses, corpus-aware contrastive loss (CCL) and Light label-aware contrastive loss (LCL-LiT). Sequence representations from our model should keep the cluster of each class distinguishable and preserve semantic relationships between classes. ## Checkpoints of Models Pre-Trained with InfoDCL * InfoDCL-RoBERTa trained with TweetEmoji-EN: https://huggingface.co/UBC-NLP/InfoDCL-emoji * InfoDCL-RoBERTa trained with TweetHashtag-EN: https://huggingface.co/UBC-NLP/InfoDCL-hashtag ## Model Performance <p align="center" width="100%"> <a><img src="https://github.com/UBC-NLP/infodcl/blob/master/images/main_table.png?raw=true" alt="main table" style="width: 95%; min-width: 300px; display: block; margin: auto;"></a> </p> Fine-tuning results on our 24 Socio-pragmatic Meaning datasets (average macro-F1 over five runs).
Litzy619/V0305P4	Litzy619	2024-03-06T05:43:14Z	0	0	null	[ "safetensors", "generated_from_trainer", "base_model:yahma/llama-7b-hf", "base_model:finetune:yahma/llama-7b-hf", "license:other", "region:us" ]	null	2024-03-05T16:50:42Z	--- license: other base_model: yahma/llama-7b-hf tags: - generated_from_trainer model-index: - name: V0305P4 results: [] --- <!-- 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. --> # V0305P4 This model is a fine-tuned version of [yahma/llama-7b-hf](https://huggingface.co/yahma/llama-7b-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0583 ## 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 20 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.8125 \| 0.09 \| 10 \| 0.6624 \| \| 0.25 \| 0.17 \| 20 \| 0.1568 \| \| 0.1567 \| 0.26 \| 30 \| 0.1543 \| \| 0.1522 \| 0.34 \| 40 \| 0.1471 \| \| 0.1487 \| 0.43 \| 50 \| 0.1446 \| \| 0.1517 \| 0.51 \| 60 \| 0.1370 \| \| 0.1348 \| 0.6 \| 70 \| 0.1154 \| \| 0.1261 \| 0.68 \| 80 \| 0.1077 \| \| 0.1125 \| 0.77 \| 90 \| 0.0915 \| \| 0.1142 \| 0.85 \| 100 \| 0.0879 \| \| 0.1095 \| 0.94 \| 110 \| 0.0932 \| \| 0.1035 \| 1.02 \| 120 \| 0.0936 \| \| 0.094 \| 1.11 \| 130 \| 0.0874 \| \| 0.0899 \| 1.19 \| 140 \| 0.0800 \| \| 0.0875 \| 1.28 \| 150 \| 0.0835 \| \| 0.0887 \| 1.37 \| 160 \| 0.0783 \| \| 0.0884 \| 1.45 \| 170 \| 0.0791 \| \| 0.0819 \| 1.54 \| 180 \| 0.0745 \| \| 0.0831 \| 1.62 \| 190 \| 0.0685 \| \| 0.0878 \| 1.71 \| 200 \| 0.0681 \| \| 0.0847 \| 1.79 \| 210 \| 0.0680 \| \| 0.0798 \| 1.88 \| 220 \| 0.0646 \| \| 0.0757 \| 1.96 \| 230 \| 0.0680 \| \| 0.0653 \| 2.05 \| 240 \| 0.0663 \| \| 0.0557 \| 2.13 \| 250 \| 0.0678 \| \| 0.052 \| 2.22 \| 260 \| 0.0634 \| \| 0.0517 \| 2.3 \| 270 \| 0.0654 \| \| 0.0576 \| 2.39 \| 280 \| 0.0593 \| \| 0.0573 \| 2.47 \| 290 \| 0.0584 \| \| 0.056 \| 2.56 \| 300 \| 0.0569 \| \| 0.0597 \| 2.65 \| 310 \| 0.0584 \| \| 0.0514 \| 2.73 \| 320 \| 0.0578 \| \| 0.0533 \| 2.82 \| 330 \| 0.0577 \| \| 0.0538 \| 2.9 \| 340 \| 0.0582 \| \| 0.0507 \| 2.99 \| 350 \| 0.0583 \| ### Framework versions - Transformers 4.36.0.dev0 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.14.1
stablediffusionapi/hello-young-25d	stablediffusionapi	2024-03-06T05:37:15Z	23	0	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T05:36:38Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # API Inference ![generated from modelslab.com](https://cdn2.stablediffusionapi.com/generations/bf190b5a-fe19-437c-ba05-82f29cb1f7ad-0.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 "hello-young-25d" 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/hello-young-25d) Model link: [View model](https://modelslab.com/models/hello-young-25d) 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": "hello-young-25d", "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
youngko/mistral-test	youngko	2024-03-06T05:29:14Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "region:us" ]	null	2024-03-06T05:27:40Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.1 model-index: - name: mistral-test results: [] --- <!-- 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. --> # mistral-test This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.3 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
min-dong/LLM_test1	min-dong	2024-03-06T04:58:42Z	2	0	transformers	[ "transformers", "safetensors", "gpt_neox", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-03-06T04:47:11Z	--- library_name: transformers tags: [] --- # 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. This model card has been automatically generated. - 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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(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]
Padu98/ampazephyr-2-prompt-2-versuch-2	Padu98	2024-03-06T04:57:58Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:HuggingFaceH4/zephyr-7b-beta", "base_model:adapter:HuggingFaceH4/zephyr-7b-beta", "license:mit", "region:us" ]	null	2024-03-05T15:08:33Z	--- license: mit library_name: peft tags: - trl - sft - generated_from_trainer base_model: HuggingFaceH4/zephyr-7b-beta model-index: - name: ampazephyr-2-prompt-2-versuch-2 results: [] --- <!-- 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. --> # ampazephyr-2-prompt-2-versuch-2 This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3778 ## 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: 2 - 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 \| 136 \| 0.4528 \| \| No log \| 2.0 \| 272 \| 0.3838 \| \| No log \| 3.0 \| 408 \| 0.3778 \| ### Framework versions - PEFT 0.9.0 - Transformers 4.39.0.dev0 - Pytorch 2.1.2 - Datasets 2.16.0 - Tokenizers 0.15.2
