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ftopal
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huggingface-models-embeddings

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train · 241k rows

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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model 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]	{"language": ["en"], "license": "apache-2.0", "tags": ["spam", "ham", "text-classification", "spam-detection", "transformer", "BERT"], "metrics": ["accuracy", "precision", "recall", "f1"]}	text-classification	surajkarki/bert_spam_detection	[ "transformers", "safetensors", "bert", "text-classification", "spam", "ham", "spam-detection", "transformer", "BERT", "en", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T11:41:04+00:00	[ "1910.09700" ]	[ "en" ]	TAGS #transformers #safetensors #bert #text-classification #spam #ham #spam-detection #transformer #BERT #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID This modelcard aims to be a base template for new models. It has been generated using this raw template. ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #spam #ham #spam-detection #transformer #BERT #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 73, 29, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #spam #ham #spam-detection #transformer #BERT #en #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID\n\n\n\nThis modelcard aims to be a base template for new models. It has been generated using this raw template.## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.060846053063869476, 0.23102636635303497, -0.005311095155775547, 0.018827879801392555, 0.09810111671686172, 0.006204136647284031, 0.06435392796993256, 0.11026522517204285, -0.044758230447769165, 0.11905689537525177, 0.04496736079454422, 0.10052280873060226, 0.11213523894548416, 0.14894649386405945, -0.011842279694974422, -0.2270294427871704, 0.042261771857738495, -0.09480778872966766, -0.01131212618201971, 0.12356702983379364, 0.15160435438156128, -0.10081950575113297, 0.07366161793470383, -0.021857889369130135, -0.02774098329246044, -0.02955561876296997, -0.05628713220357895, -0.046249162405729294, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-classification	sekhharr/hackathon_v9	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T11:43:06+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 46, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06817419826984406, 0.1699906885623932, -0.003845146857202053, 0.018365124240517616, 0.11478200554847717, 0.00763329304754734, 0.07986336201429367, 0.10738246887922287, -0.0269484706223011, 0.1267213374376297, 0.03862300142645836, 0.1017010435461998, 0.11044707149267197, 0.18616852164268494, 0.002953584771603346, -0.2117370218038559, 0.062315817922353745, -0.11355884373188019, 0.01421935111284256, 0.12174045294523239, 0.14285145699977875, -0.10472407191991806, 0.07340893894433975, -0.03533155843615532, -0.019184017553925514, -0.029508300125598907, -0.06138347089290619, -0.062117863446474075, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # audio_classification This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the minds14 dataset. It achieves the following results on the evaluation set: - Loss: 2.6430 - Accuracy: 0.0708 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| No log \| 1.0 \| 15 \| 2.6430 \| 0.0708 \| \| No log \| 2.0 \| 30 \| 2.6430 \| 0.0708 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["minds14"], "metrics": ["accuracy"], "base_model": "facebook/wav2vec2-base", "model-index": [{"name": "audio_classification", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "minds14", "type": "minds14", "config": "en-US", "split": "train", "args": "en-US"}, "metrics": [{"type": "accuracy", "value": 0.07079646017699115, "name": "Accuracy"}]}]}]}	audio-classification	andikamandalaa/audio_classification	[ "transformers", "safetensors", "wav2vec2", "audio-classification", "generated_from_trainer", "dataset:minds14", "base_model:facebook/wav2vec2-base", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-10T11:43:55+00:00	[]	[]	TAGS #transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us	audio\_classification ===================== This model is a fine-tuned version of facebook/wav2vec2-base on the minds14 dataset. It achieves the following results on the evaluation set: * Loss: 2.6430 * Accuracy: 0.0708 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 3e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.2+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 73, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #safetensors #wav2vec2 #audio-classification #generated_from_trainer #dataset-minds14 #base_model-facebook/wav2vec2-base #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.2+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ -0.11834293603897095, 0.11365389823913574, -0.0014671443495899439, 0.09533829241991043, 0.14046558737754822, 0.003528430825099349, 0.14492540061473846, 0.07881590723991394, -0.0836125984787941, 0.039837874472141266, 0.10059697926044464, 0.08848977833986282, 0.018113821744918823, 0.1472911536693573, -0.0771133303642273, -0.19080811738967896, 0.040149662643671036, 0.00825540255755186, -0.056458450853824615, 0.11892567574977875, 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null	null	transformers	this is [miqu-1-70b](https://huggingface.co/miqudev/miqu-1-70b), dequantised from q5 to f16 && transposed to pytorch. shapes have been rotated less wrongly than in [alpindale/miqu-1-70b-pytorch](https://huggingface.co/alpindale/miqu-1-70b-pytorch/tree/main) usage ```python from transformers import LlamaForCausalLM as LLM, LlamaTokenizer as LT lt = LT.from_pretrained("NousResearch/Llama-2-7b-hf") t = lt("[INST] eloquent high camp prose about a cute catgirl [/INST]", return_tensors='pt').input_ids.cuda() llm = LLM.from_pretrained("152334H/miqu-1-70b-sf", device_map='auto') # note: you may need many gpus for this out = llm.generate(t, use_cache=False, max_new_tokens=200) print(lt.decode(out[0])) ``` result: ``` <s> [INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature of such enchanting allure that she captivates the hearts and minds of all who behold her. This beguiling figure, a vision of feline grace and innocence, is none other than the inimitable catgirl. With her delicate features and winsome smile, she is the embodiment of a dream, a living testament to the power of imagination and the boundless possibilities of the human spirit. Her eyes, those twin orbs of sapphire fire, sparkle with a mischievous intelligence that belies her diminutive stature. They are windows into a soul that is at once ancient and eternally young, a soul that has traversed the vast expanse of time and space to find solace in the warm embrace of human companion ``` this roughly (but not entirely) matches the llama.cpp q5 result: ```bash $ ./main -ngl 99 -m ./miqu-q5 --color --temp 0.0 -n -1 -p '[INST] eloquent high camp prose about a cute catgirl [/INST]' ... [INST] eloquent high camp prose about a cute catgirl [/INST] In the resplendent realm of high camp, where irony and extravagance dance in a dazzling pas de deux, there exists a creature so enchantingly adorable that she captures the hearts of all who behold her. This is no ordinary feline, but rather a vision of elegance and whimsy combined: the cute catgirl. With her delicate features framed by an ethereal halo of pastel tresses, this darling diva prowls through life with the grace of a prima ballerina and the playfulness of a kitten. Her eyes, twin pools of sapphire or emerald, sparkle with mischief and intelligence as they survey their surroundings, ever alert for the next grand adventure or delightful prank. Her ensemble is a symphony of ruffles, bows, and lace, each detail painstakingly chosen to accentuate her lithe form and play up her feline charms. A frilly apron adorned with paw prints sways gently as she moves, while dainty ears perched atop her head twitch in response to every sound. Her gloved hands, so petite and perfect, seem made for holding teacups or sketching delicate portraits of her many admirers. But do not be fooled by her diminutive stature and sweet demeanor; beneath that fluffy exterior lies the heart of a lioness. Fiercely loyal and protective, she will stop at nothing to defend those she loves from harm. And when the situation calls for it, she can unleash a ferocious roar that belies her cute exterior. Indeed, the cute catgirl is a paradox wrapped in ruffles and ribbons, a living embodiment of the high camp aesthetic. She revels in the absurdity of her existence, finding joy in every outrageous situation and turning even the most mundane tasks into opportunities for sartorial expression. In her world, there is no such thing as too much glitter or too many bows; more is always more, and excess is a virtue to be celebrated. So let us raise our teacups in honor of this fabulous feline, this queen of camp who reminds us that life is too short for dull clothing and boring hairstyles. May we all strive to embody her spirit, embracing the absurdity of existence with open arms and a generous helping of glitter. Long live the cute catgirl! [end of text] ``` exl2 3.0bpw coming soon ![](https://thicc-af.mywaifulist.moe/waifus/miku-nakano-the-quintessential-quintuplets/phUEiEhPOL75GTDLncGy2dUbkDVMfYExZ2A1RBeQ.png?class=thumbnail)	{"license": "mit"}	text-generation	LoneStriker/miqu-1-70b-sf-GPTQ	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T11:51:10+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	this is miqu-1-70b, dequantised from q5 to f16 && transposed to pytorch. shapes have been rotated less wrongly than in alpindale/miqu-1-70b-pytorch usage result: this roughly (but not entirely) matches the URL q5 result: exl2 3.0bpw coming soon ![](URL	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 56 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.0005691677215509117, 0.03026840090751648, -0.005779425613582134, -0.007680800743401051, 0.12393421679735184, -0.016670791432261467, 0.20057982206344604, 0.10256171226501465, 0.0027368133887648582, -0.025139721110463142, 0.14202554523944855, 0.2090049833059311, -0.02653181366622448, 0.03626692295074463, -0.12612687051296234, -0.17803461849689484, 0.08635426312685013, -0.0035030082799494267, 0.04833058640360832, 0.08384886384010315, 0.10781697928905487, -0.06203937530517578, 0.08011354506015778, -0.04676711559295654, -0.0940304547548294, 0.03246979042887688, 0.06822408735752106, -0.13098374009132385, 0.12263950705528259, 0.06911366432905197, 0.10126137733459473, 0.060711462050676346, -0.04248986020684242, 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null	null	null	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="tahsinkheya/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	tahsinkheya/q-FrozenLake-v1-4x4-noSlippery	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T11:51:11+00:00	[]	[]	TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage	[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 40, 39 ]	[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 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null	null	peft	## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	{"library_name": "peft"}	null	saracandu/llama2-7b-harrypotter	[ "peft", "region:us" ]	2024-02-10T11:52:24+00:00	[]	[]	TAGS #peft #region-us	## Training procedure The following 'bitsandbytes' quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ "TAGS\n#peft #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ 9, 154, 11 ]	[ "passage: TAGS\n#peft #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n\n- PEFT 0.4.0" ]	[ -0.06979092955589294, 0.021877916529774666, -0.0024098597932606936, 0.1378258466720581, 0.10862318426370621, 0.07474296540021896, 0.10942144691944122, 0.12339992076158524, 0.0572756864130497, 0.09055759757757187, 0.09443594515323639, 0.05080708488821983, 0.07617583870887756, 0.15425844490528107, -0.02472618594765663, -0.030937515199184418, 0.05461447685956955, 0.0024960634764283895, 0.003683591727167368, 0.07837489992380142, 0.05202331766486168, -0.036243125796318054, 0.037176959216594696, -0.09029994904994965, -0.1683429628610611, -0.0007763148751109838, 0.010803398676216602, 