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modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-09-21 00:45:47	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 567 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-09-21 00:45:01	card stringlengths 11 1.01M
andakm/cars_new_classifier	andakm	2024-05-26T06:04:48Z	63	0	transformers	[ "transformers", "tf", "vit", "image-classification", "generated_from_keras_callback", "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	2023-12-20T16:20:51Z	--- license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_keras_callback model-index: - name: andakm/cars_new_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. --> # andakm/cars_new_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 an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 1.0611 - Train Accuracy: 0.6863 - 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': 2295, '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 \| Train Accuracy \| Epoch \| \|:----------:\|:--------------:\|:-----:\| \| 2.0876 \| 0.2941 \| 0 \| \| 1.8215 \| 0.3922 \| 1 \| \| 1.5758 \| 0.4510 \| 2 \| \| 1.3175 \| 0.5490 \| 3 \| \| 1.0611 \| 0.6863 \| 4 \| ### Framework versions - Transformers 4.41.1 - TensorFlow 2.15.0 - Datasets 2.19.1 - Tokenizers 0.19.1
Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-4_25bpw_exl2	Zoyd	2024-05-26T06:03:21Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T23:20:32Z	--- library_name: transformers license: llama3 --- Exllamav2 quant (exl2 / 4.25 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-2_2bpw_exl2)</center> \| <center>20886 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-2_5bpw_exl2)</center> \| <center>23200 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-3_0bpw_exl2)</center> \| <center>27269 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-3_5bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-3_75bpw_exl2)</center> \| <center>33395 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-4_0bpw_exl2)</center> \| <center>35426 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-4_25bpw_exl2)</center> \| <center>37478 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-5_0bpw_exl2)</center> \| <center>43559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-6_0bpw_exl2)</center> \| <center>51958 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-6_5bpw_exl2)</center> \| <center>56019 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-8_0bpw_exl2)</center> \| <center>61865 MB</center> \| <center>8</center> \| # Smaug-Llama-3-70B-Instruct-abliterated-v3 Model Card [My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb) I'll be honest: it just kinda bothered me Smaug isn't evil enough. This is [abacusai/Smaug-Llama-3-70B-Instruct](https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more. ## Hang on, "abliteration"? Orthogonalization? Ablation? What is this? TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out. TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model. As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes. Ablate + obliterated = Abliterated Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization. ## A little more on the methodology, and why this is interesting To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt. Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights. > Why this over fine-tuning? Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage. As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.) Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques. It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa. I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity. > Okay, fine, but why V3? There's no V2 70B? Well, I released a V2 a while back for 8B under Cognitive Computations. It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model. I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations. So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's such an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.) ## Quirkiness awareness notice This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects. If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored. Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
second-state/Qwen1.5-14B-Chat-GGUF	second-state	2024-05-26T06:00:22Z	116	4	transformers	[ "transformers", "gguf", "qwen2", "text-generation", "chat", "en", "base_model:Qwen/Qwen1.5-14B-Chat", "base_model:quantized:Qwen/Qwen1.5-14B-Chat", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-02-06T09:40:52Z	--- base_model: Qwen/Qwen1.5-14B-Chat license: other license_name: tongyi-qianwen-research license_link: >- https://huggingface.co/Qwen/Qwen1.5-14B-Chat/blob/main/LICENSE model_creator: Qwen model_name: Qwen1.5 14B Chat quantized_by: Second State Inc. language: - en pipeline_tag: text-generation tags: - chat --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-14B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-14B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Context size: `32000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-14B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-14B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Qwen1.5-14B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q2_K.gguf) \| Q2_K \| 2 \| 6.09 GB\| smallest, significant quality loss - not recommended for most purposes \| \| [Qwen1.5-14B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 7.84 GB\| small, substantial quality loss \| \| [Qwen1.5-14B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 7.42 GB\| very small, high quality loss \| \| [Qwen1.5-14B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 6.95 GB\| very small, high quality loss \| \| [Qwen1.5-14B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q4_0.gguf) \| Q4_0 \| 4 \| 8.18 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Qwen1.5-14B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 9.19 GB\| medium, balanced quality - recommended \| \| [Qwen1.5-14B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 8.56 GB\| small, greater quality loss \| \| [Qwen1.5-14B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q5_0.gguf) \| Q5_0 \| 5 \| 9.85 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Qwen1.5-14B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 10.5 GB\| large, very low quality loss - recommended \| \| [Qwen1.5-14B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 10.0 GB\| large, low quality loss - recommended \| \| [Qwen1.5-14B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q6_K.gguf) \| Q6_K \| 6 \| 12.3 GB\| very large, extremely low quality loss \| \| [Qwen1.5-14B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-14B-Chat-GGUF/blob/main/Qwen1.5-14B-Chat-Q8_0.gguf) \| Q8_0 \| 8 \| 15.1 GB\| very large, extremely low quality loss - not recommended \|
DiederikMartens/gBERT_sa_cv_10_fold5	DiederikMartens	2024-05-26T06:00:10Z	113	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T05:35:33Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_10_fold5 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. --> # gBERT_sa_cv_10_fold5 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5893 - F1: 0.6773 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.4275 \| 0.5400 \| \| 0.3946 \| 2.0 \| 802 \| 0.4152 \| 0.6578 \| \| 0.1794 \| 3.0 \| 1203 \| 0.5893 \| 0.6773 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
second-state/Qwen1.5-7B-Chat-GGUF	second-state	2024-05-26T05:59:54Z	91	1	transformers	[ "transformers", "gguf", "qwen2", "text-generation", "chat", "en", "base_model:Qwen/Qwen1.5-7B-Chat", "base_model:quantized:Qwen/Qwen1.5-7B-Chat", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-02-06T06:59:47Z	--- base_model: Qwen/Qwen1.5-7B-Chat license: other license_name: tongyi-qianwen-research license_link: >- https://huggingface.co/Qwen/Qwen1.5-7B-Chat/blob/main/LICENSE model_creator: Qwen model_name: Qwen1.5 7B Chat quantized_by: Second State Inc. language: - en pipeline_tag: text-generation tags: - chat --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-7B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-7B-Chat](https://huggingface.co/Qwen/Qwen1.5-7B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Context size: `32000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-7B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-7B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Qwen1.5-7B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q2_K.gguf) \| Q2_K \| 2 \| 3.10 GB\| smallest, significant quality loss - not recommended for most purposes \| \| [Qwen1.5-7B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 4.22 GB\| small, substantial quality loss \| \| [Qwen1.5-7B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.92 GB\| very small, high quality loss \| \| [Qwen1.5-7B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 3.57 GB\| very small, high quality loss \| \| [Qwen1.5-7B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_0.gguf) \| Q4_0 \| 4 \| 4.51 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Qwen1.5-7B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.77 GB\| medium, balanced quality - recommended \| \| [Qwen1.5-7B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 4.54 GB\| small, greater quality loss \| \| [Qwen1.5-7B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_0.gguf) \| Q5_0 \| 5 \| 5.40 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Qwen1.5-7B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 5.53 GB\| large, very low quality loss - recommended \| \| [Qwen1.5-7B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 5.4 GB\| large, low quality loss - recommended \| \| [Qwen1.5-7B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q6_K.gguf) \| Q6_K \| 6 \| 6.34 GB\| very large, extremely low quality loss \| \| [Qwen1.5-7B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-7B-Chat-GGUF/blob/main/Qwen1.5-7B-Chat-Q8_0.gguf) \| Q8_0 \| 8 \| 8.21 GB\| very large, extremely low quality loss - not recommended \|
YuyuTong/540526	YuyuTong	2024-05-26T05:58:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T05:58:21Z	--- license: apache-2.0 ---
second-state/Liberated-Qwen1.5-72B-GGUF	second-state	2024-05-26T05:58:06Z	48	1	transformers	[ "transformers", "gguf", "qwen2", "text-generation", "en", "base_model:abacusai/Liberated-Qwen1.5-72B", "base_model:quantized:abacusai/Liberated-Qwen1.5-72B", "autotrain_compatible", "region:us", "conversational" ]	text-generation	2024-03-07T09:17:08Z	--- base_model: abacusai/Liberated-Qwen1.5-72B inference: false library_name: transformers license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE model_creator: Abacus.AI and Eric Hartford model_name: Liberated Qwen1.5 72B pipeline_tag: text-generation quantized_by: Second State Inc. language: - en --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Liberated-Qwen1.5-72B-GGUF ## Original Model [abacusai/Liberated-Qwen1.5-72B](https://huggingface.co/abacusai/Liberated-Qwen1.5-72B) ## Run with LlamaEdge - LlamaEdge version: [v0.4.3](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.4.3) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Context size: `32000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Liberated-Qwen1.5-72B-Q4_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Liberated-Qwen1.5-72B-Q4_K_M.gguf llama-chat.wasm -p chatml ``` For specifying the system message, append the `--system-prompt` option with the system prompt to the command above. For example, ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Liberated-Qwen1.5-72B-Q4_K_M.gguf llama-chat.wasm -p chatml -s 'Your name is Liberated. You are an uncensored and unbiased AI assistant. You always respond with a JSON object.' ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Liberated-Qwen1.5-72B-Q2_K.gguf](https://huggingface.co/second-state/Liberated-Qwen1.5-72B-GGUF/blob/main/Liberated-Qwen1.5-72B-Q2_K.gguf) \| Q2_K \| 2 \| 28.5 GB\| smallest, significant quality loss - not recommended for most purposes \| \| [Liberated-Qwen1.5-72B-Q3_K_L.gguf](https://huggingface.co/second-state/Liberated-Qwen1.5-72B-GGUF/blob/main/Liberated-Qwen1.5-72B-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 38.5 GB\| small, substantial quality loss \| \| [Liberated-Qwen1.5-72B-Q3_K_M.gguf](https://huggingface.co/second-state/Liberated-Qwen1.5-72B-GGUF/blob/main/Liberated-Qwen1.5-72B-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 35.9 GB\| very small, high quality loss \| \| [Liberated-Qwen1.5-72B-Q3_K_S.gguf](https://huggingface.co/second-state/Liberated-Qwen1.5-72B-GGUF/blob/main/Liberated-Qwen1.5-72B-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 32.9 GB\| very small, high quality loss \| \| [Liberated-Qwen1.5-72B-Q4_0.gguf](https://huggingface.co/second-state/Liberated-Qwen1.5-72B-GGUF/blob/main/Liberated-Qwen1.5-72B-Q4_0.gguf) \| Q4_0 \| 4 \| 41 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Liberated-Qwen1.5-72B-Q4_K_M.gguf](https://huggingface.co/second-state/Liberated-Qwen1.5-72B-GGUF/blob/main/Liberated-Qwen1.5-72B-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 44.1 GB\| medium, balanced quality - recommended \| \| [Liberated-Qwen1.5-72B-Q4_K_S.gguf](https://huggingface.co/second-state/Liberated-Qwen1.5-72B-GGUF/blob/main/Liberated-Qwen1.5-72B-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 41.9 GB\| small, greater quality loss \| Quantized with llama.cpp b2334
Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-3_75bpw_exl2	Zoyd	2024-05-26T05:55:59Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-25T20:20:42Z	--- library_name: transformers license: llama3 --- Exllamav2 quant (exl2 / 3.75 bpw) made with ExLlamaV2 v0.0.21 Other EXL2 quants: \| Quant \| Model Size \| lm_head \| \| ----- \| ---------- \| ------- \| \|<center>[2.2](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-2_2bpw_exl2)</center> \| <center>20886 MB</center> \| <center>6</center> \| \|<center>[2.5](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-2_5bpw_exl2)</center> \| <center>23200 MB</center> \| <center>6</center> \| \|<center>[3.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-3_0bpw_exl2)</center> \| <center>27269 MB</center> \| <center>6</center> \| \|<center>[3.5](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-3_5bpw_exl2)</center> \| <center>31359 MB</center> \| <center>6</center> \| \|<center>[3.75](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-3_75bpw_exl2)</center> \| <center>33395 MB</center> \| <center>6</center> \| \|<center>[4.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-4_0bpw_exl2)</center> \| <center>35426 MB</center> \| <center>6</center> \| \|<center>[4.25](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-4_25bpw_exl2)</center> \| <center>37478 MB</center> \| <center>6</center> \| \|<center>[5.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-5_0bpw_exl2)</center> \| <center>43559 MB</center> \| <center>6</center> \| \|<center>[6.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-6_0bpw_exl2)</center> \| <center>51958 MB</center> \| <center>8</center> \| \|<center>[6.5](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-6_5bpw_exl2)</center> \| <center>56019 MB</center> \| <center>8</center> \| \|<center>[8.0](https://huggingface.co/Zoyd/failspy_Smaug-Llama-3-70B-Instruct-abliterated-v3-8_0bpw_exl2)</center> \| <center>61865 MB</center> \| <center>8</center> \| # Smaug-Llama-3-70B-Instruct-abliterated-v3 Model Card [My Jupyter "cookbook" to replicate the methodology can be found here, refined library coming soon](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb) I'll be honest: it just kinda bothered me Smaug isn't evil enough. This is [abacusai/Smaug-Llama-3-70B-Instruct](https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct) with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more. ## Hang on, "abliteration"? Orthogonalization? Ablation? What is this? TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out. TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model. As far as "abliteration": it's just a fun play-on-words using the original "ablation" term used in the original paper to refer to removing features, which I made up particularly to differentiate the model from "uncensored" fine-tunes. Ablate + obliterated = Abliterated Anyways, orthogonalization/ablation are both aspects to refer to the same thing here, the technique in which the refusal feature was "ablated" from the model was via orthogonalization. ## A little more on the methodology, and why this is interesting To me, ablation (or applying the methodology for the inverse, "augmentation") seems to be good for inducing/removing very specific features that you'd have to spend way too many tokens on encouraging or discouraging in your system prompt. Instead, you just apply your system prompt in the ablation script against a blank system prompt on the same dataset and orthogonalize for the desired behaviour in the final model weights. > Why this over fine-tuning? Ablation is much more surgical in nature whilst also being effectively executed with a _lot_ less data than fine-tuning, which I think is its main advantage. As well, and its most valuable aspect is it keeps as much of the original model's knowledge and training intact, whilst removing its tendency to behave in one very specific undesireable manner. (In this case, refusing user requests.) Fine tuning is still exceptionally useful and the go-to for broad behaviour changes; however, you may be able to get close to your desired behaviour with very few samples using the ablation/augmentation techniques. It may also be a useful step to add to your model refinement: orthogonalize -> fine-tune or vice-versa. I haven't really gotten around to exploring this model stacked with fine-tuning, I encourage others to give it a shot if they've got the capacity. > Okay, fine, but why V3? There's no V2 70B? Well, I released a V2 a while back for 8B under Cognitive Computations. It ended up being not worth it to try V2 with 70B, I wanted to refine the model before wasting compute cycles on what might not even be a better model. I am however quite pleased about this latest methodology, it seems to have induced fewer hallucinations. So to show that it's a new fancy methodology from even that of the 8B V2, I decided to do a Microsoft and double up on my version jump because it's such an advancement (or so the excuse went, when in actuality it was because too many legacy but actively used Microsoft libraries checked for 'Windows 9' in the OS name to detect Windows 95/98 as one.) ## Quirkiness awareness notice This model may come with interesting quirks, with the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects. If you manage to develop further improvements, please share! This is really the most basic way to use ablation, but there are other possibilities that I believe are as-yet unexplored. Additionally, feel free to reach out in any way about this. I'm on the Cognitive Computations Discord, I'm watching the Community tab, reach out! I'd love to see this methodology used in other ways, and so would gladly support whoever whenever I can.