hakuhodo-tech/japanese-clip-vit-h-14-bert-base	hakuhodo-tech	2024-03-06T04:52:02Z	11	1	transformers	[ "transformers", "safetensors", "custom-clip-model", "feature-extraction", "clip", "ja", "japanese", "japanese-clip", "custom_code", "arxiv:2103.00020", "arxiv:2111.07991", "license:cc-by-nc-sa-4.0", "region:us" ]	feature-extraction	2024-03-06T03:23:16Z	--- license: cc-by-nc-sa-4.0 language: - ja tags: - clip - ja - japanese - japanese-clip pipeline_tag: feature-extraction --- # Japanese CLIP ViT-H/14 (Base) ## Table of Contents 1. [Overview](#overview) 1. [Usage](#usage) 1. [Model Details](#model-details) 1. [Evaluation](#evaluation) 1. [Limitations and Biases](#limitations-and-biases) 1. [Citation](#citation) 1. [See Also](#see-also) 1. [Contact Information](#contact-information) ## Overview * Developed by: [HAKUHODO Technologies Inc.](https://www.hakuhodo-technologies.co.jp/) * Model type: Contrastive Language-Image Pre-trained Model * Language(s): Japanese * LICENSE: [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) Presented here is a Japanese [CLIP (Contrastive Language-Image Pre-training)](https://arxiv.org/abs/2103.00020) model, mapping Japanese texts and images to a unified embedding space. Capable of multimodal tasks including zero-shot image classification, text-to-image retrieval, and image-to-text retrieval, this model extends its utility when integrated with other components, contributing to generative models like image-to-text and text-to-image generation. ## Usage ### Dependencies ```bash python3 -m pip install pillow sentencepiece torch torchvision transformers ``` ### Inference The usage is similar to [`CLIPModel`](https://huggingface.co/docs/transformers/model_doc/clip) and [`VisionTextDualEncoderModel`](https://huggingface.co/docs/transformers/model_doc/vision-text-dual-encoder). ```python import requests import torch from PIL import Image from transformers import AutoModel, AutoProcessor, BatchEncoding # Download model_name = "hakuhodo-tech/japanese-clip-vit-h-14-bert-base" device = "cuda" if torch.cuda.is_available() else "cpu" model = AutoModel.from_pretrained(model_name, trust_remote_code=True).to(device) processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True) # Prepare raw inputs url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) # Process inputs inputs = processor( text=["犬", "猫", "象"], images=image, return_tensors="pt", padding=True, ) # Infer and output outputs = model(BatchEncoding(inputs).to(device)) probs = outputs.logits_per_image.softmax(dim=1) print([f"{x:.2f}" for x in probs.flatten().tolist()]) # ['0.00', '1.00', '0.00'] ``` ## Model Details ### Components The model consists of a frozen ViT-H image encoder from [laion/CLIP-ViT-H-14-laion2B-s32B-b79K](https://huggingface.co/laion/CLIP-ViT-H-14-laion2B-s32B-b79K) and a 12-layer 12-head BERT text encoder initialized from [rinna/japanese-clip-vit-b-16](https://huggingface.co/rinna/japanese-clip-vit-b-16). ### Training Model training is done by Zhi Wang with 8 A100 (80 GB) GPUs. [Locked-image Tuning (LiT)](https://arxiv.org/abs/2111.07991) is adopted. See more details in [the paper](https://www.anlp.jp/proceedings/annual_meeting/2024/pdf_dir/B6-5.pdf). ### Dataset The Japanese subset of the [laion2B-multi](https://huggingface.co/datasets/laion/laion2B-multi) dataset containing ~120M image-text pairs. ## Evaluation ### Testing Data The 5K evaluation set (val2017) of [MS-COCO](https://cocodataset.org/) with [STAIR Captions](http://captions.stair.center/). ### Metrics Zero-shot image-to-text and text-to-image recall@1, 5, 10. ### Results \| \| \| \| \| \| \| \| \| :---------------------------------------------------------------------------------------------------------------------- \| :------: \| :------: \| :------: \| :------: \| :------: \| :------: \| \| <td colspan=3 align=center>Text Retrieval</td> <td colspan=3 align=center>Image Retrieval</td> \| \| \| R@1 \| R@5 \| R@10 \| R@1 \| R@5 \| R@10 \| \| [recruit-jp/japanese-clip-vit-b-32-roberta-base](https://huggingface.co/recruit-jp/japanese-clip-vit-b-32-roberta-base) \| 23.0 \| 46.1 \| 57.4 \| 16.1 \| 35.4 \| 46.3 \| \| [rinna/japanese-cloob-vit-b-16](https://huggingface.co/rinna/japanese-cloob-vit-b-16) \| 37.1 \| 63.7 \| 74.2 \| 25.1 \| 48.0 \| 58.8 \| \| [rinna/japanese-clip-vit-b-16](https://huggingface.co/rinna/japanese-clip-vit-b-16) \| 36.9 \| 64.3 \| 74.3 \| 24.8 \| 48.8 \| 60.0 \| \| [Japanese CLIP ViT-H/14 (Base)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-base) \| 39.2 \| 66.3 \| 76.6 \| 28.9 \| 53.3 \| 63.9 \| \| [Japanese CLIP ViT-H/14 (Deeper)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-deeper) \| 48.7 \| 74.0 \| 82.4 \| 36.5 \| 61.5 \| 71.8 \| \| [Japanese CLIP ViT-H/14 (Wider)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-wider) \| 47.9 \| 74.2 \| 83.2 \| 37.3 \| 62.8 \| 72.7** \| \* [Japanese Stable CLIP ViT-L/16](https://huggingface.co/stabilityai/japanese-stable-clip-vit-l-16) is excluded for zero-shot retrieval evaluation as [the model was partially pre-trained with MS-COCO](https://huggingface.co/stabilityai/japanese-stable-clip-vit-l-16#training-dataset). ## Limitations and Biases Despite our data filtering, it is crucial to acknowledge the possibility of the training dataset containing offensive or inappropriate content. Users should be mindful of the potential societal impact and ethical considerations associated with the outputs generated by the model when deploying in production systems. It is recommended not to employ the model for