0.03168312832713127, 0.04643981158733368, 0.03667012229561806, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	khanhnto/kyt-tietest-13b	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T11:53:26+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 56, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06061961501836777, 0.15481999516487122, -0.004844071343541145, 0.02074851468205452, 0.0983177199959755, 0.007407687604427338, 0.07119518518447876, 0.11185134947299957, -0.023851769044995308, 0.1167980208992958, 0.031993988901376724, 0.09781743586063385, 0.11217817664146423, 0.16186554729938507, 0.0015333457849919796, -0.22897611558437347, 0.049678247421979904, -0.125278040766716, -0.0294334813952446, 0.11977242678403854, 0.1422213912010193, -0.10954539477825165, 0.0752737894654274, -0.038042325526475906, -0.005828251596540213, -0.0323176346719265, -0.06205610930919647, -0.05266609415411949, 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null	null	transformers	[speakleash.org](https://speakleash.org) ## Prompt template: ChatML ``` <\|im_start\|>system {system_message}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ```	{"license": "apache-2.0"}	text-generation	Nondzu/openchat-3.5-0106-speakleash-007-pl-8192-32-16-0.01	[ "transformers", "pytorch", "safetensors", "mistral", "text-generation", "conversational", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T11:56:19+00:00	[]	[]	TAGS #transformers #pytorch #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	URL ## Prompt template: ChatML	[ "## Prompt template: ChatML" ]	[ "TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## Prompt template: ChatML" ]	[ 63, 8 ]	[ "passage: TAGS\n#transformers #pytorch #safetensors #mistral #text-generation #conversational #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## Prompt template: ChatML" ]	[ -0.03472244367003441, 0.023891953751444817, -0.006842698436230421, 0.01020998414605856, 0.11760085076093674, -0.04041735455393791, 0.12997494637966156, 0.11317834258079529, -0.021746229380369186, -0.051469963043928146, 0.10812696069478989, 0.20791468024253845, -0.016136495396494865, 0.08788542449474335, -0.07167194038629532, -0.20487642288208008, 0.10530126094818115, 0.0033248967956751585, 0.05757478252053261, 0.09610744565725327, 0.1241317018866539, -0.02645120397210121, 0.06300366669893265, 0.0045683374628424644, -0.04567689076066017, -0.002450791886076331, 0.020076945424079895, -0.09779462218284607, 0.06149734556674957, 0.037758782505989075, 0.03577270358800888, 0.0441470742225647, -0.0732150673866272, 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null	null	transformers	dict( name="igenius-llama-2b", # official hf_config=dict(org="Igenius", name="igenius-llama-2b"), block_size=2048, vocab_size=32000, padding_multiple=64, n_layer=22, n_head=64, n_embd=2560, rotary_percentage=1.0, parallel_residual=False, bias=False, _norm_class="RMSNorm", norm_eps=1e-5, _mlp_class="LLaMAMLP", intermediate_size=8960, n_query_groups=8, ), Trained on wikipedia and editorial corpus for about 2 epochs	{}	text-generation	iGenius-AI-Team/Italia-2B-ckpt-61B-wikiEditorial	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T11:57:16+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	dict( name="igenius-llama-2b", # official hf_config=dict(org="Igenius", name="igenius-llama-2b"), block_size=2048, vocab_size=32000, padding_multiple=64, n_layer=22, n_head=64, n_embd=2560, rotary_percentage=1.0, parallel_residual=False, bias=False, _norm_class="RMSNorm", norm_eps=1e-5, _mlp_class="LLaMAMLP", intermediate_size=8960, n_query_groups=8, ), Trained on wikipedia and editorial corpus for about 2 epochs	[ "# official\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-2b\"),\n block_size=2048,\n vocab_size=32000,\n padding_multiple=64,\n n_layer=22,\n n_head=64,\n n_embd=2560,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\",\n intermediate_size=8960,\n n_query_groups=8,\n ),\n\n Trained on wikipedia and editorial corpus for about 2 epochs" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# official\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-2b\"),\n block_size=2048,\n vocab_size=32000,\n padding_multiple=64,\n n_layer=22,\n n_head=64,\n n_embd=2560,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\",\n intermediate_size=8960,\n n_query_groups=8,\n ),\n\n Trained on wikipedia and editorial corpus for about 2 epochs" ]	[ 47, 154 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# official\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-2b\"),\n block_size=2048,\n vocab_size=32000,\n padding_multiple=64,\n n_layer=22,\n n_head=64,\n n_embd=2560,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\",\n intermediate_size=8960,\n n_query_groups=8,\n ),\n\n Trained on wikipedia and editorial corpus for about 2 epochs" ]	[ -0.07190508395433426, -0.01878994144499302, -0.004910103511065245, 0.05577511712908745, 0.054755788296461105, 0.040023669600486755, 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null	null	stable-baselines3	# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib Install the RL Zoo (with SB3 and SB3-Contrib): ```bash pip install rl_zoo3 ``` ``` # Download model and save it into the logs/ folder python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tatlook -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do: ``` python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga tatlook -f logs/ python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga tatlook ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 10000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ``` # Environment Arguments ```python {'render_mode': 'rgb_array'} ```	{"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "632.50 +/- 127.03", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	tatlook/dqn-SpaceInvadersNoFrameskip-v4	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-10T11:58:20+00:00	[]	[]	TAGS #stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: URL SB3: URL SB3 Contrib: URL Install the RL Zoo (with SB3 and SB3-Contrib): If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters # Environment Arguments	[ "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]	[ "TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.", "## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:", "## Training (with the RL Zoo)", "## Hyperparameters", "# Environment Arguments" ]	[ 43, 90, 73, 9, 5, 7 ]	[ "passage: TAGS\n#stable-baselines3 #SpaceInvadersNoFrameskip-v4 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# DQN Agent playing SpaceInvadersNoFrameskip-v4\nThis is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4\nusing the stable-baselines3 library\nand the RL Zoo.\n\nThe RL Zoo is a training framework for Stable Baselines3\nreinforcement learning agents,\nwith hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)\n\nRL Zoo: URL\nSB3: URL\nSB3 Contrib: URL\n\nInstall the RL Zoo (with SB3 and SB3-Contrib):\n\n\n\n\nIf you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:## Training (with the RL Zoo)## Hyperparameters# Environment Arguments" ]	[ 0.043572068214416504, 0.2414778620004654, -0.0026879787910729647, 0.012635791674256325, 0.05784223601222038, 0.0030472534708678722, 0.08585051447153091, 0.10650663822889328, 0.024212315678596497, -0.001382096204906702, 0.003954293206334114, 0.17533031105995178, 0.03632635250687599, 0.13125447928905487, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	aidonuts/marigold-005-ep3	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T11:58:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	null	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="tahsinkheya/autonomous-taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "autonomous-taxi", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/- 2.67", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	tahsinkheya/autonomous-taxi	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T12:00:11+00:00	[]	[]	TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage	[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 32, 33 ]	[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 0.048862796276807785, -0.16549694538116455, -0.005485367961227894, 0.02960980497300625, 0.1345081776380539, -0.01784728653728962, 0.11895976960659027, 0.07759871333837509, -0.07461097836494446, -0.055395450443029404, 0.1418241262435913, 0.09088201075792313, 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null	null	diffusers	# frnkzane <Gallery /> ## Trigger words You should use `frnkzane` to trigger the image generation. ## Download model [Download](/Pfs2021Funny/Frank-Zane/tree/main) them in the Files & versions tab.	{"tags": ["text-to-image", "stable-diffusion", "lora", "diffusers", "template:sd-lora"], "widget": [{"text": "-", "output": {"url": "images/82642704991.jpg"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "frnkzane"}	text-to-image	Pfs2021Funny/Frank-Zane	[ "diffusers", "text-to-image", "stable-diffusion", "lora", "template:sd-lora", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "region:us" ]	2024-02-10T12:00:41+00:00	[]	[]	TAGS #diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us	# frnkzane <Gallery /> ## Trigger words You should use 'frnkzane' to trigger the image generation. ## Download model Download them in the Files & versions tab.	[ "# frnkzane\n\n<Gallery />", "## Trigger words\n\nYou should use 'frnkzane' to trigger the image generation.", "## Download model\n\n\nDownload them in the Files & versions tab." ]	[ "TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n", "# frnkzane\n\n<Gallery />", "## Trigger words\n\nYou should use 'frnkzane' to trigger the image generation.", "## Download model\n\n\nDownload them in the Files & versions tab." ]	[ 56, 9, 18, 14 ]	[ "passage: TAGS\n#diffusers #text-to-image #stable-diffusion #lora #template-sd-lora #base_model-stabilityai/stable-diffusion-xl-base-1.0 #region-us \n# frnkzane\n\n<Gallery />## Trigger words\n\nYou should use 'frnkzane' to trigger the image generation.## Download model\n\n\nDownload them in the Files & versions tab." ]	[ -0.08398307114839554, -0.09166717529296875, -0.005762984976172447, 0.05153873935341835, 0.07648049294948578, 0.0388760007917881, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Tr Cv11 - tgrhn This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.4045 - Wer: 104.3596 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 64 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 0.0776 \| 2.04 \| 1000 \| 0.2936 \| 70.0976 \| \| 0.0114 \| 4.07 \| 2000 \| 0.3641 \| 83.6851 \| \| 0.0038 \| 7.03 \| 3000 \| 0.3743 \| 94.4617 \| \| 0.0013 \| 9.07 \| 4000 \| 0.4023 \| 114.4720 \| \| 0.0011 \| 12.02 \| 5000 \| 0.4045 \| 104.3596 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.1 - Datasets 2.16.1 - Tokenizers 0.15.1	{"language": ["tr"], "license": "apache-2.0", "tags": ["whisper-event", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Tr Cv11 - tgrhn", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "tr", "split": "test", "args": "tr"}, "metrics": [{"type": "wer", "value": 104.359626480413, "name": "Wer"}]}]}]}	automatic-speech-recognition	tgrhn/whisper-small-tr-cv11	[ "transformers", "tensorboard", "whisper", "automatic-speech-recognition", "whisper-event", "generated_from_trainer", "tr", "dataset:mozilla-foundation/common_voice_11_0", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	2024-02-10T12:06:03+00:00	[]	[ "tr" ]	TAGS #transformers #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us	Whisper Small Tr Cv11 - tgrhn ============================= This model is a fine-tuned version of openai/whisper-small on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: * Loss: 0.4045 * Wer: 104.3596 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 64 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 5000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.1 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 95, 130, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #whisper #automatic-speech-recognition #whisper-event #generated_from_trainer #tr #dataset-mozilla-foundation/common_voice_11_0 #base_model-openai/whisper-small #license-apache-2.0 #model-index #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 5000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.1\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 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null	null	transformers	# MoECPM Untrained 4x2b ## Model Details ### Model Description A MoE model out of 4 MiniCPM-2B-sft models. Intended to be trained. This version probably does not perform well (if it works at all, lol. I haven't tested it). ## Uses - Training ### 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.	