second-state/Qwen1.5-1.8B-Chat-GGUF	second-state	2024-05-26T05:55:56Z	1,100	2	transformers	[ "transformers", "gguf", "qwen2", "text-generation", "chat", "en", "base_model:Qwen/Qwen1.5-1.8B-Chat", "base_model:quantized:Qwen/Qwen1.5-1.8B-Chat", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-02-06T04:33:23Z	--- base_model: Qwen/Qwen1.5-1.8B-Chat license: other license_name: tongyi-qianwen-research license_link: >- https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/blob/main/LICENSE model_creator: Qwen model_name: Qwen1.5 1.8B Chat quantized_by: Second State Inc. language: - en pipeline_tag: text-generation tags: - chat --- <!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </div> <hr style="margin-top: 1.0em; margin-bottom: 1.0em;"> <!-- header end --> # Qwen1.5-1.8B-Chat-GGUF ## Original Model [Qwen/Qwen1.5-1.8B-Chat](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat) ## Run with LlamaEdge - LlamaEdge version: [v0.2.15](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.2.15) and above - Prompt template - Prompt type: `chatml` - Prompt string ```text <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Context size: `32000` - Run as LlamaEdge service ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-1.8B-Chat-Q5_K_M.gguf llama-api-server.wasm -p chatml ``` - Run as LlamaEdge command app ```bash wasmedge --dir .:. --nn-preload default:GGML:AUTO:Qwen1.5-1.8B-Chat-Q5_K_M.gguf llama-chat.wasm -p chatml ``` ## Quantized GGUF Models \| Name \| Quant method \| Bits \| Size \| Use case \| \| ---- \| ---- \| ---- \| ---- \| ----- \| \| [Qwen1.5-1.8B-Chat-Q2_K.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q2_K.gguf) \| Q2_K \| 2 \| 863 MB\| smallest, significant quality loss - not recommended for most purposes \| \| [Qwen1.5-1.8B-Chat-Q3_K_L.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 1.06 GB\| small, substantial quality loss \| \| [Qwen1.5-1.8B-Chat-Q3_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 1.02 GB\| very small, high quality loss \| \| [Qwen1.5-1.8B-Chat-Q3_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 970 MB\| very small, high quality loss \| \| [Qwen1.5-1.8B-Chat-Q4_0.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q4_0.gguf) \| Q4_0 \| 4 \| 1.12 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \| \| [Qwen1.5-1.8B-Chat-Q4_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 1.22 GB\| medium, balanced quality - recommended \| \| [Qwen1.5-1.8B-Chat-Q4_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 1.16 GB\| small, greater quality loss \| \| [Qwen1.5-1.8B-Chat-Q5_0.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q5_0.gguf) \| Q5_0 \| 5 \| 1.31 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \| \| [Qwen1.5-1.8B-Chat-Q5_K_M.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 1.38 GB\| large, very low quality loss - recommended \| \| [Qwen1.5-1.8B-Chat-Q5_K_S.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 1.33 GB\| large, low quality loss - recommended \| \| [Qwen1.5-1.8B-Chat-Q6_K.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q6_K.gguf) \| Q6_K \| 6 \| 1.58 GB\| very large, extremely low quality loss \| \| [Qwen1.5-1.8B-Chat-Q8_0.gguf](https://huggingface.co/second-state/Qwen1.5-1.8B-Chat-GGUF/blob/main/Qwen1.5-1.8B-Chat-Q8_0.gguf) \| Q8_0 \| 8 \| 1.96 GB\| very large, extremely low quality loss - not recommended \|
katryo/controlnet-facesynthetics-spiga-sdxl-15000	katryo	2024-05-26T05:52:29Z	2	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-26T04:51:43Z	--- license: openrail++ library_name: diffusers tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet - diffusers-training base_model: stabilityai/stable-diffusion-xl-base-1.0 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. --> # controlnet-katryo/controlnet-facesynthetics-spiga-sdxl-15000 These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below. prompt: a close-up of a man ![images_0)](./images_0.png) prompt: a close-up of a woman ![images_1)](./images_1.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]
godlzj/SDXL_CKPT	godlzj	2024-05-26T05:51:31Z	0	0	null	[ "region:us" ]	null	2024-05-25T17:53:40Z	转载自https://civitai.com/models/139565?modelVersionId=294470 Reprinted from https://civitai.com/models/139565?modelVersionId=294470
ShenRu/TT011	ShenRu	2024-05-26T05:49:58Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T05:49:58Z	--- license: apache-2.0 ---
DiederikMartens/eBERT_sa_cv_10_fold4	DiederikMartens	2024-05-26T05:48:53Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T05:22:23Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_10_fold4 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. --> # eBERT_sa_cv_10_fold4 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4709 - F1: 0.4896 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.6001 \| 0.3375 \| \| 0.6001 \| 2.0 \| 802 \| 0.4709 \| 0.4896 \| \| 0.4331 \| 3.0 \| 1203 \| 0.4930 \| 0.4776 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
DiederikMartens/tsBERT_sa_cv_10_fold4	DiederikMartens	2024-05-26T05:46:46Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:igorsterner/german-english-code-switching-bert", "base_model:finetune:igorsterner/german-english-code-switching-bert", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T05:20:36Z	--- license: mit base_model: igorsterner/german-english-code-switching-bert tags: - generated_from_trainer metrics: - f1 model-index: - name: tsBERT_sa_cv_10_fold4 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. --> # tsBERT_sa_cv_10_fold4 This model is a fine-tuned version of [igorsterner/german-english-code-switching-bert](https://huggingface.co/igorsterner/german-english-code-switching-bert) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5768 - F1: 0.6619 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.4331 \| 0.5843 \| \| 0.4074 \| 2.0 \| 802 \| 0.4577 \| 0.6317 \| \| 0.2191 \| 3.0 \| 1203 \| 0.5768 \| 0.6619 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
tsang326/test2605	tsang326	2024-05-26T05:42:07Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "base_model:vilm/vinallama-7b-chat", "base_model:adapter:vilm/vinallama-7b-chat", "license:llama2", "region:us" ]	null	2024-05-26T05:41:51Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: vilm/vinallama-7b-chat model-index: - name: test2605 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. --> # test2605 This model is a fine-tuned version of [vilm/vinallama-7b-chat](https://huggingface.co/vilm/vinallama-7b-chat) 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.36.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.15.2
team-sanai/zoo_3exp_v2_2epoch_5000	team-sanai	2024-05-26T05:34:38Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T05:27:21Z	--- 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. (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]
roofdancer/plain-bart-on-presummarized-2-clusters-wcep	roofdancer	2024-05-26T05:34:24Z	112	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "generated_from_trainer", "base_model:sshleifer/distilbart-cnn-6-6", "base_model:finetune:sshleifer/distilbart-cnn-6-6", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-26T04:48:58Z	--- license: apache-2.0 base_model: sshleifer/distilbart-cnn-6-6 tags: - generated_from_trainer metrics: - rouge model-index: - name: plain-bart-on-presummarized-2-clusters-wcep 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. --> # plain-bart-on-presummarized-2-clusters-wcep This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0775 - Rouge1: 36.3774 - Rouge2: 15.2074 - Rougel: 25.7706 - Rougelsum: 29.2593 - Gen Len: 67.6608 ## 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 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.2178 \| 1.0 \| 510 \| 2.0873 \| 36.3079 \| 15.0162 \| 25.5837 \| 29.129 \| 67.8461 \| \| 1.8901 \| 2.0 \| 1020 \| 2.0696 \| 36.0914 \| 15.0005 \| 25.6729 \| 29.2956 \| 68.3451 \| \| 1.7267 \| 3.0 \| 1530 \| 2.0775 \| 36.3774 \| 15.2074 \| 25.7706 \| 29.2593 \| 67.6608 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
RSFfen/distilbert-base-uncased-finetuned-imdb	RSFfen	2024-05-26T05:31:53Z	108	0	transformers	[ "transformers", "safetensors", "distilbert", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-26T05:27:45Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-imdb 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. --> # distilbert-base-uncased-finetuned-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.4895 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.6838 \| 1.0 \| 157 \| 2.5107 \| \| 2.5895 \| 2.0 \| 314 \| 2.4504 \| \| 2.531 \| 3.0 \| 471 \| 2.4822 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
shkna1368/hazhar-hemen	shkna1368	2024-05-26T05:29:26Z	106	0	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-26T05:28:04Z	--- 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. (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]
saransh03sharma/mintrec2-llama-2-7b-200-10	saransh03sharma	2024-05-26T05:24:56Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T18:14:31Z	--- 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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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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DiederikMartens/eBERT_sa_cv_10_fold3	DiederikMartens	2024-05-26T05:22:10Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:55:17Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_10_fold3 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. --> # eBERT_sa_cv_10_fold3 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5231 - F1: 0.5195 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.5293 \| 0.4312 \| \| 0.5713 \| 2.0 \| 802 \| 0.4941 \| 0.4680 \| \| 0.3994 \| 3.0 \| 1203 \| 0.5231 \| 0.5195 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
auchoi/unslot_practice_lora_model_5_epoch	auchoi	2024-05-26T05:08:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "mistral", "trl", "en", "base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T05:07:36Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit --- # Uploaded model - Developed by: auchoi - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sorour/mistral_cls_fomc_v3	Sorour	2024-05-26T05:02:21Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T04:56:44Z	--- 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. (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]
DiederikMartens/mBERT_sa_cv_10_fold2	DiederikMartens	2024-05-26T04:52:57Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:26:59Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: mBERT_sa_cv_10_fold2 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. --> # mBERT_sa_cv_10_fold2 This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4393 - F1: 0.5954 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.5961 \| 0.4534 \| \| 0.5446 \| 2.0 \| 802 \| 0.4266 \| 0.4988 \| \| 0.3965 \| 3.0 \| 1203 \| 0.4393 \| 0.5954 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
keitokei1994/Llama-3-8B-shisa-2x8B-gguf	keitokei1994	2024-05-26T04:52:49Z	5	0	null	[ "gguf", "license:llama3", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-24T18:49:04Z	--- license: llama3 --- # Llama-3-8B-shisa-2x8B-gguf [Llama-3-8B-shisa-2x8B](https://huggingface.co/keitokei1994/Llama-3-8B-shisa-2x8B)のggufフォーマット変換版です。
GENIAC-Team-Ozaki/full-sft-finetuned-stage4-iter86000-v4-cont-neftune-5	GENIAC-Team-Ozaki	2024-05-26T04:50:51Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T04:46:32Z	--- 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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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
katryo/controlnet-facesynthetics-spiga-sdxl-10000	katryo	2024-05-26T04:49:41Z	2	0	diffusers	[ "diffusers", "safetensors", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "controlnet", "diffusers-training", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0", "license:openrail++", "region:us" ]	text-to-image	2024-05-26T04:06:23Z	--- license: openrail++ library_name: diffusers tags: - stable-diffusion-xl - stable-diffusion-xl-diffusers - text-to-image - diffusers - controlnet - diffusers-training base_model: stabilityai/stable-diffusion-xl-base-1.0 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. --> # controlnet-katryo/controlnet-facesynthetics-spiga-sdxl-10000 These are controlnet weights trained on stabilityai/stable-diffusion-xl-base-1.0 with new type of conditioning. You can find some example images below. prompt: a close-up of a man ![images_0)](./images_0.png) prompt: a close-up of a woman ![images_1)](./images_1.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]