applications that have the potential to cause harm or distress to individuals or groups. ## Citation If you found this model useful, please consider citing: ```bibtex @article{japanese-clip-vit-h, author = {王直 and 細野健人 and 石塚湖太 and 奥田悠太 and 川上孝介}, journal = {言語処理学会年次大会発表論文集}, month = {Mar}, pages = {1547--1552}, title = {日本語特化の視覚と言語を組み合わせた事前学習モデルの開発 Developing Vision-Language Pre-Trained Models for {J}apanese}, volume = {30}, year = {2024} } ``` ## See Also * [Japanese CLIP ViT-H/14 (Deeper)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-deeper) * [Japanese CLIP ViT-H/14 (Wider)](https://huggingface.co/hakuhodo-tech/japanese-clip-vit-h-14-bert-wider) ## Contact Information Please contact [hr-koho\@hakuhodo-technologies.co.jp](mailto:[email protected]?subject=Japanese%20CLIP%20ViT-H/14%20Models) for questions and comments about the model, and/or for business and partnership inquiries. お問い合わせは [hr-koho\@hakuhodo-technologies.co.jp](mailto:[email protected]?subject=日本語CLIP%20ViT-H/14モデルについて) にご連絡ください。
WokeEngineer/Reinforce-Pixelcopter-PLE-v0	WokeEngineer	2024-03-06T04:51:45Z	0	0	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-03-06T04:51:40Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-Pixelcopter-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 62.70 +/- 32.09 name: mean_reward verified: false --- # 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
ChaoticNeutrals/Layris_9B	ChaoticNeutrals	2024-03-06T04:51:34Z	7	7	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:ChaoticNeutrals/Eris_Remix_7B", "base_model:merge:ChaoticNeutrals/Eris_Remix_7B", "base_model:l3utterfly/mistral-7b-v0.1-layla-v4", "base_model:merge:l3utterfly/mistral-7b-v0.1-layla-v4", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T01:54:39Z	--- base_model: - ChaoticNeutrals/Eris_Remix_7B - l3utterfly/mistral-7b-v0.1-layla-v4 library_name: transformers tags: - mergekit - merge license: other --- # Layris ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/rZgYprmvvy_5OnjJqmePd.jpeg) 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 passthrough merge method. ### Models Merged The following models were included in the merge: * [ChaoticNeutrals/Eris_Remix_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B) * [l3utterfly/mistral-7b-v0.1-layla-v4](https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v4) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: ChaoticNeutrals/Eris_Remix_7B layer_range: [0, 20] - sources: - model: l3utterfly/mistral-7b-v0.1-layla-v4 layer_range: [12, 32] merge_method: passthrough dtype: float16 ```
cllm/deepseekcoder-7b-instruct-spider	cllm	2024-03-06T04:46:28Z	12	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-01-27T09:58:48Z	Metadata: AR loss to consistency loss ratio: 10: 1 Spider dataset size: 7k n-token sequence length: 16 Jacobi trajectory data cleaning: True Target model: Deepseek-Coder-7B fine-tuned on Spider release date: 02/26/2024
leducanh1203/BioMistral7B_AutoTrain	leducanh1203	2024-03-06T04:41:58Z	0	0	null	[ "tensorboard", "safetensors", "autotrain", "text-generation", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T04:41:50Z	--- tags: - autotrain - text-generation widget: - text: "I love AutoTrain because " license: other --- # 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) ```
brainventures/deplot_kr	brainventures	2024-03-06T04:38:11Z	124	1	transformers	[ "transformers", "pytorch", "pix2struct", "image-text-to-text", "image-to-text", "ko", "arxiv:2212.10505", "endpoints_compatible", "region:us" ]	image-to-text	2024-02-07T06:45:49Z	--- language: - ko pipeline_tag: image-to-text --- # deplot_kr deplot_kr is a Image-to-Data(Text) model based on the google's pix2struct architecture. It was fine-tuned from [DePlot](https://huggingface.co/google/deplot), using korean chart image-text pairs. deplot_kr은 google의 pix2struct 구조를 기반으로 한 한국어 image-to-data(텍스트 형태의 데이터 테이블) 모델입니다. [DePlot](https://huggingface.co/google/deplot) 모델을 한국어 차트 이미지-텍스트 쌍 데이터세트(30만 개)를 이용하여 fine-tuning 했습니다. ## How to use You can run a prediction by input an image. Model predict the data table of text form in the image. 이미지를 모델에 입력하면 모델은 이미지로부터 표 형태의 데이터 테이블을 예측합니다. ```python from transformers import Pix2StructForConditionalGeneration, AutoProcessor from PIL import Image processor = AutoProcessor.from_pretrained("brainventures/deplot_kr") model = Pix2StructForConditionalGeneration.from_pretrained("brainventures/deplot_kr") image_path = "IMAGE_PATH" image = Image.open(image_path) inputs = processor(images=image, return_tensors="pt") pred = model.generate(flattened_patches=flattened_patches, attention_mask=attention_mask, max_length=1024) print(processor.batch_decode(deplot_generated_ids, skip_special_token=True)[0]) ``` Model Input Image ![model_input_image](./sample.jpg) Model Output - Prediction 대상: 제목: 2011-2021 보건복지 분야 일자리의 <unk>증 유형: 단일형 일반 세로 <unk>대형 \| 보건(천 명) \| 복지(천 명) 1분위 \| 29.7 \| 178.4 2분위 \| 70.8 \| 97.3 3분위 \| 86.4 \| 61.3 4분위 \| 28.2 \| 16.0 5분위 \| 52.3 \| 0.9 ### Preprocessing According to [Liu et al.(2023)](https://arxiv.org/pdf/2212.10505.pdf)... - markdown format - \| : seperating cells (열 구분) - \n : seperating rows (행 구분) ### Train The model was trained in a TPU environment. - num_warmup_steps : 1,000 - num_training_steps : 40,000 ## Evaluation Results This model achieves the following results: \|metrics name \| % \| \|:---\|---:\| \| RNSS (Relative Number Set Similarity)\| 98.1615 \| \|RMS (Relative Mapping Similarity) Precision \| 83.1615 \| \|RMS Recall \| 26.3549 \| \| RMS F1 Score \| 31.5633 \| ## Contact For questions and comments, please use the discussion tab or email [email protected]
seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF	seyf1elislam	2024-03-06T04:33:26Z	0	0	null	[ "gguf", "GGUF", "base_model:seyf1elislam/WestKunai-Hermes-long-128k-test-7b", "base_model:quantized:seyf1elislam/WestKunai-Hermes-long-128k-test-7b", "endpoints_compatible", "region:us" ]	null	2024-03-06T02:14:44Z	--- tags: - GGUF base_model: - seyf1elislam/WestKunai-Hermes-long-128k-test-7b --- # WestKunai-Hermes-long-128k-test-7b - Model creator: [seyf1elislam](https://huggingface.co/seyf1elislam) - Original model: [WestKunai-Hermes-long-128k-test-7b](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [seyf1elislam's WestKunai-Hermes-long-128k-test-7b ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b). ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [westkunai-hermes-long-128k-test-7b.Q2_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q2_K.gguf ) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [westkunai-hermes-long-128k-test-7b.Q3_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q3_K_M.gguf ) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [WestKunai-Hermes-long-128k-test-7b.Q4_K_S.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/WestKunai-Hermes-long-128k-test-7b.Q4_K_S.gguf ) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [westkunai-hermes-long-128k-test-7b.Q4_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q4_K_M.gguf ) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [westkunai-hermes-long-128k-test-7b.Q5_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q5_K_M.gguf ) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [westkunai-hermes-long-128k-test-7b.Q6_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q6_K.gguf ) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [westkunai-hermes-long-128k-test-7b.Q8_0.gguf ](https://huggingface.co/seyf1elislam/WestKunai-Hermes-long-128k-test-7b-GGUF/blob/main/westkunai-hermes-long-128k-test-7b.Q8_0.gguf ) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \|
bhaswata08/Llama-2-7b-chat-hf-function-calling-v3-AWQ	bhaswata08	2024-03-06T04:26:51Z	48	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "license:llama2", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-03-05T09:45:58Z	--- license: llama2 --- # Model Card for bhaswata08/Llama-2-7b-chat-hf-function-calling-v3-AWQ Model creator: Trelis Original model: Llama-2-7b-chat-hf-function-calling-v3 This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description - 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. 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DFJordan/binary-image-classifier	DFJordan	2024-03-06T04:23:02Z	8	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2024-03-06T04:03:43Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer datasets: - imagefolder model-index: - name: binary-image-classifier results: [] --- <!-- 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. --> # binary-image-classifier This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.1222 ## 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: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.1302 \| 1.0 \| 67 \| 0.1486 \| \| 0.0503 \| 2.0 \| 134 \| 0.1087 \| \| 0.0188 \| 3.0 \| 201 \| 0.1511 \| \| 0.0116 \| 4.0 \| 268 \| 0.1225 \| \| 0.0088 \| 5.0 \| 335 \| 0.1222 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
lcfrs/gemma-2b-it	lcfrs	2024-03-06T04:18:41Z	0	0	null	[ "gguf", "license:other", "endpoints_compatible", "region:us" ]	null	2024-03-06T03:55:44Z	--- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms --- Created by running `quantize` from `llama.cpp` on [gemma-2b-it.gguf](https://huggingface.co/google/gemma-2b-it-GGUF/blob/main/). ```sh llama.cpp $ ./quantize models/gemma-2b-it.gguf models/gemma-2b-it-Q4_K_M.gguf Q4_K_M main: build = 2351 (652ca2bd) main: built with Android (10552028, +pgo, +bolt, +lto, -mlgo, based on r487747d) clang version 17.0.2 (https://android.googlesource.com/toolchain/llvm-project d9f89f4d16663d5012e5c09495f3b30ece3d2362) for x86_64-apple-darwin22.5.0 main: quantizing 'models/gemma-2b-it.gguf' to 'models/gemma-2b-it-Q4_K_M.gguf' as Q4_K_M llama_model_loader: loaded meta data with 19 key-value pairs and 164 tensors from models/gemma-2b-it.gguf (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = gemma llama_model_loader: - kv 1: general.name str = gemma-2b-it llama_model_loader: - kv 2: gemma.context_length u32 = 8192 llama_model_loader: - kv 3: gemma.block_count u32 = 18 llama_model_loader: - kv 4: gemma.embedding_length u32 = 2048 llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 16384 llama_model_loader: - kv 6: gemma.attention.head_count u32 = 8 llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 1 llama_model_loader: - kv 8: gemma.attention.key_length u32 = 256 llama_model_loader: - kv 9: gemma.attention.value_length u32 = 256 llama_model_loader: - kv 10: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 11: tokenizer.ggml.model str = llama llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 2 llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 1 llama_model_loader: - kv 14: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 3 llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256128] = ["<pad>", "<eos>", "<bos>", "<unk>", ... llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256128] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256128] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - type f32: 164 tensors llama_model_quantize_internal: meta size = 6042528 bytes [..snip..] llama_model_quantize_internal: model size = 9561.29 MB llama_model_quantize_internal: quant size = 1549.19 MB main: quantize time = 27285.22 ms main: total time = 27285.22 ms ```