{"language": ["en", "zh"], "license": "apache-2.0", "tags": ["Mixtral", "openbmb/MiniCPM-2B-sft-bf16-llama-format", "MoE", "merge", "mergekit", "moerge", "MiniCPM"], "base_model": ["openbmb/MiniCPM-2B-sft-bf16-llama-format"]}	text-generation	Inv/MoECPM-Untrained-4x2b	[ "transformers", "safetensors", "mixtral", "text-generation", "Mixtral", "openbmb/MiniCPM-2B-sft-bf16-llama-format", "MoE", "merge", "mergekit", "moerge", "MiniCPM", "conversational", "en", "zh", "base_model:openbmb/MiniCPM-2B-sft-bf16-llama-format", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T12:10:34+00:00	[]	[ "en", "zh" ]	TAGS #transformers #safetensors #mixtral #text-generation #Mixtral #openbmb/MiniCPM-2B-sft-bf16-llama-format #MoE #merge #mergekit #moerge #MiniCPM #conversational #en #zh #base_model-openbmb/MiniCPM-2B-sft-bf16-llama-format #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# MoECPM Untrained 4x2b ## Model Details ### Model Description A MoE model out of 4 MiniCPM-2B-sft models. Intended to be trained. This version probably does not perform well (if it works at all, lol. I haven't tested it). ## Uses - Training ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.	[ "# MoECPM Untrained 4x2b", "## Model Details", "### Model Description\n\nA MoE model out of 4 MiniCPM-2B-sft models. Intended to be trained. This version probably does not perform well (if it works at all, lol. I haven't tested it).", "## Uses\n\n - Training", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations." ]	[ "TAGS\n#transformers #safetensors #mixtral #text-generation #Mixtral #openbmb/MiniCPM-2B-sft-bf16-llama-format #MoE #merge #mergekit #moerge #MiniCPM #conversational #en #zh #base_model-openbmb/MiniCPM-2B-sft-bf16-llama-format #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# MoECPM Untrained 4x2b", "## Model Details", "### Model Description\n\nA MoE model out of 4 MiniCPM-2B-sft models. Intended to be trained. This version probably does not perform well (if it works at all, lol. I haven't tested it).", "## Uses\n\n - Training", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations." ]	[ 131, 11, 3, 53, 5, 42 ]	[ "passage: TAGS\n#transformers #safetensors #mixtral #text-generation #Mixtral #openbmb/MiniCPM-2B-sft-bf16-llama-format #MoE #merge #mergekit #moerge #MiniCPM #conversational #en #zh #base_model-openbmb/MiniCPM-2B-sft-bf16-llama-format #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# MoECPM Untrained 4x2b## Model Details### Model Description\n\nA MoE model out of 4 MiniCPM-2B-sft models. Intended to be trained. This version probably does not perform well (if it works at all, lol. I haven't tested it).## Uses\n\n - Training### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations." ]	[ -0.08854611963033676, -0.08571867644786835, -0.0034186476841568947, 0.009682427160441875, 0.0671890377998352, -0.013999701477587223, 0.13435028493404388, 0.028683967888355255, 0.0027855061925947666, 0.05159854143857956, 0.05300029739737511, -0.023063521832227707, 0.024408169090747833, 0.15066967904567719, -0.05860418453812599, -0.20362308621406555, 0.07452122867107391, -0.07516609877347946, -0.07396337389945984, 0.08604761213064194, 0.11376060545444489, -0.10049492120742798, 0.06252019852399826, 0.005201385356485844, -0.05981840938329697, 0.006935307290405035, 0.011597748845815659, 0.0081955436617136, 0.0490197129547596, 0.07889765501022339, 0.02950512059032917, 0.05802296847105026, 0.03598363697528839, -0.11483936011791229, 0.049382392317056656, 0.040964990854263306, 0.03176364302635193, 0.054092131555080414, -0.0052979253232479095, 0.004410963971167803, 0.11232511699199677, -0.07383008301258087, 0.05690436810255051, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	aidonuts/marigold-005-ep4	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T12:12:22+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	transformers	dict( name="igenius-llama-2b", # official hf_config=dict(org="Igenius", name="igenius-llama-2b"), block_size=2048, vocab_size=32000, padding_multiple=64, n_layer=22, n_head=64, n_embd=2560, rotary_percentage=1.0, parallel_residual=False, bias=False, _norm_class="RMSNorm", norm_eps=1e-5, _mlp_class="LLaMAMLP", intermediate_size=8960, n_query_groups=8, ), Trained on wikipedia and editorial corpus for about 1 epoch	{}	text-generation	iGenius-AI-Team/Italia-2B-ckpt-30B-wikiEditorial	[ "transformers", "safetensors", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T12:14:46+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	dict( name="igenius-llama-2b", # official hf_config=dict(org="Igenius", name="igenius-llama-2b"), block_size=2048, vocab_size=32000, padding_multiple=64, n_layer=22, n_head=64, n_embd=2560, rotary_percentage=1.0, parallel_residual=False, bias=False, _norm_class="RMSNorm", norm_eps=1e-5, _mlp_class="LLaMAMLP", intermediate_size=8960, n_query_groups=8, ), Trained on wikipedia and editorial corpus for about 1 epoch	[ "# official\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-2b\"),\n block_size=2048,\n vocab_size=32000,\n padding_multiple=64,\n n_layer=22,\n n_head=64,\n n_embd=2560,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\",\n intermediate_size=8960,\n n_query_groups=8,\n ),\n\n Trained on wikipedia and editorial corpus for about 1 epoch" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# official\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-2b\"),\n block_size=2048,\n vocab_size=32000,\n padding_multiple=64,\n n_layer=22,\n n_head=64,\n n_embd=2560,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\",\n intermediate_size=8960,\n n_query_groups=8,\n ),\n\n Trained on wikipedia and editorial corpus for about 1 epoch" ]	[ 47, 153 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# official\n hf_config=dict(org=\"Igenius\", name=\"igenius-llama-2b\"),\n block_size=2048,\n vocab_size=32000,\n padding_multiple=64,\n n_layer=22,\n n_head=64,\n n_embd=2560,\n rotary_percentage=1.0,\n parallel_residual=False,\n bias=False,\n _norm_class=\"RMSNorm\",\n norm_eps=1e-5,\n _mlp_class=\"LLaMAMLP\",\n intermediate_size=8960,\n n_query_groups=8,\n ),\n\n Trained on wikipedia and editorial corpus for about 1 epoch" ]	[ -0.07252874225378036, -0.02175043523311615, -0.004738959483802319, 0.05557278171181679, 0.05883242189884186, 0.0396529957652092, 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null	null	null	# 基于SDXL模型LoRA微调实现《少前2：追放》文生图 ![example](./assets/example.png) ## 一、Model Library 1. 微调数据集：[基于SDXL模型的《少女前线2：追放》LoRA微调数据集](https://www.kaggle.com/datasets/yukikonata/sdxl2lora) 2. 预训练模型：[stable_diffusion_xl](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl) 3. 底模：[animagine-xl-3.0](https://huggingface.co/cagliostrolab/animagine-xl-3.0) 4. SDXL LoRA微调训练器：[kohya_ss](https://github.com/bmaltais/kohya_ss) 5. 数据集画质增强：[waifu2x](https://github.com/nagadomi/waifu2x) ## 二、Prompt Dict 1. 少前2追放角色 * 佩里缇亚: PKPSP * 塞布丽娜: SPAS12 * 托洛洛: AKAlfa * 桑朵莱希: G36 * 琼玖: QBZ191 * 维普雷: Vepr12 * 莫辛纳甘: MosinNagant * 黛烟: QBZ95 * 克罗丽科: Kroliko * 夏克里: XCRL * 奇塔: MP7 * 寇尔芙: TaurusCurve * 科谢尼娅: APS * 纳甘: Nagant1895 * 纳美西丝: OM50 * 莉塔拉: GalilARM * 闪电: OTs14 2. Pixiv画师风格 * おにねこ（鬼猫）: Onineko26 * 麻生: AsouAsabu * mignon: Mignon * migolu: Migolu ## 三、使用方式 1. 安装部分环境（默认已安装pytorch等必要环境） ~~~sh pip install diffusers --upgrade pip install transformers accelerate safetensors ~~~ 2. 使用Hugging Face下载并使用底模（animagine-xl-3.0）和LoRA模型 ~~~python import torch import datetime from PIL import Image import matplotlib.pyplot as plt from diffusers import ( StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler, AutoencoderKL ) # LoRA Hugging Face ID lora_id = "TfiyuenLau/GirlsFrontline2_SDXL_LoRA" # Load VAE component vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) # Configure the pipeline pipe = StableDiffusionXLPipeline.from_pretrained( "cagliostrolab/animagine-xl-3.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, ) pipe.load_lora_weights(lora_id) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) pipe.to('cuda') ~~~ 3. 生成图像 ~~~python # Define Prompt output = "./output.png" prompt = "1girl, OTs14, gloves, looking at viewer, smile, food, holding, solo, closed mouth, sitting, yellow eyes, black gloves, masterpiece, best quality" negative_prompt = "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name" # Generate Image image = pipe( prompt, negative_prompt=negative_prompt, width=1024, height=1024, guidance_scale=7, num_inference_steps=28 ).images[0] # Save & Show image.save(output) image = Image.open(output) plt.axis('off') plt.imshow(image) image.close() ~~~	{"language": "en", "license": "apache-2.0", "tags": ["text-to-image", "diffusion models", "LoRA fine-tuning", "animagine-xl-3.0", "stable_diffusion_xl", "kohya_ss", "waifu2x"], "thumbnail": "./assets/example.png", "model": {"name": "GirlsFrontline2-SDXL-LoRA", "description": "A model based on SDXL with LoRA fine-tuning for \u300aGirl's Frontline 2\u300b Text-To-Image generation.", "pipeline_tag": "image-generation", "repo": "https://huggingface.co/TfiyuenLau/GirlsFrontline2_SDXL_LoRA", "library": "huggingface", "framework": "pytorch", "version": "1.0.0", "pretrained_model": "stable_diffusion_xl", "base_model": "animagine-xl-3.0", "fine_tuner": "kohya_ss", "data_augmentation": "waifu2x", "task": "text-to-image"}}	text-to-image	TfiyuenLau/GirlsFrontline2_SDXL_LoRA	[ "tensorboard", "text-to-image", "diffusion models", "LoRA fine-tuning", "animagine-xl-3.0", "stable_diffusion_xl", "kohya_ss", "waifu2x", "en", "license:apache-2.0", "region:us" ]	2024-02-10T12:30:23+00:00	[]	[ "en" ]	TAGS #tensorboard #text-to-image #diffusion models #LoRA fine-tuning #animagine-xl-3.0 #stable_diffusion_xl #kohya_ss #waifu2x #en #license-apache-2.0 #region-us	# 基于SDXL模型LoRA微调实现《少前2：追放》文生图 !example ## 一、Model Library 1. 微调数据集：基于SDXL模型的《少女前线2：追放》LoRA微调数据集 2. 预训练模型：stable_diffusion_xl 3. 底模：animagine-xl-3.0 4. SDXL LoRA微调训练器：kohya_ss 5. 数据集画质增强：waifu2x ## 二、Prompt Dict 1. 少前2追放角色 * 佩里缇亚: PKPSP * 塞布丽娜: SPAS12 * 托洛洛: AKAlfa * 桑朵莱希: G36 * 琼玖: QBZ191 * 维普雷: Vepr12 * 莫辛纳甘: MosinNagant * 黛烟: QBZ95 * 克罗丽科: Kroliko * 夏克里: XCRL * 奇塔: MP7 * 寇尔芙: TaurusCurve * 科谢尼娅: APS * 纳甘: Nagant1895 * 纳美西丝: OM50 * 莉塔拉: GalilARM * 闪电: OTs14 2. Pixiv画师风格 * おにねこ（鬼猫）: Onineko26 * 麻生: AsouAsabu * mignon: Mignon * migolu: Migolu ## 三、使用方式 1. 安装部分环境（默认已安装pytorch等必要环境） ~~~sh pip install diffusers --upgrade pip install transformers accelerate safetensors ~~~ 2. 使用Hugging Face下载并使用底模（animagine-xl-3.0）和LoRA模型 ~~~python import torch import datetime from PIL import Image import URL as plt from diffusers import ( StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler, AutoencoderKL ) # LoRA Hugging Face ID lora_id = "TfiyuenLau/GirlsFrontline2_SDXL_LoRA" # Load VAE component vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) # Configure the pipeline pipe = StableDiffusionXLPipeline.from_pretrained( "cagliostrolab/animagine-xl-3.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, ) pipe.load_lora_weights(lora_id) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(URL) URL('cuda') ~~~ 3. 生成图像 ~~~python # Define Prompt output = "./URL" prompt = "1girl, OTs14, gloves, looking at viewer, smile, food, holding, solo, closed mouth, sitting, yellow eyes, black gloves, masterpiece, best quality" negative_prompt = "nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name" # Generate Image image = pipe( prompt, negative_prompt=negative_prompt, width=1024, height=1024, guidance_scale=7, num_inference_steps=28 ).images[0] # Save & Show URL(output) image = URL(output) URL('off') URL(image) URL() ~~~	[ "# 基于SDXL模型LoRA微调实现《少前2：追放》文生图\n\n!example", "## 一、Model Library\n1. 微调数据集：基于SDXL模型的《少女前线2：追放》LoRA微调数据集\n2. 预训练模型：stable_diffusion_xl\n3. 底模：animagine-xl-3.0\n4. SDXL LoRA微调训练器：kohya_ss\n5. 数据集画质增强：waifu2x", "## 二、Prompt Dict\n1. 