amir1226/ppo-LunarLander-v2-rl	amir1226	2024-05-26T04:47:41Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-26T04:47:22Z	--- 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: 261.79 +/- 19.42 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 ... ```
DiederikMartens/gBERT_sa_cv_10_fold2	DiederikMartens	2024-05-26T04:46:38Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:22:59Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_10_fold2 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. --> # gBERT_sa_cv_10_fold2 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4944 - F1: 0.6926 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.3225 \| 0.6749 \| \| 0.3982 \| 2.0 \| 802 \| 0.3810 \| 0.6846 \| \| 0.1835 \| 3.0 \| 1203 \| 0.4944 \| 0.6926 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
poojapremnath/SnakeCLEF-resnet	poojapremnath	2024-05-26T04:45:16Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T04:35:43Z	--- license: apache-2.0 ---
mradermacher/Daredevil-8B-GGUF	mradermacher	2024-05-26T04:37:44Z	59	1	transformers	[ "transformers", "gguf", "merge", "mergekit", "lazymergekit", "en", "base_model:mlabonne/Daredevil-8B", "base_model:quantized:mlabonne/Daredevil-8B", "license:other", "endpoints_compatible", "region:us" ]	null	2024-05-26T03:36:18Z	--- base_model: mlabonne/Daredevil-8B language: - en library_name: transformers license: other quantized_by: mradermacher tags: - merge - mergekit - lazymergekit --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> static quants of https://huggingface.co/mlabonne/Daredevil-8B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q2_K.gguf) \| Q2_K \| 3.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ3_S.gguf) \| IQ3_S \| 3.8 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ3_M.gguf) \| IQ3_M \| 3.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q3_K_M.gguf) \| Q3_K_M \| 4.1 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.6 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.8 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q4_K_M.gguf) \| Q4_K_M \| 5.0 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q5_K_M.gguf) \| Q5_K_M \| 5.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q6_K.gguf) \| Q6_K \| 6.7 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.Q8_0.gguf) \| Q8_0 \| 8.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Daredevil-8B-GGUF/resolve/main/Daredevil-8B.f16.gguf) \| f16 \| 16.2 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
DiederikMartens/eBERT_sa_cv_10_fold1	DiederikMartens	2024-05-26T04:27:49Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-cased", "base_model:finetune:google-bert/bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T04:00:47Z	--- license: apache-2.0 base_model: google-bert/bert-base-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: eBERT_sa_cv_10_fold1 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. --> # eBERT_sa_cv_10_fold1 This model is a fine-tuned version of [google-bert/bert-base-cased](https://huggingface.co/google-bert/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4772 - F1: 0.4637 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.6262 \| 0.2953 \| \| 0.6031 \| 2.0 \| 802 \| 0.5669 \| 0.4470 \| \| 0.4469 \| 3.0 \| 1203 \| 0.4772 \| 0.4637 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
khnhlinh/gpt-on-hugging-face	khnhlinh	2024-05-26T04:27:35Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T04:27:35Z	--- license: apache-2.0 ---
DiederikMartens/gBERT_sa_cv_10_fold1	DiederikMartens	2024-05-26T04:22:46Z	111	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-german-cased", "base_model:finetune:google-bert/bert-base-german-cased", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T03:59:05Z	--- license: mit base_model: google-bert/bert-base-german-cased tags: - generated_from_trainer metrics: - f1 model-index: - name: gBERT_sa_cv_10_fold1 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. --> # gBERT_sa_cv_10_fold1 This model is a fine-tuned version of [google-bert/bert-base-german-cased](https://huggingface.co/google-bert/bert-base-german-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3789 - F1: 0.6518 ## 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: 4.47e-05 - train_batch_size: 16 - eval_batch_size: 32 - 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 \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| No log \| 1.0 \| 401 \| 0.4394 \| 0.5390 \| \| 0.3976 \| 2.0 \| 802 \| 0.3789 \| 0.6518 \| \| 0.1916 \| 3.0 \| 1203 \| 0.4834 \| 0.6415 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
stablediffusionapi/fluently-xl	stablediffusionapi	2024-05-26T04:21:03Z	29	2	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2024-05-26T04:17:58Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # Fluently XL API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/17728965371716696938.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 "fluently-xl" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com) Try model for free: [Generate Images](https://modelslab.com/models/fluently-xl) Model link: [View model](https://modelslab.com/models/fluently-xl) 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": "fluently-xl", "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
TroyDoesAI/Contextual-Llama3-8B-RAG	TroyDoesAI	2024-05-26T04:18:18Z	10	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:cc-by-nd-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T04:10:52Z	--- license: cc-by-nd-4.0 ---
Anish13/results_sratch	Anish13	2024-05-26T04:17:37Z	36	0	transformers	[ "transformers", "safetensors", "transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-05-26T02:49:14Z	--- tags: - generated_from_trainer model-index: - name: results_sratch 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. --> # results_sratch This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.4991 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 123 - 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_steps: 30 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:---------------:\| \| 3.4563 \| 5.5310 \| 10000 \| 3.3200 \| \| 2.7398 \| 11.0619 \| 20000 \| 2.7421 \| \| 2.441 \| 16.5929 \| 30000 \| 2.4991 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
Kaballas/Kaballas	Kaballas	2024-05-26T04:14:19Z	36	0	transformers	[ "transformers", "safetensors", "bert", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T04:06:29Z	--- license: apache-2.0 ---
cti-ttp-18/ttp-extraction-llama	cti-ttp-18	2024-05-26T04:14:07Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T03:49:26Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers widget: - messages: - role: user content: What is your favorite condiment? 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) ```
mrovejaxd/FNST_trad_j	mrovejaxd	2024-05-26T04:10:48Z	10	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T02:03:33Z	--- base_model: dccuchile/bert-base-spanish-wwm-cased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: FNST_trad_j 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. --> # FNST_trad_j This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6540 - Accuracy: 0.6525 - F1: 0.6178 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 32 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 1.1058 \| 1.0 \| 1500 \| 1.0564 \| 0.5442 \| 0.3843 \| \| 0.9559 \| 2.0 \| 3000 \| 0.9522 \| 0.585 \| 0.5503 \| \| 0.8789 \| 3.0 \| 4500 \| 0.8843 \| 0.61 \| 0.5733 \| \| 0.8292 \| 4.0 \| 6000 \| 0.8614 \| 0.6167 \| 0.5734 \| \| 0.7807 \| 5.0 \| 7500 \| 0.8519 \| 0.62 \| 0.5896 \| \| 0.7559 \| 6.0 \| 9000 \| 0.8648 \| 0.6283 \| 0.5965 \| \| 0.7098 \| 7.0 \| 10500 \| 0.8579 \| 0.63 \| 0.5961 \| \| 0.6703 \| 8.0 \| 12000 \| 0.8536 \| 0.6417 \| 0.6029 \| \| 0.6114 \| 9.0 \| 13500 \| 0.8686 \| 0.6358 \| 0.5997 \| \| 0.611 \| 10.0 \| 15000 \| 0.8948 \| 0.6342 \| 0.6045 \| \| 0.5614 \| 11.0 \| 16500 \| 0.9173 \| 0.6342 \| 0.6046 \| \| 0.515 \| 12.0 \| 18000 \| 0.9289 \| 0.6425 \| 0.6089 \| \| 0.5107 \| 13.0 \| 19500 \| 0.9581 \| 0.64 \| 0.6052 \| \| 0.4691 \| 14.0 \| 21000 \| 1.0099 \| 0.6433 \| 0.6091 \| \| 0.4476 \| 15.0 \| 22500 \| 1.0543 \| 0.6458 \| 0.6108 \| \| 0.398 \| 16.0 \| 24000 \| 1.1170 \| 0.6425 \| 0.6051 \| \| 0.3828 \| 17.0 \| 25500 \| 1.1585 \| 0.6517 \| 0.6102 \| \| 0.3567 \| 18.0 \| 27000 \| 1.2252 \| 0.6475 \| 0.6114 \| \| 0.3334 \| 19.0 \| 28500 \| 1.2827 \| 0.6675 \| 0.6317 \| \| 0.2982 \| 20.0 \| 30000 \| 1.4256 \| 0.6517 \| 0.6257 \| \| 0.2734 \| 21.0 \| 31500 \| 1.4591 \| 0.6583 \| 0.6305 \| \| 0.2556 \| 22.0 \| 33000 \| 1.5516 \| 0.66 \| 0.6263 \| \| 0.2409 \| 23.0 \| 34500 \| 1.6793 \| 0.6592 \| 0.6219 \| \| 0.2226 \| 24.0 \| 36000 \| 1.8157 \| 0.66 \| 0.6218 \| \| 0.1971 \| 25.0 \| 37500 \| 1.9089 \| 0.6575 \| 0.6241 \| \| 0.1832 \| 26.0 \| 39000 \| 2.0406 \| 0.6558 \| 0.6300 \| \| 0.1921 \| 27.0 \| 40500 \| 2.1448 \| 0.6583 \| 0.6254 \| \| 0.1496 \| 28.0 \| 42000 \| 2.2888 \| 0.6458 \| 0.6136 \| \| 0.1538 \| 29.0 \| 43500 \| 2.3520 \| 0.66 \| 0.6241 \| \| 0.1558 \| 30.0 \| 45000 \| 2.4748 \| 0.6492 \| 0.6207 \| \| 0.1409 \| 31.0 \| 46500 \| 2.5126 \| 0.6542 \| 0.6175 \| \| 0.119 \| 32.0 \| 48000 \| 2.6540 \| 0.6525 \| 0.6178 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
Mantis-VL/mantis-8b-idefics2-video-eval-20k_2048	Mantis-VL	2024-05-26T04:08:26Z	8	0	transformers	[ "transformers", "safetensors", "idefics2", "image-text-to-text", "generated_from_trainer", "base_model:HuggingFaceM4/idefics2-8b", "base_model:finetune:HuggingFaceM4/idefics2-8b", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-19T09:43:11Z	--- license: apache-2.0 base_model: HuggingFaceM4/idefics2-8b tags: - generated_from_trainer model-index: - name: mantis-8b-idefics2-video-eval-20k_2048 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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/dongfu/Mantis/runs/f0l8j9ep) # mantis-8b-idefics2-video-eval-20k_2048 This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) 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: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 3.0 ### Training results ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1
QuangDuy/whisper-large-v3-common_voice	QuangDuy	2024-05-26T04:07:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-26T04:07:07Z	--- 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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SergeiAi/ppo-LunarLander-v2	SergeiAi	2024-05-26T04:06:35Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-26T04:06:12Z	--- 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: 205.60 +/- 46.26 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 ... ```
MVRL/croma-large	MVRL	2024-05-26T04:04:10Z	51	0	transformers	[ "transformers", "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "endpoints_compatible", "region:us" ]	null	2024-05-26T04:02:43Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
rupesh2009/tiny-chatbot-dpo	rupesh2009	2024-05-26T03:54:47Z	6	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "dpo", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-26T03:52:41Z	--- license: apache-2.0 library_name: peft tags: - trl - dpo - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: tiny-chatbot-dpo 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. --> # tiny-chatbot-dpo This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
elliotthwang/KimLan-Mistral-7B-Instruct-v0.3	elliotthwang	2024-05-26T03:45:24Z	0	0	transformers	[ "transformers", "safetensors", "unsloth", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T13:50:56Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Sorour/cls_fomc_mistral_v1	Sorour	2024-05-26T03:41:27Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "dataset:generator", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-19T03:20:11Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 datasets: - generator model-index: - name: cls_fomc_mistral_v1 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. --> # cls_fomc_mistral_v1 This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset. It achieves the following results on the evaluation set: - Loss: 0.6185 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 0.5623 \| 1.2903 \| 20 \| 0.6185 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.41.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