uname-n/tiny-aquatic-llama.10k	uname-n	2024-03-06T04:16:13Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "llama", "text-generation", "conversational", "dataset:Open-Orca/SlimOrca-Dedup", "dataset:uname-n/slim-orca-dedup-chat-10k", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-05T02:30:10Z	--- license: apache-2.0 datasets: - Open-Orca/SlimOrca-Dedup - uname-n/slim-orca-dedup-chat-10k widget: - text: "<\|system\|>\nYou are a chatbot who can help code!</s>\n<\|user\|>\nWrite me a function to calculate the first 10 digits of the fibonacci sequence in Python and print it out to the CLI.</s>\n<\|assistant\|>\n" --- <div align="center"> # Tiny Aquatic Llama </div> #### This Model This is a chat model finetuned on top of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T). The model was fine-tuned on a 10k sample from [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup). #### Note This model is deranged.
kaustavbhattacharjee/finetuning-DistillBERT-amazon-polarity	kaustavbhattacharjee	2024-03-06T04:11:33Z	8	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:amazon_polarity", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-05T23:03:33Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: finetuning-DistillBERT-amazon-polarity results: - task: type: text-classification name: Text Classification dataset: name: amazon_polarity type: sentiment args: default metrics: - type: accuracy value: 0.9166666666666666 name: Accuracy - type: loss value: 0.1919892132282257 name: Loss - type: f1 value: 0.9169435215946843 name: F1 datasets: - amazon_polarity pipeline_tag: text-classification --- <!-- 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. --> # finetuning-DistillBERT-amazon-polarity This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on [Amazon Polarity](https://huggingface.co/datasets/amazon_polarity) dataset. It achieves the following results on the evaluation set: - Loss: 0.1920 - Accuracy: 0.9167 - F1: 0.9169 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.38.1 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
OwOOwO/eacc_3_10	OwOOwO	2024-03-06T04:05:24Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T04:02:50Z	--- library_name: transformers tags: [] --- # 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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shg1421/t5_astrology_peft	shg1421	2024-03-06T04:01:27Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-03-06T01:20:56Z	--- library_name: transformers tags: [] --- # 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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erichen-0615/zephyr-7b-dpo-full	erichen-0615	2024-03-06T03:55:21Z	4	0	transformers	[ "transformers", "tensorboard", "safetensors", "mistral", "text-generation", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "conversational", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:alignment-handbook/zephyr-7b-sft-full", "base_model:finetune:alignment-handbook/zephyr-7b-sft-full", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-04T23:43:58Z	--- license: apache-2.0 base_model: alignment-handbook/zephyr-7b-sft-full tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: zephyr-7b-dpo-full results: [] --- <!-- 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. --> # zephyr-7b-dpo-full This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set: - Loss: 0.5026 - Rewards/chosen: -1.0472 - Rewards/rejected: -1.9901 - Rewards/accuracies: 0.7619 - Rewards/margins: 0.9430 - Logps/rejected: -460.7913 - Logps/chosen: -388.8264 - Logits/rejected: 1.7625 - Logits/chosen: 1.0392 ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 615 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - total_eval_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\| \| 0.5736 \| 0.21 \| 100 \| 0.5842 \| -0.3837 \| -0.8468 \| 0.7242 \| 0.4632 \| -346.4596 \| -322.4754 \| -2.3510 \| -2.4330 \| \| 0.5116 \| 0.42 \| 200 \| 0.5308 \| -0.9042 \| -1.7331 \| 0.7520 \| 0.8289 \| -435.0859 \| -374.5288 \| 0.5012 \| 0.0459 \| \| 0.5027 \| 0.63 \| 300 \| 0.5084 \| -0.8877 \| -1.7467 \| 0.7639 \| 0.8590 \| -436.4478 \| -372.8834 \| 1.8224 \| 1.1385 \| \| 0.4823 \| 0.84 \| 400 \| 0.5037 \| -1.1953 \| -2.1521 \| 0.7619 \| 0.9568 \| -476.9852 \| -403.6375 \| 1.9978 \| 1.3075 \| ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.0
Sumail/Bubble_bee04_2b	Sumail	2024-03-06T03:55:03Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "mergekit", "merge", "conversational", "base_model:tomaszki/gemma-28", "base_model:merge:tomaszki/gemma-28", "base_model:tomaszki/gemma-28-copy", "base_model:merge:tomaszki/gemma-28-copy", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T03:52:00Z	--- base_model: - tomaszki/gemma-28-copy - tomaszki/gemma-28 library_name: transformers tags: - mergekit - merge --- # 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: * [tomaszki/gemma-28-copy](https://huggingface.co/tomaszki/gemma-28-copy) * [tomaszki/gemma-28](https://huggingface.co/tomaszki/gemma-28) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: tomaszki/gemma-28 layer_range: [0, 18] - model: tomaszki/gemma-28-copy layer_range: [0, 18] merge_method: slerp base_model: tomaszki/gemma-28 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 ```