少前2追放角色\n* 佩里缇亚: PKPSP\n* 塞布丽娜: SPAS12\n* 托洛洛: AKAlfa\n* 桑朵莱希: G36\n* 琼玖: QBZ191\n* 维普雷: Vepr12\n* 莫辛纳甘: MosinNagant\n* 黛烟: QBZ95\n* 克罗丽科: Kroliko\n* 夏克里: XCRL\n* 奇塔: MP7\n* 寇尔芙: TaurusCurve\n* 科谢尼娅: APS\n* 纳甘: Nagant1895\n* 纳美西丝: OM50\n* 莉塔拉: GalilARM\n* 闪电: OTs14\n\n2. Pixiv画师风格\n* おにねこ（鬼猫）: Onineko26\n* 麻生: AsouAsabu\n* mignon: Mignon\n* migolu: Migolu", "## 三、使用方式\n1. 安装部分环境（默认已安装pytorch等必要环境）\n~~~sh\npip install diffusers --upgrade\npip install transformers accelerate safetensors\n~~~\n\n2. 使用Hugging Face下载并使用底模（animagine-xl-3.0）和LoRA模型\n~~~python\nimport torch\nimport datetime\nfrom PIL import Image\nimport URL as plt\nfrom diffusers import (\n StableDiffusionXLPipeline, \n EulerAncestralDiscreteScheduler,\n AutoencoderKL\n)", "# LoRA Hugging Face ID\nlora_id = \"TfiyuenLau/GirlsFrontline2_SDXL_LoRA\"", "# Load VAE component\nvae = AutoencoderKL.from_pretrained(\n \"madebyollin/sdxl-vae-fp16-fix\", \n torch_dtype=torch.float16\n)", "# Configure the pipeline\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"cagliostrolab/animagine-xl-3.0\", \n vae=vae,\n torch_dtype=torch.float16, \n use_safetensors=True, \n)\npipe.load_lora_weights(lora_id)\npipe.scheduler = EulerAncestralDiscreteScheduler.from_config(URL)\nURL('cuda')\n~~~\n\n3. 生成图像\n~~~python", "# Define Prompt\noutput = \"./URL\"\nprompt = \"1girl, OTs14, gloves, looking at viewer, smile, food, holding, solo, closed mouth, sitting, yellow eyes, black gloves, masterpiece, best quality\"\nnegative_prompt = \"nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name\"", "# Generate Image\nimage = pipe(\n prompt, \n negative_prompt=negative_prompt, \n width=1024,\n height=1024,\n guidance_scale=7,\n num_inference_steps=28\n).images[0]", "# Save & Show\nURL(output)\nimage = URL(output)\nURL('off')\nURL(image)\nURL()\n~~~" ]	[ "TAGS\n#tensorboard #text-to-image #diffusion models #LoRA fine-tuning #animagine-xl-3.0 #stable_diffusion_xl #kohya_ss #waifu2x #en #license-apache-2.0 #region-us \n", "# 基于SDXL模型LoRA微调实现《少前2：追放》文生图\n\n!example", "## 一、Model Library\n1. 微调数据集：基于SDXL模型的《少女前线2：追放》LoRA微调数据集\n2. 预训练模型：stable_diffusion_xl\n3. 底模：animagine-xl-3.0\n4. SDXL LoRA微调训练器：kohya_ss\n5. 数据集画质增强：waifu2x", "## 二、Prompt Dict\n1. 少前2追放角色\n* 佩里缇亚: PKPSP\n* 塞布丽娜: SPAS12\n* 托洛洛: AKAlfa\n* 桑朵莱希: G36\n* 琼玖: QBZ191\n* 维普雷: Vepr12\n* 莫辛纳甘: MosinNagant\n* 黛烟: QBZ95\n* 克罗丽科: Kroliko\n* 夏克里: XCRL\n* 奇塔: MP7\n* 寇尔芙: TaurusCurve\n* 科谢尼娅: APS\n* 纳甘: Nagant1895\n* 纳美西丝: OM50\n* 莉塔拉: GalilARM\n* 闪电: OTs14\n\n2. Pixiv画师风格\n* おにねこ（鬼猫）: Onineko26\n* 麻生: AsouAsabu\n* mignon: Mignon\n* migolu: Migolu", "## 三、使用方式\n1. 安装部分环境（默认已安装pytorch等必要环境）\n~~~sh\npip install diffusers --upgrade\npip install transformers accelerate safetensors\n~~~\n\n2. 使用Hugging Face下载并使用底模（animagine-xl-3.0）和LoRA模型\n~~~python\nimport torch\nimport datetime\nfrom PIL import Image\nimport URL as plt\nfrom diffusers import (\n StableDiffusionXLPipeline, \n EulerAncestralDiscreteScheduler,\n AutoencoderKL\n)", "# LoRA Hugging Face ID\nlora_id = \"TfiyuenLau/GirlsFrontline2_SDXL_LoRA\"", "# Load VAE component\nvae = AutoencoderKL.from_pretrained(\n \"madebyollin/sdxl-vae-fp16-fix\", \n torch_dtype=torch.float16\n)", "# Configure the pipeline\npipe = StableDiffusionXLPipeline.from_pretrained(\n \"cagliostrolab/animagine-xl-3.0\", \n vae=vae,\n torch_dtype=torch.float16, \n use_safetensors=True, \n)\npipe.load_lora_weights(lora_id)\npipe.scheduler = EulerAncestralDiscreteScheduler.from_config(URL)\nURL('cuda')\n~~~\n\n3. 生成图像\n~~~python", "# Define Prompt\noutput = \"./URL\"\nprompt = \"1girl, OTs14, gloves, looking at viewer, smile, food, holding, solo, closed mouth, sitting, yellow eyes, black gloves, masterpiece, best quality\"\nnegative_prompt = \"nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name\"", "# Generate Image\nimage = pipe(\n prompt, \n negative_prompt=negative_prompt, \n width=1024,\n height=1024,\n guidance_scale=7,\n num_inference_steps=28\n).images[0]", "# Save & Show\nURL(output)\nimage = URL(output)\nURL('off')\nURL(image)\nURL()\n~~~" ]	[ 65, 26, 84, 213, 121, 33, 52, 125, 127, 58, 31 ]	[ "passage: TAGS\n#tensorboard #text-to-image #diffusion models #LoRA fine-tuning #animagine-xl-3.0 #stable_diffusion_xl #kohya_ss #waifu2x #en #license-apache-2.0 #region-us \n# 基于SDXL模型LoRA微调实现《少前2：追放》文生图\n\n!example## 一、Model Library\n1. 微调数据集：基于SDXL模型的《少女前线2：追放》LoRA微调数据集\n2. 预训练模型：stable_diffusion_xl\n3. 底模：animagine-xl-3.0\n4. SDXL LoRA微调训练器：kohya_ss\n5. 数据集画质增强：waifu2x## 二、Prompt Dict\n1. 少前2追放角色\n* 佩里缇亚: PKPSP\n* 塞布丽娜: SPAS12\n* 托洛洛: AKAlfa\n* 桑朵莱希: G36\n* 琼玖: QBZ191\n* 维普雷: Vepr12\n* 莫辛纳甘: MosinNagant\n* 黛烟: QBZ95\n* 克罗丽科: Kroliko\n* 夏克里: XCRL\n* 奇塔: MP7\n* 寇尔芙: TaurusCurve\n* 科谢尼娅: APS\n* 纳甘: Nagant1895\n* 纳美西丝: OM50\n* 莉塔拉: GalilARM\n* 闪电: OTs14\n\n2. Pixiv画师风格\n* おにねこ（鬼猫）: Onineko26\n* 麻生: AsouAsabu\n* mignon: Mignon\n* migolu: Migolu" ]	[ -0.01026773452758789, 0.12035059928894043, -0.013864881359040737, 0.0660625770688057, 0.0897306427359581, 0.01106198225170374, 0.0713854655623436, 0.10473394393920898, 0.05220695585012436, 0.09146631509065628, 0.06294351071119308, 0.1170414388179779, 0.09398120641708374, 0.13552001118659973, -0.009199519641697407, -0.27518680691719055, 0.036263443529605865, -0.011130673810839653, -0.06117238849401474, 0.05533680319786072, 0.06250035762786865, -0.04758439213037491, 0.08277592808008194, -0.036427225917577744, -0.0014468879671767354, -0.053370799869298935, -0.03497495874762535, -0.05014447495341301, 0.030910922214388847, 0.07076995819807053, 0.01373730693012476, 0.04874415323138237, 0.012273464351892471, -0.15134471654891968, 0.0344514362514019, 0.0012418415863066912, -0.06513547897338867, 0.0024643559008836746, 0.06685794144868851, -0.03887559846043587, 0.18834511935710907, -0.08496198058128357, 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null	null	transformers	# ukr-t5-small A compact T5-small model fine-tuned for Ukrainian language tasks, with base English understanding. ## Model Description * Base Model: mT5-small * Fine-tuning Data: Leipzig Corpora Collection (English & Ukrainian news from 2023) * Tasks: * Text summarization (Ukrainian) * Text generation (Ukrainian) * Other Ukrainian-centric NLP tasks ## Technical Details * Model Size: 300 MB * Framework: Transformers (Hugging Face) ## Usage Installation ```bash pip install transformers ``` Loading the Model ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("path/to/ukr-t5-small") model = AutoModelForSeq2SeqLM.from_pretrained("path/to/ukr-t5-small") ``` Example: Machine Translation ```python text = "(Text in Ukrainian here)" # Tokenize and translate inputs = tokenizer("summarize: " + text, return_tensors="pt", max_length=512, truncation=True) summary_ids = model.generate(inputs["input_ids"], num_beams=4, max_length=128) # Decode output summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True) print(summary) ``` ## Limitations * The model's focus is on Ukrainian text processing, so performance on purely English tasks may be below that of general T5-small models. * Further fine-tuning may be required for optimal results on specific NLP tasks. ## Dataset Credits This model was fine-tuned on the Leipzig Corpora Collection (specify if there's a particular subset within the collection that you used). For full licensing and usage information of the original dataset, please refer to [Leipzig Corpora Collection website](https://wortschatz.uni-leipzig.de/en/download) ## Ethical Considerations * NLP models can reflect biases present in their training data. Be mindful of this when using this model for applications that have real-world impact. * It's important to test this model thoroughly across a variety of Ukrainian language samples to evaluate its reliability and fairness.	{"language": ["uk", "en"], "license": "apache-2.0"}	text2text-generation	d0p3/ukr-t5-small	[ "transformers", "safetensors", "t5", "text2text-generation", "uk", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T12:32:41+00:00	[]	[ "uk", "en" ]	TAGS #transformers #safetensors #t5 #text2text-generation #uk #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# ukr-t5-small A compact T5-small model fine-tuned for Ukrainian language tasks, with base English understanding. ## Model Description * Base Model: mT5-small * Fine-tuning Data: Leipzig Corpora Collection (English & Ukrainian news from 2023) * Tasks: * Text summarization (Ukrainian) * Text generation (Ukrainian) * Other Ukrainian-centric NLP tasks ## Technical Details * Model Size: 300 MB * Framework: Transformers (Hugging Face) ## Usage Installation Loading the Model Example: Machine Translation ## Limitations * The model's focus is on Ukrainian text processing, so performance on purely English tasks may be below that of general T5-small models. * Further fine-tuning may be required for optimal results on specific NLP tasks. ## Dataset Credits This model was fine-tuned on the Leipzig Corpora Collection (specify if there's a particular subset within the collection that you used). For full licensing and usage information of the original dataset, please refer to Leipzig Corpora Collection website ## Ethical Considerations * NLP models can reflect biases present in their training data. Be mindful of this when using this model for applications that have real-world impact. * It's important to test this model thoroughly across a variety of Ukrainian language samples to evaluate its reliability and fairness.	[ "# ukr-t5-small\n\nA compact T5-small model fine-tuned for Ukrainian language tasks, with base English understanding.", "## Model Description\n\n* Base Model: mT5-small \n* Fine-tuning Data: Leipzig Corpora Collection (English & Ukrainian news from 2023)\n* Tasks: \n * Text summarization (Ukrainian)\n * Text generation (Ukrainian)\n * Other Ukrainian-centric NLP tasks", "## Technical Details\n* Model Size: 300 MB \n* Framework: Transformers (Hugging Face)", "## Usage \n\nInstallation\n\n\n\nLoading the Model\n\n\n\nExample: Machine Translation", "## Limitations\n\n* The model's focus is on Ukrainian text processing, so performance on purely English tasks may be below that of general T5-small models.\n* Further fine-tuning may be required for optimal results on specific NLP tasks.", "## Dataset Credits\n\nThis model was fine-tuned on the Leipzig Corpora Collection (specify if there's a particular subset within the collection that you used). For full licensing and usage information of the original dataset, please refer to Leipzig Corpora Collection website", "## Ethical Considerations\n\n* NLP models can reflect biases present in their training data. Be mindful of this when using this model for applications that have real-world impact.\n* It's important to test this model thoroughly across a variety of Ukrainian language samples to evaluate its reliability and fairness." ]	[ "TAGS\n#transformers #safetensors #t5 #text2text-generation #uk #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# ukr-t5-small\n\nA compact T5-small model fine-tuned for Ukrainian language tasks, with base English understanding.", "## Model Description\n\n* Base Model: mT5-small \n* Fine-tuning Data: Leipzig Corpora Collection (English & Ukrainian news from 2023)\n* Tasks: \n * Text summarization (Ukrainian)\n * Text generation (Ukrainian)\n * Other Ukrainian-centric NLP tasks", "## Technical Details\n* Model Size: 300 MB \n* Framework: Transformers (Hugging Face)", "## Usage \n\nInstallation\n\n\n\nLoading the Model\n\n\n\nExample: Machine Translation", "## Limitations\n\n* The model's focus is on Ukrainian text processing, so performance on purely English tasks may be below that of general T5-small models.\n* Further fine-tuning may be required for optimal results on specific NLP tasks.", "## Dataset Credits\n\nThis model was fine-tuned on the Leipzig Corpora Collection (specify if there's a particular subset within the collection that you used). For full licensing and usage information of the original dataset, please refer to Leipzig Corpora Collection website", "## Ethical Considerations\n\n* NLP models can reflect biases present in their training data. Be mindful of this when using this model for applications that have real-world impact.\n* It's important to test this model thoroughly across a variety of Ukrainian language samples to evaluate its reliability and fairness." ]	[ 61, 30, 66, 21, 12, 57, 56, 69 ]	[ "passage: TAGS\n#transformers #safetensors #t5 #text2text-generation #uk #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# ukr-t5-small\n\nA compact T5-small model fine-tuned for Ukrainian language tasks, with base English understanding.