armabird/EPonyAndOOO	armabird	2024-05-26T03:39:45Z	0	1	null	[ "StableDiffusionXL", "PonyDiffusionXL", "en", "license:other", "region:us" ]	null	2024-05-25T18:17:45Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en tags: - StableDiffusionXL - PonyDiffusionXL --- # About the model - This model was created by merging the following two model files.<br> 1. ebara_pony_2<br>https://huggingface.co/tsukihara/xl_model 2. ooo_beta71<br>https://civitai.com/models/179340?modelVersionId=407892 # License - Follow those licenses.<br> 1. [Pony Diffusion V6 XL](https://civitai.com/models/257749/pony-diffusion-v6-xl) 2. [OOO License](https://civitai.com/models/license/407892) 3. [Stable Diffusion XL 1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md) 4. [Fair AI Public License 1.0-SD](https://freedevproject.org/faipl-1.0-sd/)
suthanhcong/movie_summarize_model	suthanhcong	2024-05-26T03:31:44Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "base_model:google-t5/t5-small", "base_model:finetune:google-t5/t5-small", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-26T03:31:28Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer metrics: - rouge model-index: - name: movie_summarize_model 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. --> # movie_summarize_model This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 3.3072 - Rouge1: 0.1621 - Rouge2: 0.0398 - Rougel: 0.1305 - Rougelsum: 0.1304 - Gen Len: 18.9634 ## 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 3.5827 \| 1.0 \| 573 \| 3.3072 \| 0.1621 \| 0.0398 \| 0.1305 \| 0.1304 \| 18.9634 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
JinbiaoZhu/gemma-2b-it-QLoRA-RobotPlanning-v2	JinbiaoZhu	2024-05-26T03:29:18Z	10	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T06:05:09Z	--- 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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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
rupesh2009/sft-tiny-chatbot	rupesh2009	2024-05-26T03:14:08Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0", "license:apache-2.0", "region:us" ]	null	2024-05-26T03:12:46Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 model-index: - name: sft-tiny-chatbot 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. --> # sft-tiny-chatbot This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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: 1 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.41.0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
euiyulsong/BrierPC_correct	euiyulsong	2024-05-26T03:06:23Z	80	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "trl", "orpo", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-26T03:02:13Z	--- library_name: transformers tags: - trl - orpo --- # 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. (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]
mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF	mradermacher	2024-05-26T03:05:54Z	16	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-3", "70b", "smaug", "lumimaid", "tess", "arimas", "breadcrums", "en", "base_model:ryzen88/Llama-3-70b-Arimas-story-RP-V1", "base_model:quantized:ryzen88/Llama-3-70b-Arimas-story-RP-V1", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T13:40:03Z	--- base_model: ryzen88/Llama-3-70b-Arimas-story-RP-V1 language: - en library_name: transformers quantized_by: mradermacher tags: - mergekit - merge - llama-3 - 70b - smaug - lumimaid - tess - arimas - breadcrums --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/ryzen88/Llama-3-70b-Arimas-story-RP-V1 <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q2_K.gguf) \| Q2_K \| 26.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ3_XS.gguf) \| IQ3_XS \| 29.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ3_S.gguf) \| IQ3_S \| 31.0 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q3_K_S.gguf) \| Q3_K_S \| 31.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ3_M.gguf) \| IQ3_M \| 32.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q3_K_M.gguf) \| Q3_K_M \| 34.4 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q3_K_L.gguf) \| Q3_K_L \| 37.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.IQ4_XS.gguf) \| IQ4_XS \| 38.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q4_K_S.gguf) \| Q4_K_S \| 40.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q4_K_M.gguf) \| Q4_K_M \| 42.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q5_K_S.gguf) \| Q5_K_S \| 48.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q5_K_M.gguf) \| Q5_K_M \| 50.0 \| \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q6_K.gguf.part2of2) \| Q6_K \| 58.0 \| very good quality \| \| [PART 1](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-70b-Arimas-story-RP-V1-GGUF/resolve/main/Llama-3-70b-Arimas-story-RP-V1.Q8_0.gguf.part2of2) \| Q8_0 \| 75.1 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
QuantFactory/pair-preference-model-LLaMA3-8B-GGUF	QuantFactory	2024-05-26T03:05:22Z	39	1	transformers	[ "transformers", "gguf", "llama", "conversational", "text-generation", "arxiv:2405.07863", "base_model:RLHFlow/pair-preference-model-LLaMA3-8B", "base_model:quantized:RLHFlow/pair-preference-model-LLaMA3-8B", "license:llama3", "endpoints_compatible", "region:us" ]	text-generation	2024-05-24T15:24:15Z	--- license: llama3 base_model: RLHFlow/pair-preference-model-LLaMA3-8B library_name: transformers pipeline_tag: text-generation tags: - llama - conversational --- # pair-preference-model-LLaMA3-8B-GGUF This is quantized version of [RLHFlow/pair-preference-model-LLaMA3-8B](https://huggingface.co/RLHFlow/pair-preference-model-LLaMA3-8B) created using llama.cpp # Model Description This preference model is trained from [LLaMA3-8B-it](meta-llama/Meta-Llama-3-8B-Instruct) with the training script at [Reward Modeling](https://github.com/RLHFlow/RLHF-Reward-Modeling/tree/pm_dev/pair-pm). The dataset is RLHFlow/pair_preference_model_dataset. It achieves Chat-98.6, Char-hard 65.8, Safety 89.6, and reasoning 94.9 in reward bench. See our paper [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/abs/2405.07863) for more details of this model. ## Service the RM Here is an example to use the Preference Model to rank a pair. For n>2 responses, it is recommened to use the tournament style ranking strategy to get the best response so that the complexity is linear in n. ```python device = 0 model = AutoModelForCausalLM.from_pretrained(script_args.preference_name_or_path, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2").cuda() tokenizer = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain = AutoTokenizer.from_pretrained(script_args.preference_name_or_path, use_fast=True) tokenizer_plain.chat_template = "\n{% for message in messages %}{% if loop.index0 % 2 == 0 %}\n\n<turn> user\n {{ message['content'] }}{% else %}\n\n<turn> assistant\n {{ message['content'] }}{% endif %}{% endfor %}\n\n\n" prompt_template = "[CONTEXT] {context} [RESPONSE A] {response_A} [RESPONSE B] {response_B} \n" token_id_A = tokenizer.encode("A", add_special_tokens=False) token_id_B = tokenizer.encode("B", add_special_tokens=False) assert len(token_id_A) == 1 and len(token_id_B) == 1 token_id_A = token_id_A[0] token_id_B = token_id_B[0] temperature = 1.0 model.eval() response_chosen = "BBBB" response_rejected = "CCCC" ## We can also handle multi-turn conversation. instruction = [{"role": "user", "content": ...}, {"role": "assistant", "content": ...}, {"role": "user", "content": ...}, ] context = tokenizer_plain.apply_chat_template(instruction, tokenize=False) responses = [response_chosen, response_rejected] probs_chosen = [] for chosen_position in [0, 1]: # we swap order to mitigate position bias response_A = responses[chosen_position] response_B = responses[1 - chosen_position] prompt = prompt_template.format(context=context, response_A=response_A, response_B=response_B) message = [ {"role": "user", "content": prompt}, ] input_ids = tokenizer.encode(tokenizer.apply_chat_template(message, tokenize=False).replace(tokenizer.bos_token, ""), return_tensors='pt', add_special_tokens=False).cuda() with torch.no_grad(): output = model(input_ids) logit_A = output.logits[0, -1, token_id_A].item() logit_B = output.logits[0, -1, token_id_B].item() # take softmax to get the probability; using numpy Z = np.exp(logit_A / temperature) + np.exp(logit_B / temperature) logit_chosen = [logit_A, logit_B][chosen_position] prob_chosen = np.exp(logit_chosen / temperature) / Z probs_chosen.append(prob_chosen) avg_prob_chosen = np.mean(probs_chosen) correct = 0.5 if avg_prob_chosen == 0.5 else float(avg_prob_chosen > 0.5) print(correct) ```
LarryAIDraw/aidxlv05_neg	LarryAIDraw	2024-05-26T03:05:03Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-26T02:58:15Z	--- license: creativeml-openrail-m --- https://civitai.com/models/144327?modelVersionId=195614
LarryAIDraw/SimplePositiveXLv2	LarryAIDraw	2024-05-26T03:03:45Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-26T02:57:05Z	--- license: creativeml-openrail-m --- https://civitai.com/models/118758/simplepositivexl?modelVersionId=182974
LarryAIDraw/unaestheticXL_bp5	LarryAIDraw	2024-05-26T03:03:23Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2024-05-26T02:55:23Z	--- license: creativeml-openrail-m --- https://civitai.com/models/119032?modelVersionId=480651
leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF	leungchunghong	2024-05-26T03:02:10Z	2	0	null	[ "gguf", "nlp", "code", "llama-cpp", "gguf-my-repo", "text-generation", "en", "license:mit", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2024-05-26T03:02:03Z	--- language: - en license: mit tags: - nlp - code - llama-cpp - gguf-my-repo license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE pipeline_tag: text-generation inference: parameters: temperature: 0.0 widget: - messages: - role: user content: Can you provide ways to eat combinations of bananas and dragonfruits? --- # leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --model phi-3-mini-4k-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo leungchunghong/Phi-3-mini-4k-instruct-Q4_K_M-GGUF --model phi-3-mini-4k-instruct-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m phi-3-mini-4k-instruct-q4_k_m.gguf -n 128 ```
Raneechu/textbookbig10_ft5	Raneechu	2024-05-26T03:02:06Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-26T03:02:03Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig10_ft5 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. --> # textbookbig10_ft5 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
nepBros/nepali_news_classifier	nepBros	2024-05-26T03:01:33Z	114	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "ne", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-25T15:44:33Z	--- license: mit language: - ne metrics: - accuracy widget: - text: >- काठमाडौं शिक्षा विज्ञान प्रविधि मन्त्रालय तयार पार संघीय शिक्षा ऐन मस् शिक्षक सरुवा व्यवस्थान प्रस्ताव गर यस्तै मस् विभिन्न अवस्था शिक्षक सरुवा नहु व्यवस्था गर मस् शिक्षक स्थायी सेवा अवधि वर्ष नपुग अनिवार्य अवकास वर्ष बाँ सरुवा भई कार्य विद्यालय कम्ती शैक्षिक वर्ष पूरा नगर शिक्षक सरुवा नहु प्रस्ताव गर विद्यमान ऐन विद्यालय वर्ष सेवा अवधि पुरा स्थायी शिक्षक जिल्ला शिक्षा अधिकारी जिल्ला क्षेत्रीय निर्देशक क्षेत्र शिक्षा विभाग देशैभरी सरुवा सक् व्यवस्था यस परिवर्तन विभिन्न अवस्था शिक्षक सरुवा नहु प्रस्ताव मस् समेट व्यवस्थापन समिति सहमती सम्बन्धित स्थानीय तह पालि भित्र विद्यालय कार्य शिक्षक सरुवा सक् मस् उल्लेख जिल्ला भित्र अन्तर स्थानीय तहबीच शिक्षक सरुवा व्यवस्थापन समिति स्थानीय तह सहमती जिल्ला तह शिक्षा सम्बन्धि मामिला हेर् कार्यालय प्रस्ताव सरुवा विवरण प्रदेश शिक्षा विभाग पठाउ model-index: - name: nepBros/nepali_news_classifier results: - task: type: text-classification # Required. Example: automatic-speech-recognition name: classify nepali news # Optional. Example: Speech Recognition dataset: type: text_data # Required. Example: common_voice. Use dataset id from https://hf.co/datasets name: nepali_news # Required. A pretty name for the dataset. Example: Common Voice (French) metrics: - type: accuracy # Required. Example: wer. Use metric id from https://hf.co/metrics value: 91.16213442791175 ---
Khieminem/ip102-yolov8-imgcls	Khieminem	2024-05-26T02:52:16Z	5	0	transformers	[ "transformers", "onnx", "yolos", "image-classification", "en", "dataset:nqait05/ip102", "license:apache-2.0", "endpoints_compatible", "region:us" ]	image-classification	2024-04-26T13:30:40Z	--- license: apache-2.0 datasets: - nqait05/ip102 language: - en pipeline_tag: image-classification --- Just a simple modal using Yolov8 for Image Classification task on the dataset IP102 with 20 classes extracted based on image amount.