prajapatisushilkumar/lion	prajapatisushilkumar	2024-03-06T03:54:02Z	5	0	diffusers	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-03-06T03:47:31Z	--- license: creativeml-openrail-m tags: - NxtWave-GenAI-Webinar - text-to-image - stable-diffusion --- ### lion Dreambooth model trained by prajapatisushilkumar following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 23/cse/01 Sample pictures of this concept: ![0](https://huggingface.co/prajapatisushilkumar/lion/resolve/main/sample_images/ai.zip)
Granoladata/abnormality_classifier_biobert_v1_3_epochs	Granoladata	2024-03-06T03:52:27Z	6	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dmis-lab/biobert-v1.1", "base_model:finetune:dmis-lab/biobert-v1.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T03:52:18Z	--- base_model: dmis-lab/biobert-v1.1 tags: - generated_from_trainer metrics: - accuracy model-index: - name: abnormality_classifier_biobert_v1_3_epochs results: [] --- <!-- 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. --> # abnormality_classifier_biobert_v1_3_epochs This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2835 - Accuracy: 0.957 ## 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 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0099 \| 1.0 \| 1125 \| 0.2986 \| 0.952 \| \| 0.0048 \| 2.0 \| 2250 \| 0.2623 \| 0.961 \| \| 0.0063 \| 3.0 \| 3375 \| 0.2835 \| 0.957 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
dyang415/mixtral-fc-w-resp-new-format-4e-no-negative	dyang415	2024-03-06T03:47:37Z	7	0	peft	[ "peft", "safetensors", "mixtral", "generated_from_trainer", "base_model:mistralai/Mixtral-8x7B-Instruct-v0.1", "base_model:adapter:mistralai/Mixtral-8x7B-Instruct-v0.1", "license:apache-2.0", "4-bit", "bitsandbytes", "region:us" ]	null	2024-03-05T05:03:16Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 model-index: - name: mixtral-fc-w-resp-new-format-4e-no-negative results: [] --- <!-- 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. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: true strict: false chat_template: inst datasets: - path: ./data/with_function_response/function_not_used_training.jsonl type: sharegpt conversation: mistral # - path: ./data/with_function_response/no_function_training.jsonl # type: sharegpt # conversation: mistral - path: ./data/with_function_response/function_used_training.jsonl type: sharegpt conversation: mistral dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ../mixtral-fc-w-resp-new-format-4e-no-negative model_config: output_router_logits: true adapter: qlora lora_model_dir: sequence_len: 16384 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 64 lora_dropout: 0.05 lora_target_modules: - q_proj - k_proj - v_proj - o_proj wandb_project: function-call wandb_name: mixtral-instruct-lora-no-negative wandb_log_model: end hub_model_id: dyang415/mixtral-fc-w-resp-new-format-4e-no-negative gradient_accumulation_steps: 4 micro_batch_size: 2 num_epochs: 4 optimizer: paged_adamw_8bit lr_scheduler: cosine learning_rate: 0.0002 train_on_inputs: false group_by_length: false bf16: true fp16: false tf32: false gradient_checkpointing: true logging_steps: 1 flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_max_new_tokens: 128 saves_per_epoch: 1 debug: weight_decay: 0.0 fsdp: fsdp_config: ``` </details><br> # mixtral-fc-w-resp-new-format-4e-no-negative This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 4 ### Framework versions - PEFT 0.7.0 - Transformers 4.37.0 - Pytorch 2.0.1+cu117 - Datasets 2.17.1 - Tokenizers 0.15.0
Commandante/german-party-sentiment-bert	Commandante	2024-03-06T03:43:59Z	18	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:oliverguhr/german-sentiment-bert", "base_model:finetune:oliverguhr/german-sentiment-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-02-22T09:34:10Z	--- license: mit base_model: oliverguhr/german-sentiment-bert tags: - generated_from_trainer model-index: - name: german-party-sentiment-bert-complete-gsbert results: [] --- <!-- 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. --> # German-Party-Sentiment-Bert This model is a fine-tuned version of [oliverguhr/german-sentiment-bert](https://huggingface.co/oliverguhr/german-sentiment-bert) on a dataset consisting of mentions of german political parties. It achieves the following results on the evaluation set: - Loss: 0.8912 ## 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: 20 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 120 - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.2844 \| 1.0 \| 65 \| 0.9382 \| \| 0.9704 \| 2.0 \| 130 \| 0.8912 \| \| 0.7394 \| 3.0 \| 195 \| 1.0455 \| \| 0.5401 \| 4.0 \| 260 \| 1.2711 \| \| 0.4274 \| 5.0 \| 325 \| 1.3578 \| \| 0.2289 \| 6.0 \| 390 \| 1.6143 \| \| 0.1949 \| 7.0 \| 455 \| 1.8376 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Tokenizers 0.15.1