## Model Description\n\n* Base Model: mT5-small \n* Fine-tuning Data: Leipzig Corpora Collection (English & Ukrainian news from 2023)\n* Tasks: \n * Text summarization (Ukrainian)\n * Text generation (Ukrainian)\n * Other Ukrainian-centric NLP tasks## Technical Details\n* Model Size: 300 MB \n* Framework: Transformers (Hugging Face)## Usage \n\nInstallation\n\n\n\nLoading the Model\n\n\n\nExample: Machine Translation## Limitations\n\n* The model's focus is on Ukrainian text processing, so performance on purely English tasks may be below that of general T5-small models.\n* Further fine-tuning may be required for optimal results on specific NLP tasks.## Dataset Credits\n\nThis model was fine-tuned on the Leipzig Corpora Collection (specify if there's a particular subset within the collection that you used). For full licensing and usage information of the original dataset, please refer to Leipzig Corpora Collection website## Ethical Considerations\n\n* NLP models can reflect biases present in their training data. Be mindful of this when using this model for applications that have real-world impact.\n* It's important to test this model thoroughly across a variety of Ukrainian language samples to evaluate its reliability and fairness." ]	[ -0.04810088500380516, -0.0018965151393786073, -0.0018024018500000238, 0.024495404213666916, 0.13056443631649017, 0.0319400355219841, 0.12555581331253052, 0.06213844195008278, 0.011751469224691391, 0.04283849522471428, -0.05197909101843834, -0.07440099865198135, 0.027377033606171608, 0.041328251361846924, 0.11798960715532303, -0.24818813800811768, 0.04244768247008324, -0.09371974319219589, -0.044583551585674286, 0.028527015820145607, 0.15373726189136505, -0.08349137008190155, 0.03770899027585983, -0.03322811424732208, -0.016515720635652542, 0.06811641156673431, 0.03516490012407303, -0.03137114271521568, 0.11755725741386414, 0.1243203654885292, 0.06725147366523743, -0.05308849364519119, 0.04625359922647476, -0.2064218968153, 0.010294810868799686, 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null	null	null	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="0fluff0/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	0fluff0/q-FrozenLake-v1-4x4-noSlippery	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T12:39:10+00:00	[]	[]	TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage	[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 40, 39 ]	[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 0.04578453302383423, 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null	null	transformers	# Pasta-Sea-7b-128k 8bpw Exllama quant of [Test157t/Pasta-Sea-7b-128k](https://huggingface.co/Test157t/Pasta-Sea-7b-128k) ## Prompt format: Alpaca ``` Below is an instruction that describes a task. Write a response that appropriately completes the request. ### Instruction: {prompt} ### Input: {input} ### Response: ``` ## Contact Kooten on discord [ko-fi.com/kooten](https://ko-fi.com/kooten) if you would like to support me	{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Test157t/Kunocchini-7b-128k-test", "Test157t/Pasta-Lake-7b"]}	text-generation	Kooten/Pasta-Sea-7b-128k-8bpw-exl2	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "base_model:Test157t/Kunocchini-7b-128k-test", "base_model:Test157t/Pasta-Lake-7b", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T12:40:31+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Kunocchini-7b-128k-test #base_model-Test157t/Pasta-Lake-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Pasta-Sea-7b-128k 8bpw Exllama quant of Test157t/Pasta-Sea-7b-128k ## Prompt format: Alpaca ## Contact Kooten on discord URL if you would like to support me	[ "# Pasta-Sea-7b-128k 8bpw\nExllama quant of Test157t/Pasta-Sea-7b-128k", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL if you would like to support me" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Kunocchini-7b-128k-test #base_model-Test157t/Pasta-Lake-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Pasta-Sea-7b-128k 8bpw\nExllama quant of Test157t/Pasta-Sea-7b-128k", "## Prompt format: Alpaca", "## Contact\nKooten on discord\n\nURL if you would like to support me" ]	[ 90, 33, 8, 14 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #mergekit #merge #base_model-Test157t/Kunocchini-7b-128k-test #base_model-Test157t/Pasta-Lake-7b #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Pasta-Sea-7b-128k 8bpw\nExllama quant of Test157t/Pasta-Sea-7b-128k## Prompt format: Alpaca## Contact\nKooten on discord\n\nURL if you would like to support me" ]	[ -0.13665421307086945, 0.03328120335936546, -0.0023977274540811777, 0.010874534957110882, 0.030814211815595627, 0.0052447132766246796, 0.1293209344148636, 0.13238875567913055, 0.17011351883411407, 0.04021481052041054, 0.0887300968170166, 0.04026902839541435, 0.046389609575271606, 0.1818971186876297, -0.08776205033063889, -0.10393071174621582, 0.05500900745391846, 0.01404658704996109, 0.042717985808849335, 0.1634928584098816, 0.0969085842370987, -0.06761901080608368, 0.10274248570203781, -0.062132328748703, -0.021953262388706207, 0.08013993501663208, 0.014364074915647507, -0.10090426355600357, 0.09181226789951324, 0.04749181494116783, 0.12797346711158752, 0.07394697517156601, -0.02831091731786728, -0.14941003918647766, 0.06479524821043015, 0.03624352067708969, 0.03407234698534012, 0.02668621391057968, 0.05695432052016258, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"license": "apache-2.0", "library_name": "transformers", "datasets": ["jondurbin/truthy-dpo-v0.1"]}	text-generation	vicgalle/CarbonBeagle-11B-truthy	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:jondurbin/truthy-dpo-v0.1", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T12:40:49+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 84, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]" ]	[ -0.07881546765565872, 0.19828130304813385, -0.003788086585700512, 0.02594563364982605, 0.10099275410175323, -0.004296048078685999, 0.0479087196290493, 0.1277962476015091, 0.00588206248357892, 0.13631753623485565, 0.035890862345695496, 0.15719355642795563, 0.10844240337610245, 0.1793917864561081, 0.007658287417143583, -0.15868471562862396, 0.0245195385068655, -0.10021348297595978, 0.024458106607198715, 0.11435500532388687, 0.12693296372890472, -0.10097949206829071, 0.06654553860425949, -0.04340309277176857, 0.005332469940185547, -0.05228358507156372, -0.07581142336130142, -0.03051694668829441, 0.04182282090187073, 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null	null	sample-factory	A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory. Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/ ## Downloading the model After installing Sample-Factory, download the model with: ``` python -m sample_factory.huggingface.load_from_hub -r IrinaArcadievna/rl_course_vizdoom_health_gathering_supreme ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme ``` You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag. See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details ## Training with this model To continue training with this model, use the `train` script corresponding to this environment: ``` python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000 ``` Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.	{"library_name": "sample-factory", "tags": ["deep-reinforcement-learning", "reinforcement-learning", "sample-factory"], "model-index": [{"name": "APPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "doom_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "11.48 +/- 5.52", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	IrinaArcadievna/rl_course_vizdoom_health_gathering_supreme	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-10T12:41:40+00:00	[]	[]	TAGS #sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	A(n) APPO model trained on the doom_health_gathering_supreme environment. This model was trained using Sample-Factory 2.0: URL Documentation for how to use Sample-Factory can be found at URL ## Downloading the model After installing Sample-Factory, download the model with: ## Using the model To run the model after download, use the 'enjoy' script corresponding to this environment: You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag. See URL for more details ## Training with this model To continue training with this model, use the 'train' script corresponding to this environment: Note, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at.	[ "## Downloading the model\n\nAfter installing Sample-Factory, download the model with:", "## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details", "## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at." ]	[ "TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "## Downloading the model\n\nAfter installing Sample-Factory, download the model with:", "## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details", "## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at." ]	[ 34, 19, 59, 67 ]	[ "passage: TAGS\n#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n## Downloading the model\n\nAfter installing Sample-Factory, download the model with:## Using the model\n\nTo run the model after download, use the 'enjoy' script corresponding to this environment:\n\n\n\nYou can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.\nSee URL for more details## Training with this model\n\nTo continue training with this model, use the 'train' script corresponding to this environment:\n\n\nNote, you may have to adjust '--train_for_env_steps' to a suitably high number as the experiment will resume at the number of steps it concluded at." ]	[ -0.162887305021286, -0.07949446886777878, 0.0013769814977422357, 0.0244897473603487, 0.13643795251846313, 0.08826540410518646, 0.13243556022644043, 0.07938782125711441, 0.19449298083782196, 0.07451266050338745, 0.12160012871026993, 0.06742649525403976, 0.02505551464855671, 0.31084391474723816, 0.08655242621898651, -0.18235880136489868, 0.031082456931471825, -0.06436605006456375, -0.02882574498653412, 0.05590416118502617, 0.050910040736198425, -0.06422623991966248, 0.11641133576631546, -0.05714287608861923, -0.15497641265392303, 0.08288847655057907, 0.008126083761453629, 0.03596968948841095, 0.12199652194976807, -0.007729834411293268, 0.06358569860458374, 0.02508161962032318, 0.09885215014219284, -0.08979995548725128, 0.05817115306854248, 0.037268251180648804, -0.005583701189607382, 0.0697544738650322, -0.02916712686419487, 0.01197513286024332, 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null	null	transformers	<div align="center"> <h1> MiniCPM </h1> </div> <p align="center"> <a href="https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4" target="_blank">MiniCPM 技术报告</a><a href="https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4" target="_blank"> Technical Report</a> \| <a href="https://github.com/OpenBMB/OmniLMM/" target="_blank">OmniLMM 多模态模型 Multi-modal Model</a> \| <a href="https://luca.cn/" target="_blank">CPM-C 千亿模型试用 ~100B Model Trial </a> </p> MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 - 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 - 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 - 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 - 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 - 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 - 基于MiniCPM-2B的指令微调与人类偏好对MiniCPM-2B-SFT/DPO。 - 基于MiniCPM-2B的多模态模型MiniCPM-V，能力超越基于Phi-2的同参数级别多模态模型。 - MiniCPM-2B-SFT/DPO的Int4量化版MiniCPM-2B-SFT/DPO-Int4。 - 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，文本及多模态模型均可在手机端进行推理。 MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. - MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. - After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. - MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. - MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. - The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. - SFT and DPO version based on MiniCPM-2B and human preference: MiniCPM-2B-SFT/DPO - The multi-modal model MiniCPM-V based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 - The INT4 quantized version MiniCPM-2B-SFT/DPO-Int4 based on MiniCPM-2B-SFT/DPO - Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于[github仓库](https://github.com/OpenBMB/MiniCPM?tab=readme-ov-file#%E8%AF%84%E6%B5%8B%E7%BB%93%E6%9E%9C) Detailed evaluation results are in [github repo](https://github.com/OpenBMB/MiniCPM/blob/main/README-en.md#evaluation-results) 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations - 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； - 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； - 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； - 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 - Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. - To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. - Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. - Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. ## 模型下载 Download \| HuggingFace \| ModelScope \| WiseModel \| \|-------------\|------------\|-----------\| \|[sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16)\|[sft-bf16](https://modelscope.cn/models/OpenBMB/miniCPM-bf16)\|[sft-bf16](https://wisemodel.cn/models/OpenBMB/miniCPM-bf16) \|[sft-fp32](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32)\|[sft-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-sft-fp32)\|[sft-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) \|[dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)\|[dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)\|[dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16) \|[dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)\|[dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)\|[dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16) \|[dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)\|[dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)\|[dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) ## 模型使用 Usage * 安装`transformers>=4.36.0`以及`accelerate`后，运行以下代码 * 注意：需要在`from_pretrained`中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install `transformers>=4.36.0` and `accelerate` * Warning: It is necessary to specify the data type of the model clearly in 'from_pretrained', otherwise large calculation errors will be caused ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch.manual_seed(0) path = 'openbmb/MiniCPM-2B-sft-bf16' tokenizer = AutoTokenizer.from_pretrained(path) model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True) responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.8, top_p=0.8) print(responds) ``` * 期望输出 Expected Output ```shell 山东省最高的山是泰山，海拔1545米。相对于黄山（海拔1864米），泰山海拔较低，相差约319米。 ``` ## 开源协议 LICENSE #### 模型协议 Model LICENSE * 本仓库中代码依照 [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 协议开源 * MiniCPM 模型权重的使用则需要遵循 [“通用模型许可协议-来源说明-宣传限制-商业授权”](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md)。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. * The usage of MiniCPM model weights must strictly follow [the General Model License (GML)](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to [email protected] to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. <p id="8"></p> ## 工作引用 Citation * 如果觉得MiniCPM有助于您的工作，请考虑引用下列[技术报告](https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4) * Please cite our [techinical report](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4) if you find our work valuable. ``` @inproceedings{minicpm2024, title={MiniCPM：Unveiling the Potential of End-side Large Language Models}, booktitle={OpenBMB Blog}, year={2024} } ```	{"language": ["en", "zh"], "tags": ["MiniCPM", "ModelBest", "THUNLP"]}	null	jncraton/MiniCPM-2B-sft-bf16-llama-format-ct2-int8	[ "transformers", "MiniCPM", "ModelBest", "THUNLP", "en", "zh", "endpoints_compatible", "region:us" ]	2024-02-10T12:44:48+00:00	[]	[ "en", "zh" ]	TAGS #transformers #MiniCPM #ModelBest #THUNLP #en #zh #endpoints_compatible #region-us	MiniCPM ========= [MiniCPM 技术报告](URL target=) [Technical Report](URL target=) \| [OmniLMM 多模态模型 Multi-modal Model](URL target=) \| [CPM-C 千亿模型试用 ~100B Model Trial](URL target=) MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 * 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 * 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 * 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 * 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 * 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 * 基于MiniCPM-2B的指令微调与人类偏好对MiniCPM-2B-SFT/DPO。 * 基于MiniCPM-2B的多模态模型MiniCPM-V，能力超越基于Phi-2的同参数级别多模态模型。 * MiniCPM-2B-SFT/DPO的Int4量化版MiniCPM-2B-SFT/DPO-Int4。 * 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，文本及多模态模型均可在手机端进行推理。 MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. * MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. * After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. * MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. * MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. * The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. * SFT and DPO version based on MiniCPM-2B and human preference: MiniCPM-2B-SFT/DPO * The multi-modal model MiniCPM-V based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 * The INT4 quantized version MiniCPM-2B-SFT/DPO-Int4 based on MiniCPM-2B-SFT/DPO * Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于github仓库 Detailed evaluation results are in github repo 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations * 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； * 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； * 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； * 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 * Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. * To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. * Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. * Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. 模型下载 Download ------------- HuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16 HuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32 HuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16 HuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16 HuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32 模型使用 Usage ---------- * 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码 * 注意：需要在'from\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install 'transformers>=4.36.0' and 'accelerate' * Warning: It is necessary to specify the data type of the model clearly in 'from\_pretrained', otherwise large calculation errors will be caused * 期望输出 Expected Output 开源协议 LICENSE ------------ #### 模型协议 Model LICENSE * 本仓库中代码依照 Apache-2.0 协议开源 * MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the Apache-2.0 License. * The usage of MiniCPM model weights must strictly follow the General Model License (GML). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. 工作引用 Citation ------------- * 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告 * Please cite our techinical report if you find our work valuable.	[ "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]	[ "TAGS\n#transformers #MiniCPM #ModelBest #THUNLP #en #zh #endpoints_compatible #region-us \n", "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]	[ 32, 101, 655, 180, 262 ]	[ "passage: TAGS\n#transformers #MiniCPM #ModelBest #THUNLP #en #zh #endpoints_compatible #region-us \n### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now." ]	[ -0.007029905449599028, -0.18129052221775055, -0.005951940082013607, 0.035357579588890076, 0.09986112266778946, -0.0005699562607333064, 0.046559251844882965, 0.0894036665558815, -0.0011693296255543828, 0.014035903848707676, 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null	null	null	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="0fluff0/taxi", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "taxi", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	0fluff0/taxi	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T12:45:45+00:00	[]	[]	TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage	[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 32, 33 ]	[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 0.048862796276807785, -0.16549694538116455, -0.005485367961227894, 0.02960980497300625, 0.1345081776380539, -0.01784728653728962, 0.11895976960659027, 0.07759871333837509, -0.07461097836494446, -0.055395450443029404, 0.1418241262435913, 0.09088201075792313, 0.055222880095243454, 0.05699880048632622, 0.09511256217956543, 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null	null	transformers	## BigMaid-20B-v1.0 [exllamav2](https://github.com/turboderp/exllamav2) quant for [TeeZee/BigMaid-20B-v1.0](https://huggingface.co/TeeZee/BigMaid-20B-v1.0) Should run on 12 GB of VRAM cards in webui with context length set to 4096, ExLlamav2_HF loader and cache_8bit=True All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel: <a href="https://www.buymeacoffee.com/TeeZee" target="_blank"><img src="https://cd	{"license": "apache-2.0", "tags": ["merge", "not-for-all-audiences"]}	text-generation	TeeZee/BigMaid_20B_v1.0-bpw4-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "merge", "not-for-all-audiences", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T12:53:04+00:00	[]	[]	TAGS #transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	## BigMaid-20B-v1.0 exllamav2 quant for TeeZee/BigMaid-20B-v1.0 Should run on 12 GB of VRAM cards in webui with context length set to 4096, ExLlamav2_HF loader and cache_8bit=True All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel: <a href="URL target="_blank"><img src="https://cd	[ "## BigMaid-20B-v1.0\n\nexllamav2 quant for TeeZee/BigMaid-20B-v1.0\n\nShould run on 12 GB of VRAM cards in webui with context length set to 4096, ExLlamav2_HF loader\nand cache_8bit=True\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"https://cd" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "## BigMaid-20B-v1.0\n\nexllamav2 quant for TeeZee/BigMaid-20B-v1.0\n\nShould run on 12 GB of VRAM cards in webui with context length set to 4096, ExLlamav2_HF loader\nand cache_8bit=True\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"https://cd" ]	[ 67, 110 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #merge #not-for-all-audiences #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n## BigMaid-20B-v1.0\n\nexllamav2 quant for TeeZee/BigMaid-20B-v1.0\n\nShould run on 12 GB of VRAM cards in webui with context length set to 4096, ExLlamav2_HF loader\nand cache_8bit=True\n\nAll comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:\n<a href=\"URL target=\"_blank\"><img src=\"https://cd" ]	[ -0.03086857497692108, -0.1447758823633194, -0.0019936945755034685, 0.10542882978916168, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	Sakil/mistral_7b_finetuned	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T12:57:23+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	null	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="aturja65/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	aljaziz/q-Taxi-v3	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T13:05:05+00:00	[]	[]	TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage	[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 32, 33 ]	[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 0.048862796276807785, -0.16549694538116455, -0.005485367961227894, 0.02960980497300625, 0.1345081776380539, -0.01784728653728962, 0.11895976960659027, 0.07759871333837509, -0.07461097836494446, -0.055395450443029404, 0.1418241262435913, 0.09088201075792313, 0.055222880095243454, 0.05699880048632622, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Qwen1.5-1.8B-OpenHermes-2.5 This model is a fine-tuned version of [Qwen/Qwen1.5-1.8B](https://huggingface.co/Qwen/Qwen1.5-1.8B) on the teknium/OpenHermes-2.5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Inference ``` from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline model_id = "minghaowu/phi-2-OpenHermes-2.5" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto") your_instruction = <your_instruction> infer_prompt = f"### USER: {your_instruction} <\|endoftext\|>\n### ASSISTANT:" output = pipe(infer_prompt, do_sample=True, max_new_tokens=256)[0]["generated_text"] print(output) ```	{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["teknium/OpenHermes-2.5"], "base_model": "Qwen/Qwen1.5-1.8B", "model-index": [{"name": "Qwen1.5-1.8B-OpenHermes-2.5", "results": []}]}	