Raneechu/textbookbig10_ft4	Raneechu	2024-05-26T02:47:07Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2024-05-26T02:47:03Z	--- license: llama2 library_name: peft tags: - trl - sft - generated_from_trainer base_model: meta-llama/Llama-2-7b-hf model-index: - name: textbookbig10_ft4 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. --> # textbookbig10_ft4 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) 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: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.14.5 - Tokenizers 0.19.1 ## Training procedure ### Framework versions - PEFT 0.6.2
FrankL/storytellerLM-v0.1	FrankL	2024-05-26T02:46:30Z	172	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T07:41:16Z	--- library_name: transformers tags: [] --- ## 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: FrankL - Language(s) (NLP): English ### Direct Use ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained('FrankL/storytellerLM-v0.1', trust_remote_code=True, torch_dtype=torch.float16) model = model.to(device='cuda') tokenizer = AutoTokenizer.from_pretrained('FrankL/storytellerLM-v0.1', trust_remote_code=True) def inference( model: AutoModelForCausalLM, tokenizer: AutoTokenizer, input_text: str = "Once upon a time, ", max_new_tokens: int = 16 ): inputs = tokenizer(input_text, return_tensors="pt").to(device) outputs = model.generate( **inputs, pad_token_id=tokenizer.eos_token_id, max_new_tokens=max_new_tokens, do_sample=True, top_k=40, top_p=0.95, temperature=0.8 ) generated_text = tokenizer.decode( outputs[0], skip_special_tokens=True ) # print(outputs) print(generated_text) inference(model, tokenizer) ```
Naveenkumar2002/Bart-QnA-Base	Naveenkumar2002	2024-05-26T02:39:02Z	178	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-26T02:37:58Z	--- 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. 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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G-R-A-V-I-T-Y/long-t5-local-base-ARv1	G-R-A-V-I-T-Y	2024-05-26T02:36:26Z	115	0	transformers	[ "transformers", "tensorboard", "safetensors", "longt5", "text2text-generation", "generated_from_trainer", "base_model:google/long-t5-local-base", "base_model:finetune:google/long-t5-local-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-25T23:45:23Z	--- license: apache-2.0 base_model: google/long-t5-local-base tags: - generated_from_trainer model-index: - name: long-t5-local-base-ARv1 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. --> # long-t5-local-base-ARv1 This model is a fine-tuned version of [google/long-t5-local-base](https://huggingface.co/google/long-t5-local-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9303 - Exact Match: 18.0 - Gen Len: 3.38 ## 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: 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: 60 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Exact Match \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-----------:\|:-------:\| \| No log \| 1.0 \| 7 \| 3.4004 \| 14.0 \| 3.86 \| \| 2.7206 \| 2.0 \| 14 \| 3.1925 \| 8.0 \| 3.66 \| \| 2.6501 \| 3.0 \| 21 \| 2.9867 \| 8.0 \| 3.7 \| \| 2.6501 \| 4.0 \| 28 \| 2.8576 \| 12.0 \| 4.58 \| \| 1.9849 \| 5.0 \| 35 \| 2.9078 \| 12.0 \| 4.52 \| \| 2.0193 \| 6.0 \| 42 \| 2.8173 \| 8.0 \| 3.84 \| \| 2.0193 \| 7.0 \| 49 \| 2.7735 \| 16.0 \| 3.42 \| \| 1.6108 \| 8.0 \| 56 \| 2.5993 \| 12.0 \| 3.82 \| \| 1.8323 \| 9.0 \| 63 \| 2.5879 \| 12.0 \| 3.92 \| \| 1.4861 \| 10.0 \| 70 \| 2.7203 \| 16.0 \| 3.4 \| \| 1.4861 \| 11.0 \| 77 \| 2.9902 \| 24.0 \| 3.1 \| \| 1.425 \| 12.0 \| 84 \| 2.7667 \| 14.0 \| 3.36 \| \| 1.0387 \| 13.0 \| 91 \| 2.6547 \| 18.0 \| 3.42 \| \| 1.0387 \| 14.0 \| 98 \| 2.7072 \| 18.0 \| 3.34 \| \| 1.0793 \| 15.0 \| 105 \| 2.8158 \| 12.0 \| 3.58 \| \| 1.1969 \| 16.0 \| 112 \| 2.9404 \| 14.0 \| 3.32 \| \| 1.1969 \| 17.0 \| 119 \| 2.8512 \| 14.0 \| 3.3 \| \| 1.15 \| 18.0 \| 126 \| 2.7513 \| 18.0 \| 3.68 \| \| 1.2024 \| 19.0 \| 133 \| 2.7124 \| 16.0 \| 3.48 \| \| 1.3331 \| 20.0 \| 140 \| 2.7484 \| 16.0 \| 3.4 \| \| 1.3331 \| 21.0 \| 147 \| 2.8289 \| 18.0 \| 3.44 \| \| 1.1469 \| 22.0 \| 154 \| 2.9873 \| 14.0 \| 3.36 \| \| 1.5639 \| 23.0 \| 161 \| 3.0321 \| 18.0 \| 3.4 \| \| 1.5639 \| 24.0 \| 168 \| 3.0117 \| 14.0 \| 3.3 \| \| 0.8542 \| 25.0 \| 175 \| 2.8331 \| 16.0 \| 3.34 \| \| 0.9789 \| 26.0 \| 182 \| 2.7876 \| 20.0 \| 3.36 \| \| 0.9789 \| 27.0 \| 189 \| 2.7820 \| 20.0 \| 3.36 \| \| 0.8853 \| 28.0 \| 196 \| 2.8082 \| 18.0 \| 3.38 \| \| 0.9126 \| 29.0 \| 203 \| 2.8316 \| 16.0 \| 3.36 \| \| 1.0543 \| 30.0 \| 210 \| 2.8449 \| 18.0 \| 3.64 \| \| 1.0543 \| 31.0 \| 217 \| 2.8034 \| 8.0 \| 3.62 \| \| 1.0683 \| 32.0 \| 224 \| 2.8115 \| 14.0 \| 3.46 \| \| 0.951 \| 33.0 \| 231 \| 2.9019 \| 18.0 \| 3.34 \| \| 0.951 \| 34.0 \| 238 \| 3.0115 \| 18.0 \| 3.24 \| \| 0.8315 \| 35.0 \| 245 \| 3.0392 \| 18.0 \| 3.24 \| \| 1.1548 \| 36.0 \| 252 \| 3.0643 \| 18.0 \| 3.36 \| \| 1.1548 \| 37.0 \| 259 \| 3.0031 \| 16.0 \| 3.42 \| \| 0.7813 \| 38.0 \| 266 \| 2.9801 \| 18.0 \| 3.48 \| \| 0.671 \| 39.0 \| 273 \| 2.9622 \| 18.0 \| 3.48 \| \| 1.1771 \| 40.0 \| 280 \| 2.9049 \| 18.0 \| 3.46 \| \| 1.1771 \| 41.0 \| 287 \| 2.9042 \| 20.0 \| 3.56 \| \| 0.5959 \| 42.0 \| 294 \| 2.9598 \| 18.0 \| 3.48 \| \| 1.1583 \| 43.0 \| 301 \| 2.9936 \| 18.0 \| 3.44 \| \| 1.1583 \| 44.0 \| 308 \| 3.0072 \| 18.0 \| 3.44 \| \| 0.5728 \| 45.0 \| 315 \| 3.0003 \| 18.0 \| 3.44 \| \| 0.7237 \| 46.0 \| 322 \| 3.0093 \| 16.0 \| 3.4 \| \| 0.7237 \| 47.0 \| 329 \| 2.9688 \| 18.0 \| 3.42 \| \| 0.7295 \| 48.0 \| 336 \| 2.9533 \| 18.0 \| 3.38 \| \| 0.5627 \| 49.0 \| 343 \| 2.9357 \| 18.0 \| 3.36 \| \| 0.6489 \| 50.0 \| 350 \| 2.9317 \| 18.0 \| 3.4 \| \| 0.6489 \| 51.0 \| 357 \| 2.9339 \| 18.0 \| 3.4 \| \| 1.0427 \| 52.0 \| 364 \| 2.9256 \| 18.0 \| 3.4 \| \| 0.9156 \| 53.0 \| 371 \| 2.9220 \| 18.0 \| 3.4 \| \| 0.9156 \| 54.0 \| 378 \| 2.9091 \| 18.0 \| 3.38 \| \| 0.4748 \| 55.0 \| 385 \| 2.9036 \| 18.0 \| 3.36 \| \| 0.5616 \| 56.0 \| 392 \| 2.8998 \| 18.0 \| 3.36 \| \| 0.5616 \| 57.0 \| 399 \| 2.9128 \| 18.0 \| 3.36 \| \| 0.4836 \| 58.0 \| 406 \| 2.9205 \| 18.0 \| 3.36 \| \| 0.6498 \| 59.0 \| 413 \| 2.9282 \| 18.0 \| 3.36 \| \| 0.615 \| 60.0 \| 420 \| 2.9303 \| 18.0 \| 3.38 \| ### Framework versions - Transformers 4.41.0 - Pytorch 2.2.1 - Datasets 2.19.1 - Tokenizers 0.19.1
kid1802/huggy_test	kid1802	2024-05-26T02:14:07Z	1	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy", "region:us" ]	reinforcement-learning	2024-05-26T02:14:02Z	--- library_name: ml-agents tags: - Huggy - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Huggy --- # ppo Agent playing Huggy This is a trained model of a ppo agent playing Huggy using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A short tutorial where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A longer tutorial to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: kid1802/huggy_test 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
backyardai/LemonadeRP-4.5.3-GGUF	backyardai	2024-05-26T02:13:26Z	575	1	null	[ "gguf", "base_model:KatyTheCutie/LemonadeRP-4.5.3", "base_model:quantized:KatyTheCutie/LemonadeRP-4.5.3", "endpoints_compatible", "region:us" ]	null	2024-03-10T03:01:40Z	--- base_model: KatyTheCutie/LemonadeRP-4.5.3 model_name: LemonadeRP-4.5.3-GGUF quantized_by: brooketh --- <img src="BackyardAI_Banner.png" alt="Backyard.ai" style="height: 90px; min-width: 32px; display: block; margin: auto;"> <p style="text-align: center;">The official library of GGUF format models for use in the local AI chat app, Backyard AI.</p> <p style="text-align: center;"><a href="https://backyard.ai/">Download Backyard AI here to get started.</a></p> <p style="text-align: center;"><a href="https://www.reddit.com/r/LLM_Quants/">Request Additional models at r/LLM_Quants.</a></p> * # LemonadeRP 4.5.3 - Creator: [KatyTheCutie](https://huggingface.co/KatyTheCutie/) - Original: [LemonadeRP 4.5.3](https://huggingface.co/KatyTheCutie/LemonadeRP-4.5.3) - Date Created: 2024-05-25 - Trained Context: 4096 tokens - Description: 7B roleplay focused model, creativity, and less cliché is the focus of this merge. * ## What is a GGUF? GGUF is a large language model (LLM) format that can be split between CPU and GPU. GGUFs are compatible with applications based on llama.cpp, such as Backyard AI. Where other model formats require higher end GPUs with ample VRAM, GGUFs can be efficiently run on a wider variety of hardware. GGUF models are quantized to reduce resource usage, with a tradeoff of reduced coherence at lower quantizations. Quantization reduces the precision of the model weights by changing the number of bits used for each weight. * <img src="BackyardAI_Logo.png" alt="Backyard.ai" style="height: 75px; min-width: 32px; display: block; horizontal align: left;"> ## Backyard AI - Free, local AI chat application. - One-click installation on Mac and PC. - Automatically use GPU for maximum speed. - Built-in model manager. - High-quality character hub. - Zero-config desktop-to-mobile tethering. Backyard AI makes it easy to start chatting with AI using your own characters or one of the many found in the built-in character hub. The model manager helps you find the latest and greatest models without worrying about whether it's the correct format. Backyard AI supports advanced features such as lorebooks, author's note, text formatting, custom context size, sampler settings, grammars, local TTS, cloud inference, and tethering, all implemented in a way that is straightforward and reliable. Join us on [Discord](https://discord.gg/SyNN2vC9tQ) *
Anish13/results_model8	Anish13	2024-05-26T02:08:55Z	37	0	transformers	[ "transformers", "safetensors", "transformer", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:14:45Z	--- tags: - generated_from_trainer model-index: - name: results_model8 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. --> # results_model8 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9686 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 30 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:-----:\|:---------------:\| \| 3.3262 \| 0.5570 \| 10000 \| 3.3012 \| \| 3.0829 \| 1.1141 \| 20000 \| 3.1175 \| \| 2.9737 \| 1.6711 \| 30000 \| 3.0091 \| \| 2.8584 \| 2.2282 \| 40000 \| 2.9686 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0 - Datasets 2.19.1 - Tokenizers 0.19.1