OwOOwO/eacc_dc3	OwOOwO	2024-03-06T03:43:30Z	5	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-03-06T03:23:31Z	--- library_name: transformers tags: [] --- # 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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seyf1elislam/WestKunai-XS-7b-GGUF	seyf1elislam	2024-03-06T03:43:19Z	22	0	null	[ "gguf", "GGUF", "base_model:seyf1elislam/WestKunai-XS-7b", "base_model:quantized:seyf1elislam/WestKunai-XS-7b", "endpoints_compatible", "region:us" ]	null	2024-03-06T01:41:02Z	--- tags: - GGUF base_model: - seyf1elislam/WestKunai-X-7b --- # WestKunai-X-7b - Model creator: [seyf1elislam](https://huggingface.co/seyf1elislam) - Original model: [WestKunai-X-7b](https://huggingface.co/seyf1elislam/WestKunai-X-7b) <!-- description start --> ## Description This repo contains GGUF format model files for [seyf1elislam's WestKunai-X-7b ](https://huggingface.co/seyf1elislam/WestKunai-X-7b). ## Provided files \| Name \| Quant method \| Bits \| Size \| Max RAM required \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [westkunai-x-7b.Q2_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q2_K.gguf ) \| Q2_K \| 2 \| 2.72 GB\| 5.22 GB \| significant quality loss - not recommended for most purposes \| \| [westkunai-x-7b.Q3_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q3_K_M.gguf ) \| Q3_K_M \| 3 \| 3.52 GB\| 6.02 GB \| very small, high quality loss \| \| [WestKunai-X-7b.Q4_K_S.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/WestKunai-X-7b.Q4_K_S.gguf ) \| Q4_K_S \| 4 \| 4.14 GB\| 6.64 GB \| small, greater quality loss \| \| [westkunai-x-7b.Q4_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q4_K_M.gguf ) \| Q4_K_M \| 4 \| 4.37 GB\| 6.87 GB \| medium, balanced quality - recommended \| \| [westkunai-x-7b.Q5_K_M.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q5_K_M.gguf ) \| Q5_K_M \| 5 \| 5.13 GB\| 7.63 GB \| large, very low quality loss - recommended \| \| [westkunai-x-7b.Q6_K.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q6_K.gguf ) \| Q6_K \| 6 \| 5.94 GB\| 8.44 GB \| very large, extremely low quality loss \| \| [westkunai-x-7b.Q8_0.gguf ](https://huggingface.co/seyf1elislam/WestKunai-X-7b-GGUF/blob/main/westkunai-x-7b.Q8_0.gguf ) \| Q8_0 \| 8 \| 7.70 GB\| 10.20 GB \| very large, extremely low quality loss - not recommended \|
DingosGotMyBaby/uhn-twitch-chat	DingosGotMyBaby	2024-03-06T03:34:11Z	100	2	transformers	[ "transformers", "pytorch", "safetensors", "gpt2", "text-generation", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-06-23T15:59:10Z	--- license: mit --- # A model based on UberHaxorNova's Twitch chat Trained on over 700 vods worth of chat and with some scuffed filtering it became a 300mb dataset. ## Dataset The dataset was created by downloading all the available vods at the time of creation as a json file and stripping out all the chat messages into a simple line-by-line text file. ## Training This was trained using [aitextgen](https://github.com/minimaxir/aitextgen), created by [Max Woolf](https://github.com/minimaxir), using the example notebook found [here](https://colab.research.google.com/drive/15qBZx5y9rdaQSyWpsreMDnTiZ5IlN0zD?usp=sharing). Using GPT-2's 124M model as the base, it was trained for 3000 steps and produces an output scuffed enough to look like a real Twitch chat user. ## Use This was created as a fun little project for the discord server and as such, should only be used for fun and not to harm people. This model must also follow the ethics guide of the tool that created it https://github.com/minimaxir/aitextgen/blob/master/docs/ethics.md
guy-smiley/flan-t5-small-samsum-3	guy-smiley	2024-03-06T03:33:26Z	8	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "dataset:samsum", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-02-12T18:29:37Z	--- library_name: transformers datasets: - samsum --- # Model Card for Model ID A version of google/flan-t5-small, fine-tuned on the samsum dataset. ## Model Details ### Model Description - Developed by: [guy-smiley](https://huggingface.co/guy-smiley) - Model type: Language model - Language(s) (NLP): English - Finetuned from model: [flan-t5-small](https://huggingface.co/google/flan-t5-small) ## Uses Chat and dialogue summarization ## Bias, Risks, and Limitations "Language models, including Flan-T5, can potentially be used for language generation in a harmful way, according to Rae et al. (2021). Flan-T5 should not be used directly in any application, without a prior assessment of safety and fairness concerns specific to the application." ## Training Details ### Training Data [samsum](https://huggingface.co/datasets/samsum) ### Training Procedure * Trained with [Seq2Se2Trainer](https://huggingface.co/docs/transformers/v4.38.2/en/main_classes/trainer#transformers.Seq2SeqTrainer) * GitHub repo: #### Training Hyperparameters * learning_rate: 0.00005 * 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 * epoch: 1 * train_loss: 1.83195 * eval * eval_loss: 1.67304 * eval_rouge1: 42.8081 * eval_rouge2: 18.6456 * eval_rougeL: 35.4345 * eval_rougeLsum: 39.1534
omzn/facemark_detection	omzn	2024-03-06T03:25:52Z	6	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "text-classification", "generated_from_trainer", "ja", "base_model:tohoku-nlp/bert-base-japanese-whole-word-masking", "base_model:finetune:tohoku-nlp/bert-base-japanese-whole-word-masking", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2023-01-09T03:30:58Z	--- language: ja license: cc-by-sa-4.0 tags: - generated_from_trainer - text-classification metrics: - accuracy widget: - text: 💪(^ω^ 🍤) example_title: Facemark 1 - text: (੭ु∂∀6)੭ु⁾⁾ ஐ•¨•.¸¸ example_title: Facemark 2 - text: :-P example_title: Facemark 3 - text: (o.o) example_title: Facemark 4 - text: （10/7~） example_title: Non-facemark 1 - text: ？？＜＜「ﾆｬｱ(しゃーねぇな)」ﾌﾟｲｯ example_title: Non-facemark 2 - text: (0.01) example_title: Non-facemark 3 base_model: cl-tohoku/bert-base-japanese-whole-word-masking --- <!