text-generation	minghaowu/Qwen1.5-1.8B-OpenHermes-2.5	[ "transformers", "safetensors", "qwen2", "text-generation", "generated_from_trainer", "conversational", "dataset:teknium/OpenHermes-2.5", "base_model:Qwen/Qwen1.5-1.8B", "license:other", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T13:06:34+00:00	[]	[]	TAGS #transformers #safetensors #qwen2 #text-generation #generated_from_trainer #conversational #dataset-teknium/OpenHermes-2.5 #base_model-Qwen/Qwen1.5-1.8B #license-other #autotrain_compatible #endpoints_compatible #region-us	# Qwen1.5-1.8B-OpenHermes-2.5 This model is a fine-tuned version of Qwen/Qwen1.5-1.8B on the teknium/OpenHermes-2.5 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.1 - Tokenizers 0.15.1 ### Inference	[ "# Qwen1.5-1.8B-OpenHermes-2.5\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-1.8B on the teknium/OpenHermes-2.5 dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 128\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1", "### Inference" ]	[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #generated_from_trainer #conversational #dataset-teknium/OpenHermes-2.5 #base_model-Qwen/Qwen1.5-1.8B #license-other #autotrain_compatible #endpoints_compatible #region-us \n", "# Qwen1.5-1.8B-OpenHermes-2.5\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-1.8B on the teknium/OpenHermes-2.5 dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 128\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1.0", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1", "### Inference" ]	[ 81, 45, 6, 12, 8, 3, 157, 4, 33, 5 ]	[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #generated_from_trainer #conversational #dataset-teknium/OpenHermes-2.5 #base_model-Qwen/Qwen1.5-1.8B #license-other #autotrain_compatible #endpoints_compatible #region-us \n# Qwen1.5-1.8B-OpenHermes-2.5\n\nThis model is a fine-tuned version of Qwen/Qwen1.5-1.8B on the teknium/OpenHermes-2.5 dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 4\n- eval_batch_size: 8\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 2\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 128\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 1.0### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.0.1+cu117\n- Datasets 2.16.1\n- Tokenizers 0.15.1### Inference" ]	[ -0.10150772333145142, 0.18253980576992035, -0.003545155515894294, 0.07475906610488892, 0.09023259580135345, 0.03941012918949127, 0.09200479835271835, 0.1539490520954132, -0.034231435507535934, 0.11628200113773346, 0.061100561171770096, 0.023559581488370895, 0.0849762037396431, 0.1146286278963089, 0.008505710400640965, -0.2199392020702362, 0.020250393077731133, -0.048189129680395126, -0.0409836508333683, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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]	{"library_name": "transformers", "tags": []}	null	imsanjoykb/QLoRA-Finetuning-Barbarik	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T13:07:46+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	null	GGUF quant of [alexshengzhili/llava-v1.5-13b-dpo](https://huggingface.co/alexshengzhili/llava-v1.5-13b-dpo)	{"license": "llama2", "tags": ["gguf", "dpo"]}	null	antiven0m/llava-v1.5-13b-dpo-gguf	[ "gguf", "dpo", "license:llama2", "region:us" ]	2024-02-10T13:10:18+00:00	[]	[]	TAGS #gguf #dpo #license-llama2 #region-us	GGUF quant of alexshengzhili/llava-v1.5-13b-dpo	[]	[ "TAGS\n#gguf #dpo #license-llama2 #region-us \n" ]	[ 19 ]	[ "passage: TAGS\n#gguf #dpo #license-llama2 #region-us \n" ]	[ -0.008604014292359352, 0.10990592837333679, -0.010436690412461758, -0.011385667137801647, 0.029359333217144012, 0.038117341697216034, 0.16163727641105652, 0.04485592618584633, 0.17062106728553772, -0.03827083483338356, 0.1400679349899292, 0.10981146991252899, 0.03900774568319321, -0.013631194829940796, -0.014640500769019127, -0.19766303896903992, 0.054711345583200455, -0.0447491779923439, -0.027350930497050285, 0.013151803985238075, 0.023680707439780235, -0.03657340630888939, 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null	null	null	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="aturja65/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	aljaziz/q-FrozenLake-v1-4x4-noSlippery	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T13:11:07+00:00	[]	[]	TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage	[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 40, 39 ]	[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 0.04578453302383423, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # RerankerModel_chat_bot This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "RerankerModel_chat_bot", "results": []}]}	text-classification	Shakhovak/RerankerModel_chat_bot	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:bert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "has_space", "region:us" ]	2024-02-10T13:13:53+00:00	[]	[]	TAGS #transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us	# RerankerModel_chat_bot This model is a fine-tuned version of bert-base-uncased on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# RerankerModel_chat_bot\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n", "# RerankerModel_chat_bot\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 68, 35, 6, 12, 8, 3, 106, 33 ]	[ "passage: TAGS\n#transformers #safetensors #bert #text-classification #generated_from_trainer #base_model-bert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #region-us \n# RerankerModel_chat_bot\n\nThis model is a fine-tuned version of bert-base-uncased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.2.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ -0.07152149826288223, 0.08271459490060806, -0.002031854121014476, 0.08368208259344101, 0.1286334991455078, 0.029067400842905045, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	tyemel/vit-base-patch16-224-in21k-finetuned-lora-food101	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T13:16:41+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Base Bisyllabic Jyutping This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the AlienKevin/cantone dataset. It achieves the following results on the evaluation set: - Loss: 0.3613 - Wer: 41.25 ## 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: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 400 - training_steps: 2400 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\| \| 0.1093 \| 0.08 \| 400 \| 0.3231 \| 51.0417 \| \| 0.0389 \| 0.15 \| 800 \| 0.2922 \| 40.4861 \| \| 0.0237 \| 0.23 \| 1200 \| 0.3020 \| 37.7778 \| \| 0.0131 \| 0.3 \| 1600 \| 0.3561 \| 42.7083 \| \| 0.01 \| 0.38 \| 2000 \| 0.3817 \| 44.6528 \| \| 0.0095 \| 0.46 \| 2400 \| 0.3613 \| 41.25 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0 - Datasets 2.14.5 - Tokenizers 0.15.1	{"language": ["yue"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-base", "model-index": [{"name": "Whisper Base Bisyllabic Jyutping", "results": []}]}	automatic-speech-recognition	AlienKevin/whisper-base-bisyllabic-jyutping	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "yue", "base_model:openai/whisper-base", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-10T13:17:00+00:00	[]	[ "yue" ]	TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #yue #base_model-openai/whisper-base #license-apache-2.0 #endpoints_compatible #region-us	Whisper Base Bisyllabic Jyutping ================================ This model is a fine-tuned version of openai/whisper-base on the AlienKevin/cantone dataset. It achieves the following results on the evaluation set: * Loss: 0.3613 * Wer: 41.25 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: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 400 * training\_steps: 2400 ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0 * Datasets 2.14.5 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* training\\_steps: 2400", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.5\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #yue #base_model-openai/whisper-base #license-apache-2.0 #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* training\\_steps: 2400", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.5\n* Tokenizers 0.15.1" ]	[ 71, 115, 4, 35 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #yue #base_model-openai/whisper-base #license-apache-2.0 #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 400\n* training\\_steps: 2400### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0\n* Datasets 2.14.5\n* Tokenizers 0.15.1" ]	[ -0.10426213592290878, 0.12275201082229614, -0.0037801957223564386, 0.07633250206708908, 0.0798211321234703, -0.029333341866731644, 0.1729552298784256, 0.14572720229625702, -0.09194523841142654, 0.07240274548530579, 0.10668366402387619, 0.11803773790597916, 0.04197275638580322, 0.17875000834465027, 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null	null	transformers	# LinkBERT: Fine-tuned BERT for Natural Link Prediction LinkBERT is an advanced fine-tuned version of the [bert-large-cased](https://huggingface.co/google-bert/bert-large-cased) model developed by [Dejan Marketing](https://dejanmarketing.com/). The model is designed to predict natural link placement within web content. This binary classification model excels in identifying distinct token ranges that web authors are likely to choose as anchor text for links. By analyzing never-before-seen texts, LinkBERT can predict areas within the content where links might naturally occur, effectively simulating web author behavior in link creation. # Online Demo Online demo of this model is available at https://linkbert.com/ ## Applications of LinkBERT LinkBERT's applications are vast and diverse, tailored to enhance both the efficiency and quality of web content creation and analysis: - Anchor Text Suggestion: Acts as a mechanism during internal link optimization, suggesting potential anchor texts to web authors. - Evaluation of Existing Links: Assesses the naturalness of link placements within existing content, aiding in the refinement of web pages. - Link Placement Guide: Offers guidance to link builders by suggesting optimal placement for links within content. - Anchor Text Idea Generator: Provides creative anchor text suggestions to enrich content and improve SEO strategies. - Spam and Inorganic SEO Detection: Helps identify unnatural link patterns, contributing to the detection of spam and inorganic SEO tactics. ## Training and Performance LinkBERT was fine-tuned on a dataset of organic web content and editorial links. The training involved preprocessing web content, annotating links with temporary markup for clear distinction, and employing a specialized tokenization process to prepare the data for model training. In adition to commonly available data sources such as Wikipedia, additional training data was also sourced from: - [Owayo USA](https://www.owayo.com/) - [Owayo Germany](https://www.owayo.de/) - [Compare the Market](https://www.comparethemarket.com.au/) - [Leonardo AI](https://leonardo.ai/) - [The Wests Group](https://mywests.com.au/) - [Zendesk](https://www.zendesk.com/) - [Australia Post](https://auspost.com.au/) - [Probe Group](https://www.probecx.com/) - [Monarch Institute](https://www.monarch.edu.au/) - And many others... ### Training Highlights: - Dataset: Custom organic web content with editorial links. - Preprocessing: Links annotated with `[START_LINK]` and `[END_LINK]` markup. - Tokenization: Utilized input_ids, token_type_ids, attention_mask, and labels for model training, with a unique labeling system to differentiate between link/anchor text and plain text. ### Technical Specifications: - Batch Size: 10, with class weights adjusted to address class imbalance between link and plain text. - Optimizer: AdamW with a learning rate of 5e-5. - Epochs: 5, incorporating gradient accumulation and warmup steps to optimize training outcomes. - Hardware: 1 x RTX4090 24GB VRAM - Duration: 32 hours ## Utilization and Integration LinkBERT is positioned as a powerful tool for content creators, SEO specialists, and webmasters, offering unparalleled support in optimizing web content for both user engagement and search engine recognition. Its predictive capabilities not only streamline the content creation process but also offer insights into the natural integration of links, enhancing the overall quality and relevance of web content. ## Accessibility LinkBERT leverages the robust architecture of bert-large-cased, enhancing it with capabilities specifically tailored for web content analysis. This model represents a significant advancement in the understanding and generation of web content, providing a nuanced approach to natural link prediction and anchor text suggestion. --- # BERT large model (cased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in [this paper](https://arxiv.org/abs/1810.04805) and first released in [this repository](https://github.com/google-research/bert). This model is cased: it makes a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters.	