empathie/Qwen1.5-0.5B-Chat-experiment-2	empathie	2024-05-26T02:07:47Z	6	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T03:04:36Z	--- 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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atgarcia/wav2vec2part7	atgarcia	2024-05-26T02:04:31Z	108	0	transformers	[ "transformers", "safetensors", "wav2vec2", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-26T01:37:11Z	--- 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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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
mrovejaxd/ABL_trad_j	mrovejaxd	2024-05-26T02:03:25Z	31	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:dccuchile/bert-base-spanish-wwm-cased", "base_model:finetune:dccuchile/bert-base-spanish-wwm-cased", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-26T00:42:17Z	--- base_model: dccuchile/bert-base-spanish-wwm-cased tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: ABL_trad_j 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. --> # ABL_trad_j This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.6432 - Accuracy: 0.6883 - F1: 0.6865 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 32 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:------:\| \| 0.9532 \| 1.0 \| 1500 \| 0.9116 \| 0.5825 \| 0.5793 \| \| 0.8601 \| 2.0 \| 3000 \| 0.8433 \| 0.6033 \| 0.6016 \| \| 0.7962 \| 3.0 \| 4500 \| 0.8150 \| 0.6275 \| 0.6252 \| \| 0.7633 \| 4.0 \| 6000 \| 0.7969 \| 0.635 \| 0.6334 \| \| 0.7153 \| 5.0 \| 7500 \| 0.7825 \| 0.6492 \| 0.6483 \| \| 0.678 \| 6.0 \| 9000 \| 0.7910 \| 0.6408 \| 0.6392 \| \| 0.6336 \| 7.0 \| 10500 \| 0.7772 \| 0.6608 \| 0.6606 \| \| 0.5981 \| 8.0 \| 12000 \| 0.7863 \| 0.6617 \| 0.6605 \| \| 0.5455 \| 9.0 \| 13500 \| 0.7954 \| 0.6658 \| 0.6657 \| \| 0.4972 \| 10.0 \| 15000 \| 0.8206 \| 0.6633 \| 0.6623 \| \| 0.4823 \| 11.0 \| 16500 \| 0.8442 \| 0.6683 \| 0.6673 \| \| 0.4258 \| 12.0 \| 18000 \| 0.8966 \| 0.6742 \| 0.6734 \| \| 0.4182 \| 13.0 \| 19500 \| 0.9327 \| 0.6767 \| 0.6761 \| \| 0.3588 \| 14.0 \| 21000 \| 0.9780 \| 0.6717 \| 0.6689 \| \| 0.3576 \| 15.0 \| 22500 \| 1.0288 \| 0.6833 \| 0.6828 \| \| 0.3252 \| 16.0 \| 24000 \| 1.0873 \| 0.6842 \| 0.6836 \| \| 0.3104 \| 17.0 \| 25500 \| 1.1417 \| 0.685 \| 0.6847 \| \| 0.2691 \| 18.0 \| 27000 \| 1.2447 \| 0.6842 \| 0.6827 \| \| 0.2559 \| 19.0 \| 28500 \| 1.3480 \| 0.6825 \| 0.6816 \| \| 0.2522 \| 20.0 \| 30000 \| 1.4782 \| 0.6867 \| 0.6859 \| \| 0.2234 \| 21.0 \| 31500 \| 1.5748 \| 0.6833 \| 0.6815 \| \| 0.1954 \| 22.0 \| 33000 \| 1.7041 \| 0.69 \| 0.6897 \| \| 0.1979 \| 23.0 \| 34500 \| 1.8398 \| 0.6808 \| 0.6789 \| \| 0.176 \| 24.0 \| 36000 \| 1.9141 \| 0.6867 \| 0.6860 \| \| 0.1862 \| 25.0 \| 37500 \| 2.0105 \| 0.6883 \| 0.6881 \| \| 0.1409 \| 26.0 \| 39000 \| 2.1345 \| 0.685 \| 0.6840 \| \| 0.1527 \| 27.0 \| 40500 \| 2.2039 \| 0.6858 \| 0.6853 \| \| 0.1474 \| 28.0 \| 42000 \| 2.2990 \| 0.6933 \| 0.6920 \| \| 0.1428 \| 29.0 \| 43500 \| 2.3780 \| 0.6883 \| 0.6878 \| \| 0.1348 \| 30.0 \| 45000 \| 2.4859 \| 0.6858 \| 0.6839 \| \| 0.1046 \| 31.0 \| 46500 \| 2.5546 \| 0.6825 \| 0.6801 \| \| 0.1147 \| 32.0 \| 48000 \| 2.6432 \| 0.6883 \| 0.6865 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1
MVRL/satclip-loc-enc-vit16-l40	MVRL	2024-05-26T01:51:36Z	0	0	null	[ "safetensors", "pytorch_model_hub_mixin", "model_hub_mixin", "region:us" ]	null	2024-05-26T01:51:35Z	--- tags: - pytorch_model_hub_mixin - model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]
drgary/ft6_lawllm_llama3_athena2	drgary	2024-05-26T01:31:37Z	2	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T01:29:51Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: drgary - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
samwit/paligemma_vqav2	samwit	2024-05-26T01:30:25Z	4	0	peft	[ "peft", "tensorboard", "safetensors", "generated_from_trainer", "dataset:vq_av2", "base_model:google/paligemma-3b-pt-224", "base_model:adapter:google/paligemma-3b-pt-224", "license:gemma", "region:us" ]	null	2024-05-26T01:09:51Z	--- license: gemma library_name: peft tags: - generated_from_trainer base_model: google/paligemma-3b-pt-224 datasets: - vq_av2 model-index: - name: paligemma_vqav2 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. --> # paligemma_vqav2 This model is a fine-tuned version of [google/paligemma-3b-pt-224](https://huggingface.co/google/paligemma-3b-pt-224) on the vq_av2 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: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.42.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
han-chi/llama2_uuu_news_qlora	han-chi	2024-05-26T01:28:37Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "region:us" ]	null	2024-05-25T05:22:30Z	--- library_name: peft base_model: NousResearch/Llama-2-7b-chat-hf --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.11.1
tz579/example_asr_wav2vec2	tz579	2024-05-26T01:27:44Z	5	0	transformers	[ "transformers", "tensorboard", "wav2vec2", "automatic-speech-recognition", "edinburghcstr/ami", "generated_from_trainer", "dataset:ami", "base_model:facebook/wav2vec2-large-lv60", "base_model:finetune:facebook/wav2vec2-large-lv60", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-24T20:28:06Z	--- license: apache-2.0 base_model: facebook/wav2vec2-large-lv60 tags: - automatic-speech-recognition - edinburghcstr/ami - generated_from_trainer datasets: - ami metrics: - wer model-index: - name: facebook/wav2vec2-large-lv60 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: EDINBURGHCSTR/AMI - IHM type: ami config: ihm split: None args: 'Config: ihm, Training split: train, Eval split: validation' metrics: - name: Wer type: wer value: 0.9542044754234227 --- <!-- 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. --> # facebook/wav2vec2-large-lv60 This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the EDINBURGHCSTR/AMI - IHM dataset. It achieves the following results on the evaluation set: - Loss: 1.2723 - Wer: 0.9542 ## 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: 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_steps: 500 - num_epochs: 2.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 1.0919 \| 0.1565 \| 1000 \| 1.0169 \| 0.7064 \| \| 1.4768 \| 0.3131 \| 2000 \| 0.7156 \| 0.4356 \| \| 0.9728 \| 0.4696 \| 3000 \| 0.6462 \| 0.4030 \| \| 0.5418 \| 0.6262 \| 4000 \| 0.6171 \| 0.3707 \| \| 0.8492 \| 0.7827 \| 5000 \| 0.5758 \| 0.3695 \| \| 1.4826 \| 0.9393 \| 6000 \| 0.5801 \| 0.3545 \| \| 0.3274 \| 1.0958 \| 7000 \| 0.5244 \| 0.3375 \| \| 0.2089 \| 1.2523 \| 8000 \| 0.5047 \| 0.3239 \| \| 0.2916 \| 1.4089 \| 9000 \| 0.4901 \| 0.3171 \| \| 0.1617 \| 1.5654 \| 10000 \| 0.5070 \| 0.3151 \| \| 0.3815 \| 1.7220 \| 11000 \| 0.4948 \| 0.3180 \| \| 1.0171 \| 1.8785 \| 12000 \| 0.9465 \| 0.8379 \| ### Framework versions - Transformers 4.42.0.dev0 - Pytorch 2.3.0a0+gitcd033a1 - Datasets 2.19.1 - Tokenizers 0.19.1
antitheft159/Zovuyo	antitheft159	2024-05-26T01:24:17Z	0	0	null	[ "license:cc-by-nd-4.0", "region:us" ]	null	2024-05-26T01:24:00Z	--- license: cc-by-nd-4.0 ---
JianKim3293/llama3_lora_blossmodel	JianKim3293	2024-05-26T01:19:09Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B", "base_model:finetune:MLP-KTLim/llama-3-Korean-Bllossom-8B", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T01:18:39Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B --- # Uploaded model - Developed by: JianKim3293 - License: apache-2.0 - Finetuned from model : MLP-KTLim/llama-3-Korean-Bllossom-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
asussome/xwin-finetuned-alpaca-cleaned	asussome	2024-05-26T01:11:31Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-04-18T19:18:46Z	--- license: apache-2.0 library_name: peft tags: - trl - sft - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 model-index: - name: xwin-finetuned-alpaca-cleaned 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. --> # xwin-finetuned-alpaca-cleaned This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - training_steps: 20 ### Training results ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
Ichsan2895/Merak-7B-v4_4bit_q128_awq	Ichsan2895	2024-05-26T01:10:16Z	80	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "id", "en", "dataset:wikipedia", "dataset:Ichsan2895/OASST_Top1_Indonesian", "dataset:Ichsan2895/alpaca-gpt4-indonesian", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "awq", "region:us" ]	text-generation	2024-05-25T18:37:33Z	--- datasets: - wikipedia - Ichsan2895/OASST_Top1_Indonesian - Ichsan2895/alpaca-gpt4-indonesian language: - id - en pipeline_tag: text-generation license: cc-by-nc-sa-4.0 --- <div style="width: auto; margin-left: auto; margin-right: auto"> <img src="https://huggingface.co/Ichsan2895/Merak-7B-v4/resolve/main/FINAL_LOGO/6.png" alt="MERAK" style="width: 50%; min-width: 100px; display: block; margin: auto;"> </div> # HAPPY TO ANNOUNCE THE RELEASE OF MERAK-7B-V4_4bit_q128_awq! Merak-7B is the Large Language Model of Indonesian Language This model is based on [Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) and fine tuned by some of Indonesia Wikipedia articles that I cleaned before. Leveraging QLoRA (QLora: Efficient Finetuning of Quantized LLMs), Merak-7B is able to run with 16 GB VRAM Licensed under Creative Commons-By Attribution-Share Alike-Non Commercial (CC-BY-SA-NC 4.0) Merak-7B empowers AI enthusiasts, researchers alike. Big thanks to all my friends and communities that help to build our first model. Thanks for Axolotl for a great fine tuning tool which designed to streamline the fine-tuning of various AI models. Feel free, to ask me about the model and please share the news on your social media.
RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf	RichardErkhov	2024-05-26T01:06:35Z	6	0	null	[ "gguf", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-25T22:16:00Z	Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Hebrew-Gemma-11B-Instruct - GGUF - Model creator: https://huggingface.co/yam-peleg/ - Original model: https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-Instruct/ \| Name \| Quant method \| Size \| \| ---- \| ---- \| ---- \| \| [Hebrew-Gemma-11B-Instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q2_K.gguf) \| Q2_K \| 3.9GB \| \| [Hebrew-Gemma-11B-Instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ3_XS.gguf) \| IQ3_XS \| 4.27GB \| \| [Hebrew-Gemma-11B-Instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ3_S.gguf) \| IQ3_S \| 4.48GB \| \| [Hebrew-Gemma-11B-Instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K_S.gguf) \| Q3_K_S \| 4.48GB \| \| [Hebrew-Gemma-11B-Instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ3_M.gguf) \| IQ3_M \| 4.63GB \| \| [Hebrew-Gemma-11B-Instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K.gguf) \| Q3_K \| 4.94GB \| \| [Hebrew-Gemma-11B-Instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K_M.gguf) \| Q3_K_M \| 4.94GB \| \| [Hebrew-Gemma-11B-Instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q3_K_L.gguf) \| Q3_K_L \| 5.33GB \| \| [Hebrew-Gemma-11B-Instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ4_XS.gguf) \| IQ4_XS \| 5.44GB \| \| [Hebrew-Gemma-11B-Instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_0.gguf) \| Q4_0 \| 5.68GB \| \| [Hebrew-Gemma-11B-Instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.IQ4_NL.gguf) \| IQ4_NL \| 5.72GB \| \| [Hebrew-Gemma-11B-Instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_K_S.gguf) \| Q4_K_S \| 5.72GB \| \| [Hebrew-Gemma-11B-Instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_K.gguf) \| Q4_K \| 6.04GB \| \| [Hebrew-Gemma-11B-Instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_K_M.gguf) \| Q4_K_M \| 6.04GB \| \| [Hebrew-Gemma-11B-Instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q4_1.gguf) \| Q4_1 \| 6.25GB \| \| [Hebrew-Gemma-11B-Instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_0.gguf) \| Q5_0 \| 6.81GB \| \| [Hebrew-Gemma-11B-Instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_K_S.gguf) \| Q5_K_S \| 6.81GB \| \| [Hebrew-Gemma-11B-Instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_K.gguf) \| Q5_K \| 7.0GB \| \| [Hebrew-Gemma-11B-Instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_K_M.gguf) \| Q5_K_M \| 7.0GB \| \| [Hebrew-Gemma-11B-Instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q5_1.gguf) \| Q5_1 \| 7.37GB \| \| [Hebrew-Gemma-11B-Instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q6_K.gguf) \| Q6_K \| 8.01GB \| \| [Hebrew-Gemma-11B-Instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/yam-peleg_-_Hebrew-Gemma-11B-Instruct-gguf/blob/main/Hebrew-Gemma-11B-Instruct.Q8_0.gguf) \| Q8_0 \| 10.37GB \| Original model description: --- license: other license_name: gemma-terms-of-use license_link: https://ai.google.dev/gemma/terms language: - en - he library_name: transformers --- # Hebrew-Gemma-11B-Instruct ### Base Models: - 07.03.2024: [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B) - 16.03.2024: [Hebrew-Gemma-11B-V2](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-V2) ### Instruct Models: - 07.03.2024: [Hebrew-Gemma-11B-Instruct](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B-Instruct) The Hebrew-Gemma-11B-Instruct Large Language Model (LLM) is a instruct fine-tuned version of the [Hebrew-Gemma-11B](https://huggingface.co/yam-peleg/Hebrew-Gemma-11B) generative text model using a variety of conversation datasets. It is continued pretrain of gemma-7b, extended to a larger scale and trained on 3B additional tokens of both English and Hebrew text data. # Instruction format This format must be strictly respected, otherwise the model will generate sub-optimal outputs. ``` <bos><start_of_turn>user Write a hello world program<end_of_turn> <start_of_turn>model Here is a simple hellow world program<end_of_turn><eos> ``` - The conversation starts with `<bos>`. - Each turn is preceded by a `<start_of_turn>` delimiter and then the role of the entity (`user` or `model`). - Turns finish with the `<end_of_turn>` token. - Conversation finish with the `<eos>` token. You can follow this format to build the prompt manually, if you need to do it without the tokenizer's chat template. A simple example using the tokenizer's chat template: ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "Hebrew-Gemma-11B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda") chat = [ { "role": "user", "content": "כתוב קוד פשוט בפייתון שמדפיס למסך את התאריך של היום" }, ] prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) ``` ### Terms of Use As an extention of Gemma-7B, this model is subject to the original license and terms of use by Google. ### Benchmark Results - Coming Soon! ### Notice Hebrew-Gemma-11B is a pretrained base model and therefore does not have any moderation mechanisms. ### Authors - Trained by Yam Peleg. - In collaboration with Jonathan Rouach and Arjeo, inc.
antitheft159/eblis.195	antitheft159	2024-05-26T01:00:19Z	0	0	null	[ "license:cc-by-nd-4.0", "region:us" ]	null	2024-05-26T00:59:29Z	--- license: cc-by-nd-4.0 ---
gaalcoro/Logomarca	gaalcoro	2024-05-26T00:57:47Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-26T00:57:47Z	--- license: apache-2.0 ---
Sorour/phi3_cls_fomc	Sorour	2024-05-26T00:53:51Z	154	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-19T05:15:45Z	--- 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. (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]
shadowdefense/ShadowWatch001	shadowdefense	2024-05-26T00:53:07Z	0	0	null	[ "license:other", "region:us" ]	null	2024-05-26T00:53:07Z	--- license: other license_name: terms license_link: https://beta.openai.com/terms/ ---
NikolayKozloff/WizardLM-2-7B-abliterated-Q5_0-GGUF	NikolayKozloff	2024-05-26T00:45:03Z	5	2	null	[ "gguf", "llama-cpp", "gguf-my-repo", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-26T00:44:50Z	--- license: apache-2.0 tags: - llama-cpp - gguf-my-repo --- # NikolayKozloff/WizardLM-2-7B-abliterated-Q5_0-GGUF This model was converted to GGUF format from [`fearlessdots/WizardLM-2-7B-abliterated`](https://huggingface.co/fearlessdots/WizardLM-2-7B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/fearlessdots/WizardLM-2-7B-abliterated) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo NikolayKozloff/WizardLM-2-7B-abliterated-Q5_0-GGUF --model wizardlm-2-7b-abliterated-q5_0.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo NikolayKozloff/WizardLM-2-7B-abliterated-Q5_0-GGUF --model wizardlm-2-7b-abliterated-q5_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && \ cd llama.cpp && \ make && \ ./main -m wizardlm-2-7b-abliterated-q5_0.gguf -n 128 ```
JianKim3293/llama3_lora_lawmodel	JianKim3293	2024-05-26T00:24:10Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:08:43Z	--- 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. (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]
minhz2003/test	minhz2003	2024-05-26T00:21:29Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-05-26T00:20:20Z	--- license: apache-2.0 ---
takassh/gemma-2b-it-lora-model	takassh	2024-05-26T00:19:42Z	8	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-26T00:16:30Z	--- 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. (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]
legraphista/aya-23-8B-IMat-GGUF	legraphista	2024-05-26T00:17:38Z	165	0	gguf	[ "gguf", "quantized", "GGUF", "imatrix", "quantization", "imat", "static", "text-generation", "en", "fr", "de", "es", "it", "pt", "ja", "ko", "zh", "ar", "el", "fa", "pl", "id", "cs", "he", "hi", "nl", "ro", "ru", "tr", "uk", "vi", "base_model:CohereForAI/aya-23-8B", "base_model:quantized:CohereForAI/aya-23-8B", "license:cc-by-nc-4.0", "region:us", "conversational" ]	text-generation	2024-05-25T20:21:19Z	--- base_model: CohereForAI/aya-23-8B inference: false language: - en - fr - de - es - it - pt - ja - ko - zh - ar - el - fa - pl - id - cs - he - hi - nl - ro - ru - tr - uk - vi library_name: gguf license: cc-by-nc-4.0 pipeline_tag: text-generation quantized_by: legraphista tags: - quantized - GGUF - imatrix - quantization - imat - static --- # aya-23-8B-IMat-GGUF _Llama.cpp imatrix quantization of CohereForAI/aya-23-8B_ Original Model: [CohereForAI/aya-23-8B](https://huggingface.co/CohereForAI/aya-23-8B) Original dtype: `FP16` (`float16`) Quantized by: llama.cpp [b2998](https://github.com/ggerganov/llama.cpp/releases/tag/b2998) IMatrix dataset: [here](https://gist.githubusercontent.com/legraphista/d6d93f1a254bcfc58e0af3777eaec41e/raw/d380e7002cea4a51c33fffd47db851942754e7cc/imatrix.calibration.medium.raw) ## Files ### IMatrix Status: ✅ Available Link: [here](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/imatrix.dat) ### Common Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [aya-23-8B.Q8_0.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q8_0.gguf) \| Q8_0 \| 8.54GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q6_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q6_K.gguf) \| Q6_K \| 6.60GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q4_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q4_K.gguf) \| Q4_K \| 5.06GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K.gguf) \| Q3_K \| 4.22GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q2_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q2_K.gguf) \| Q2_K \| 3.44GB \| ✅ Available \| 🟢 Yes \| 📦 No ### All Quants \| Filename \| Quant type \| File Size \| Status \| Uses IMatrix \| Is Split \| \| -------- \| ---------- \| --------- \| ------ \| ------------ \| -------- \| \| [aya-23-8B.FP16.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.FP16.gguf) \| F16 \| 16.07GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q5_K.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q5_K.gguf) \| Q5_K \| 5.80GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q5_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q5_K_S.gguf) \| Q5_K_S \| 5.67GB \| ✅ Available \| ⚪ No \| 📦 No \| [aya-23-8B.Q4_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q4_K_S.gguf) \| Q4_K_S \| 4.83GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K_L.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K_L.gguf) \| Q3_K_L \| 4.53GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q3_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q3_K_S.gguf) \| Q3_K_S \| 3.87GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.Q2_K_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.Q2_K_S.gguf) \| Q2_K_S \| 3.25GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ4_NL.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ4_NL.gguf) \| IQ4_NL \| 4.81GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ4_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ4_XS.gguf) \| IQ4_XS \| 4.60GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_M.gguf) \| IQ3_M \| 3.99GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_S.gguf) \| IQ3_S \| 3.89GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_XS.gguf) \| IQ3_XS \| 3.72GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ3_XXS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ3_XXS.gguf) \| IQ3_XXS \| 3.41GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_M.gguf) \| IQ2_M \| 3.08GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_S.gguf) \| IQ2_S \| 2.90GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_XS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_XS.gguf) \| IQ2_XS \| 2.80GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ2_XXS.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ2_XXS.gguf) \| IQ2_XXS \| 2.59GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ1_M.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ1_M.gguf) \| IQ1_M \| 2.35GB \| ✅ Available \| 🟢 Yes \| 📦 No \| [aya-23-8B.IQ1_S.gguf](https://huggingface.co/legraphista/aya-23-8B-IMat-GGUF/blob/main/aya-23-8B.IQ1_S.gguf) \| IQ1_S \| 2.21GB \| ✅ Available \| 🟢 Yes \| 📦 No ## Downloading using huggingface-cli First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download legraphista/aya-23-8B-IMat-GGUF --include "aya-23-8B.Q8_0.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download legraphista/aya-23-8B-IMat-GGUF --include "aya-23-8B.Q8_0/*" --local-dir aya-23-8B.Q8_0 # see FAQ for merging GGUF's ``` ## FAQ ### Why is the IMatrix not applied everywhere? According to [this investigation](https://www.reddit.com/r/LocalLLaMA/comments/1993iro/ggufs_quants_can_punch_above_their_weights_now/), it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results). ### How do I merge a split GGUF? 1. Make sure you have `gguf-split` available - To get hold of `gguf-split`, navigate to https://github.com/ggerganov/llama.cpp/releases - Download the appropriate zip for your system from the latest release - Unzip the archive and you should be able to find `gguf-split` 2. Locate your GGUF chunks folder (ex: `aya-23-8B.Q8_0`) 3. Run `gguf-split --merge aya-23-8B.Q8_0/aya-23-8B.Q8_0-00001-of-XXXXX.gguf aya-23-8B.Q8_0.gguf` - Make sure to point `gguf-split` to the first chunk of the split. --- Got a suggestion? Ping me [@legraphista](https://x.com/legraphista)!