-- 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. --> # Facemark Detection This model classifies given text into facemark(1) or not(0). This model is a fine-tuned version of [cl-tohoku/bert-base-japanese-whole-word-masking](https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking) on an original facemark dataset. It achieves the following results on the evaluation set: - Loss: 0.1301 - Accuracy: 0.9896 ## Model description This model classifies given text into facemark(1) or not(0). ## Intended uses & limitations Extract a facemark-prone potion of text and apply the text to the model. Extraction of a facemark can be done by regex but usually includes many non-facemarks. For example, I used the following regex pattern to extract a facemark-prone text by perl. ```perl $input_text = "facemark prne text" my $text = '[0-9A-Za-zぁ-ヶ一-龠]'; my $non_text = '[^0-9A-Za-zぁ-ヶ一-龠]'; my $allow_text = '[ovっつ゜ニノ三二]'; my $hw_kana = '[ｦ-ﾟ]'; my $open_branket = '[$∩꒰（]'; my $close_branket = '[$∩꒱）]'; my $around_face = '(?:' . $non_text . '\|' . $allow_text . ')'; my $face = '(?!(?:' . $text . '\|' . $hw_kana . '){3,8}).{3,8}'; my $face_char = $around_face . $open_branket . $face . $close_branket . $around_face; my $facemark; if ($input_text=~/($face_char)/) { $facemark = $1; } ``` Example of facemarks are: ``` （^U^）← 。\n\n⊂( ･ω･ )⊃ っ(｡＞﹏＜) ﾀｶ( ˘ω' ) ﾔｽｩ… 。(’↑▽↑) ……💰( ˘ω˘ )💰 ーーー(´꒳`)！（ …(o：∇：o) ！！…(；´Д｀)？ (´﹃｀)✿ ``` Examples of non-facemarks are: ``` (3,000円) : (1/3) (@nVApO) （10/7~）？＜＜「ﾆｬｱ(しゃーねぇな)」ﾌﾟｲｯ (残り 51字) (-0.1602) (25-0) （コーヒー飲んだ）（※軽トラ） ``` This model intended to use for a facemark-prone text like above. ## Training and evaluation data Facemark data is collected manually and automatically from Twitter timeline. * train.csv : 35591 samples (29911 facemark, 5680 non-facemark) * test.csv : 3954 samples (3315 facemark, 639 non-facemark) ## Training procedure ```bash python ./examples/pytorch/text-classification/run_glue.py \ --model_name_or_path=cl-tohoku/bert-base-japanese-whole-word-masking \ --do_train --do_eval \ --max_seq_length=128 --per_device_train_batch_size=32 \ --use_fast_tokenizer=False --learning_rate=2e-5 --num_train_epochs=50 \ --output_dir=facemark_classify \ --save_steps=1000 --save_total_limit=3 \ --train_file=train.csv \ --validation_file=test.csv ``` ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50.0 ### Training results It achieves the following results on the evaluation set: - Loss: 0.1301 - Accuracy: 0.9896 ### Framework versions - Transformers 4.26.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.7.1 - Tokenizers 0.13.2
eunyounglee/diverge-bert-finetuning-1	eunyounglee	2024-03-06T03:21:21Z	89	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "base_model:klue/bert-base", "base_model:finetune:klue/bert-base", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-03-06T02:29:39Z	--- license: cc-by-sa-4.0 base_model: klue/bert-base tags: - generated_from_trainer model-index: - name: diverge-bert-finetuning-1 results: [] --- <!-- 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. --> # diverge-bert-finetuning-1 This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) 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: 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: 10 ### Training results ### Framework versions - Transformers 4.32.1 - Pytorch 2.2.0+cu121 - Datasets 2.12.0 - Tokenizers 0.13.2
agnedil/Mistral-7B-openassistant-guanaco-v2	agnedil	2024-03-06T03:20:17Z	0	0	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-03-05T08:55:35Z	Model [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) fine-tuned on the [openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) dataset using the following [Colab notebook](https://colab.research.google.com/drive/19lYWzMvZAc2cWPojRiPnYIR5Ok62CgFQ?usp=drive_link).
julienkay/stable-diffusion-2-1	julienkay	2024-03-06T03:17:54Z	0	0	null	[ "onnx", "text-to-image", "license:openrail++", "region:us" ]	text-to-image	2024-02-15T20:47:45Z	--- license: openrail++ pipeline_tag: text-to-image --- The official [stabilityai/stable-diffusion-2-1](https://huggingface.co/stabilityai/stable-diffusion-2-1) model converted to ONNX for usage with Unity Sentis. See [com.doji.diffusers](https://github.com/julienkay/com.doji.diffusers) for details.
ho1iday/pokemon-lora	ho1iday	2024-03-06T03:14:20Z	1	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "diffusers-training", "lora", "base_model:runwayml/stable-diffusion-v1-5", "base_model:adapter:runwayml/stable-diffusion-v1-5", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-03-05T12:34:41Z	--- license: creativeml-openrail-m library_name: diffusers tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image - diffusers - diffusers-training - lora base_model: runwayml/stable-diffusion-v1-5 inference: true --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # LoRA text2image fine-tuning - ho1iday/pokemon-lora These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. 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) ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training details [TODO: describe the data used to train the model]
kharato/opt-125m-gptq-4bit	kharato	2024-03-06T03:10:38Z	5	0	transformers	[ "transformers", "safetensors", "opt", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-03-06T03:10:19Z	--- library_name: transformers tags: [] --- # 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. This model card has been automatically generated. - 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. 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