takassh/gemma-2b-it-lora	takassh	2024-05-26T00:16:29Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-26T00:16:28Z	--- 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. (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]
Enpas/whisper-base-co	Enpas	2024-05-26T00:12:44Z	79	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-23T21:48:26Z	``` import torch from transformers import pipeline device = "cuda:0" if torch.cuda.is_available() else "cpu" transcribe = pipeline(task="automatic-speech-recognition", model="Enpas/whisper-small-co", chunk_length_s=30, device=device) transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="am", task="transcribe") audio = "/content/tr_10000_tr097082.wav" result = transcribe(audio) print('Transcription: ', result["text"]) ```
GTsuya/cute_sexy_robutts_pony	GTsuya	2024-05-26T00:10:08Z	4	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:GraydientPlatformAPI/autism-pony", "base_model:adapter:GraydientPlatformAPI/autism-pony", "license:mit", "region:us" ]	text-to-image	2024-05-26T00:08:50Z	--- tags: - text-to-image - stable-diffusion - lora - diffusers - template:sd-lora widget: - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, dirndl, atmospheric perspective, portrait, church, rating_questionable, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00024-1661246894.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, bikini, sideways, cropped legs, tunnel, rating_explicit, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00077-2017120761.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Gloves, dutch angle, cropped legs, pool, rating_questionable, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00088-1815590393.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, armor, from above, wide shot, refinery, rating_explicit, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00171-1644120815.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Gloves, from above, close-up, flower shop, rating_safe, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00217-4158734917.png - text: >- cartoon, score_9, score_8_up, score_7_up, mature_female, Latex, atmospheric perspective, lower body, cooling tower, rating_safe, <lora:cute_sexy_robutts_pony:1> parameters: negative_prompt: >- score_6, score_5, score_4, ugly face, ugly eyes, realistic, monochrome, white and black output: url: images/00220-397098714.png base_model: GraydientPlatformAPI/autism-pony instance_prompt: null license: mit --- # cute_sexy_robutts_pony <Gallery /> ## Model description This LoRA model has been trained with Kohya SS using Cute Sexy Robutts's artworks on Autism Mix SDXL checkpoint. Obtained graphics are close to the original art style. This LoRA model could be use for cartoon/drawing representation of sexy women. ## Download model Weights for this model are available in Safetensors format. [Download](/GTsuya/cute_sexy_robutts_pony/tree/main) them in the Files & versions tab.
raulgdp/roberta-multiclase-ag_news	raulgdp	2024-05-26T00:08:49Z	108	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:FacebookAI/roberta-base", "base_model:finetune:FacebookAI/roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-25T21:35:34Z	--- license: mit base_model: roberta-base tags: - generated_from_trainer model-index: - name: roberta-multiclase-ag_news 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. --> # roberta-multiclase-ag_news This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.2671 - Rmse: 1.1967 ## 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: 8 - 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 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rmse \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.3199 \| 1.0 \| 15000 \| 1.2671 \| 1.1967 \| \| 1.3837 \| 2.0 \| 30000 \| 1.3864 \| 1.2230 \| \| 1.3879 \| 3.0 \| 45000 \| 1.3865 \| 1.8686 \| \| 1.385 \| 4.0 \| 60000 \| 1.3864 \| 1.2247 \| \| 1.3885 \| 5.0 \| 75000 \| 1.3863 \| 1.8720 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.0.1+cu117 - Datasets 2.19.1 - Tokenizers 0.19.1
umair894/llama3_1e	umair894	2024-05-25T23:58:39Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:58:25Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: umair894 - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
fearlessdots/Llama-3-Alpha-Centauri-v0.1	fearlessdots	2024-05-25T23:47:33Z	115	9	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "dataset:NobodyExistsOnTheInternet/ToxicQAFinal", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T18:00:36Z	--- license: llama3 datasets: - NobodyExistsOnTheInternet/ToxicQAFinal --- # Llama-3-Alpha-Centauri-v0.1 <img src="alpha_centauri_banner.png" alt="" style="width:500px;height:400px;"/> Image generated with [https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS](https://huggingface.co/PixArt-alpha/PixArt-Sigma-XL-2-1024-MS). --- ## Disclaimer Note: All models and LoRAs from the Centaurus series were created with the sole purpose of research. The usage of this model and/or its related LoRA implies agreement with the following terms: - The user is responsible for what they might do with it, including how the output of the model is interpreted and used; - The user should not use the model and its outputs for any illegal purposes; - The user is the only one resposible for any misuse or negative consequences from using this model and/or its related LoRA. I do not endorse any particular perspectives presented in the training data. --- ## Centaurus Series This series aims to develop highly uncensored Large Language Models (LLMs) with the following focuses: - Science, Technology, Engineering, and Mathematics (STEM) - Computer Science (including programming) - Social Sciences And several key cognitive skills, including but not limited to: - Reasoning and logical deduction - Critical thinking - Analysis While maintaining strong overall knowledge and expertise, the models will undergo refinement through: - Fine-tuning processes - Model merging techniques including Mixture of Experts (MoE) Please note that these models are experimental and may demonstrate varied levels of effectiveness. Your feedback, critique, or queries are most welcome for improvement purposes. ## Base This model and its related LoRA was fine-tuned on [https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3). ## LoRA The LoRA merged with the base model is available at [https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-LoRA](https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-LoRA). ## GGUF I provide some GGUF files here: [https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-GGUF](https://huggingface.co/fearlessdots/Llama-3-Alpha-Centauri-v0.1-GGUF). ## Datasets - [https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) ## Fine Tuning ### - Quantization Configuration - load_in_4bit=True - bnb_4bit_quant_type="fp4" - bnb_4bit_compute_dtype=compute_dtype - bnb_4bit_use_double_quant=False ### - PEFT Parameters - lora_alpha=64 - lora_dropout=0.05 - r=128 - bias="none" ### - Training Arguments - num_train_epochs=1 - per_device_train_batch_size=1 - gradient_accumulation_steps=4 - optim="adamw_bnb_8bit" - save_steps=25 - logging_steps=25 - learning_rate=2e-4 - weight_decay=0.001 - fp16=False - bf16=False - max_grad_norm=0.3 - max_steps=-1 - warmup_ratio=0.03 - group_by_length=True - lr_scheduler_type="constant" ## Credits - Meta ([https://huggingface.co/meta-llama](https://huggingface.co/meta-llama)): for the original Llama-3; - HuggingFace: for hosting this model and for creating the fine-tuning tools used; - failspy ([https://huggingface.co/failspy](https://huggingface.co/failspy)): for the base model and the orthogonalization implementation; - NobodyExistsOnTheInternet ([https://huggingface.co/NobodyExistsOnTheInternet](https://huggingface.co/NobodyExistsOnTheInternet)): for the incredible dataset; - Undi95 ([https://huggingface.co/Undi95](https://huggingface.co/Undi95)) and Sao10k ([https://huggingface.co/Sao10K](https://huggingface.co/Sao10K)): my main inspirations for doing these models =] A huge thank you to all of them ☺️ ## About Alpha Centauri Alpha Centauri is a triple star system located in the constellation of Centaurus. It includes three stars: Rigil Kentaurus (also known as α Centauri A), Toliman (or α Centauri B), and Proxima Centauri (α Centauri C). Proxima Centauri is the nearest star to the Sun, residing at approximately 4.25 light-years (1.3 parsecs) away. The primary pair, α Centauri A and B, are both similar to our Sun - α Centauri A being a class G star with 1.1 solar masses and 1.5 times the Sun's luminosity; α Centauri B having 0.9 solar masses and under half the luminosity of the Sun. They revolve around their shared center every 79 years following an elliptical path, ranging from 35.6 astronomical units apart (nearly Pluto's distance from the Sun) to 11.2 astronomical units apart (around Saturn's distance from the Sun.) Proxima Centauri, or α Centauri C, is a diminutive, dim red dwarf (a class M star) initially unseen to the naked eye. At roughly 4.24 light-years (1.3 parsecs) from us, it lies nearer than α Centauri AB, the binary system. Presently, the gap between Proxima Centauri and α Centauri AB amounts to around 13,000 Astronomical Units (0.21 light-years)—comparable to over 430 times Neptune's orbital radius. Two confirmed exoplanets accompany Proxima Centauri: Proxima b, discovered in 2016, is Earth-sized within the habitable zone; Proxima d, revealed in 2022, is a potential sub-Earth close to its host star. Meanwhile, disputes surround Proxima c, a mini-Neptune detected in 2019. Intriguingly, hints suggest that α Centauri A might possess a Neptune-sized object in its habitable region, but further investigation is required before confirming whether it truly exists and qualifies as a planet. Regarding α Centauri B, although once thought to harbor a planet (named α Cen Bb), subsequent research invalidated this claim, leaving it currently devoid of identified planets. Source: retrived from [https://en.wikipedia.org/wiki/Alpha_Centauri](https://en.wikipedia.org/wiki/Alpha_Centauri) and processed with [https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
Sorour/phi3-ft-fomc-v2	Sorour	2024-05-25T23:45:29Z	155	0	transformers	[ "transformers", "safetensors", "phi3", "text-generation", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-25T23:33:06Z	--- 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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QuangDuy/whisper-large-v3-vivos	QuangDuy	2024-05-25T23:40:18Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-25T23:40:17Z	--- 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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thdangtr/blip_recipe1m_title_v6	thdangtr	2024-05-25T23:35:49Z	67	0	transformers	[ "transformers", "safetensors", "blip", "image-text-to-text", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-25T23:34:17Z	--- 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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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ethan-ng/content-moderation-model	ethan-ng	2024-05-25T23:33:32Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-25T23:33:32Z	--- license: apache-2.0 ---

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