{"language": "en", "license": "bigscience-bloom-rail-1.0", "datasets": ["bookcorpus", "wikipedia"], "pipeline_tag": "token-classification", "widget": [{"text": "LinkBERT is an advanced fine-tuned version of the bert-large-cased model developed by Dejan Marketing. The model is designed to predict natural link placement within web content."}]}	token-classification	dejanseo/LinkBERT	[ "transformers", "pytorch", "bert", "fill-mask", "token-classification", "en", "dataset:bookcorpus", "dataset:wikipedia", "arxiv:1810.04805", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T13:18:00+00:00	[ "1810.04805" ]	[ "en" ]	TAGS #transformers #pytorch #bert #fill-mask #token-classification #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #region-us	# LinkBERT: Fine-tuned BERT for Natural Link Prediction LinkBERT is an advanced fine-tuned version of the bert-large-cased model developed by Dejan Marketing. The model is designed to predict natural link placement within web content. This binary classification model excels in identifying distinct token ranges that web authors are likely to choose as anchor text for links. By analyzing never-before-seen texts, LinkBERT can predict areas within the content where links might naturally occur, effectively simulating web author behavior in link creation. # Online Demo Online demo of this model is available at URL ## Applications of LinkBERT LinkBERT's applications are vast and diverse, tailored to enhance both the efficiency and quality of web content creation and analysis: - Anchor Text Suggestion: Acts as a mechanism during internal link optimization, suggesting potential anchor texts to web authors. - Evaluation of Existing Links: Assesses the naturalness of link placements within existing content, aiding in the refinement of web pages. - Link Placement Guide: Offers guidance to link builders by suggesting optimal placement for links within content. - Anchor Text Idea Generator: Provides creative anchor text suggestions to enrich content and improve SEO strategies. - Spam and Inorganic SEO Detection: Helps identify unnatural link patterns, contributing to the detection of spam and inorganic SEO tactics. ## Training and Performance LinkBERT was fine-tuned on a dataset of organic web content and editorial links. The training involved preprocessing web content, annotating links with temporary markup for clear distinction, and employing a specialized tokenization process to prepare the data for model training. In adition to commonly available data sources such as Wikipedia, additional training data was also sourced from: - Owayo USA - Owayo Germany - Compare the Market - Leonardo AI - The Wests Group - Zendesk - Australia Post - Probe Group - Monarch Institute - And many others... ### Training Highlights: - Dataset: Custom organic web content with editorial links. - Preprocessing: Links annotated with '[START_LINK]' and '[END_LINK]' markup. - Tokenization: Utilized input_ids, token_type_ids, attention_mask, and labels for model training, with a unique labeling system to differentiate between link/anchor text and plain text. ### Technical Specifications: - Batch Size: 10, with class weights adjusted to address class imbalance between link and plain text. - Optimizer: AdamW with a learning rate of 5e-5. - Epochs: 5, incorporating gradient accumulation and warmup steps to optimize training outcomes. - Hardware: 1 x RTX4090 24GB VRAM - Duration: 32 hours ## Utilization and Integration LinkBERT is positioned as a powerful tool for content creators, SEO specialists, and webmasters, offering unparalleled support in optimizing web content for both user engagement and search engine recognition. Its predictive capabilities not only streamline the content creation process but also offer insights into the natural integration of links, enhancing the overall quality and relevance of web content. ## Accessibility LinkBERT leverages the robust architecture of bert-large-cased, enhancing it with capabilities specifically tailored for web content analysis. This model represents a significant advancement in the understanding and generation of web content, providing a nuanced approach to natural link prediction and anchor text suggestion. --- # BERT large model (cased) Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English. Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives: - Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence. - Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the BERT model as inputs. This model has the following configuration: - 24-layer - 1024 hidden dimension - 16 attention heads - 336M parameters.	[ "# LinkBERT: Fine-tuned BERT for Natural Link Prediction\n\nLinkBERT is an advanced fine-tuned version of the bert-large-cased model developed by Dejan Marketing. The model is designed to predict natural link placement within web content. This binary classification model excels in identifying distinct token ranges that web authors are likely to choose as anchor text for links. By analyzing never-before-seen texts, LinkBERT can predict areas within the content where links might naturally occur, effectively simulating web author behavior in link creation.", "# Online Demo\n\nOnline demo of this model is available at URL", "## Applications of LinkBERT\n\nLinkBERT's applications are vast and diverse, tailored to enhance both the efficiency and quality of web content creation and analysis:\n\n- Anchor Text Suggestion: Acts as a mechanism during internal link optimization, suggesting potential anchor texts to web authors.\n- Evaluation of Existing Links: Assesses the naturalness of link placements within existing content, aiding in the refinement of web pages.\n- Link Placement Guide: Offers guidance to link builders by suggesting optimal placement for links within content.\n- Anchor Text Idea Generator: Provides creative anchor text suggestions to enrich content and improve SEO strategies.\n- Spam and Inorganic SEO Detection: Helps identify unnatural link patterns, contributing to the detection of spam and inorganic SEO tactics.", "## Training and Performance\n\nLinkBERT was fine-tuned on a dataset of organic web content and editorial links. The training involved preprocessing web content, annotating links with temporary markup for clear distinction, and employing a specialized tokenization process to prepare the data for model training. In adition to commonly available data sources such as Wikipedia, additional training data was also sourced from:\n\n- Owayo USA\n- Owayo Germany\n- Compare the Market\n- Leonardo AI\n- The Wests Group\n- Zendesk\n- Australia Post\n- Probe Group\n- Monarch Institute\n- And many others...", "### Training Highlights:\n\n- Dataset: Custom organic web content with editorial links.\n- Preprocessing: Links annotated with '[START_LINK]' and '[END_LINK]' markup.\n- Tokenization: Utilized input_ids, token_type_ids, attention_mask, and labels for model training, with a unique labeling system to differentiate between link/anchor text and plain text.", "### Technical Specifications:\n\n- Batch Size: 10, with class weights adjusted to address class imbalance between link and plain text.\n- Optimizer: AdamW with a learning rate of 5e-5.\n- Epochs: 5, incorporating gradient accumulation and warmup steps to optimize training outcomes.\n- Hardware: 1 x RTX4090 24GB VRAM\n- Duration: 32 hours", "## Utilization and Integration\n\nLinkBERT is positioned as a powerful tool for content creators, SEO specialists, and webmasters, offering unparalleled support in optimizing web content for both user engagement and search engine recognition. Its predictive capabilities not only streamline the content creation process but also offer insights into the natural integration of links, enhancing the overall quality and relevance of web content.", "## Accessibility\n\nLinkBERT leverages the robust architecture of bert-large-cased, enhancing it with capabilities specifically tailored for web content analysis. This model represents a significant advancement in the understanding and generation of web content, providing a nuanced approach to natural link prediction and anchor text suggestion.\n\n---", "# BERT large model (cased)\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it makes a difference\nbetween english and English.\n\nDisclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by\nthe Hugging Face team.", "## Model description\n\nBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it\nwas pretrained with two objectives:\n\n- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run\n the entire masked sentence through the model and has to predict the masked words. This is different from traditional\n recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like\n GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the\n sentence.\n- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes\n they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to\n predict if the two sentences were following each other or not.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nThis model has the following configuration:\n\n- 24-layer\n- 1024 hidden dimension\n- 16 attention heads\n- 336M parameters." ]	[ "TAGS\n#transformers #pytorch #bert #fill-mask #token-classification #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# LinkBERT: Fine-tuned BERT for Natural Link Prediction\n\nLinkBERT is an advanced fine-tuned version of the bert-large-cased model developed by Dejan Marketing. The model is designed to predict natural link placement within web content. This binary classification model excels in identifying distinct token ranges that web authors are likely to choose as anchor text for links. By analyzing never-before-seen texts, LinkBERT can predict areas within the content where links might naturally occur, effectively simulating web author behavior in link creation.", "# Online Demo\n\nOnline demo of this model is available at URL", "## Applications of LinkBERT\n\nLinkBERT's applications are vast and diverse, tailored to enhance both the efficiency and quality of web content creation and analysis:\n\n- Anchor Text Suggestion: Acts as a mechanism during internal link optimization, suggesting potential anchor texts to web authors.\n- Evaluation of Existing Links: Assesses the naturalness of link placements within existing content, aiding in the refinement of web pages.\n- Link Placement Guide: Offers guidance to link builders by suggesting optimal placement for links within content.\n- Anchor Text Idea Generator: Provides creative anchor text suggestions to enrich content and improve SEO strategies.\n- Spam and Inorganic SEO Detection: Helps identify unnatural link patterns, contributing to the detection of spam and inorganic SEO tactics.", "## Training and Performance\n\nLinkBERT was fine-tuned on a dataset of organic web content and editorial links. The training involved preprocessing web content, annotating links with temporary markup for clear distinction, and employing a specialized tokenization process to prepare the data for model training. In adition to commonly available data sources such as Wikipedia, additional training data was also sourced from:\n\n- Owayo USA\n- Owayo Germany\n- Compare the Market\n- Leonardo AI\n- The Wests Group\n- Zendesk\n- Australia Post\n- Probe Group\n- Monarch Institute\n- And many others...", "### Training Highlights:\n\n- Dataset: Custom organic web content with editorial links.\n- Preprocessing: Links annotated with '[START_LINK]' and '[END_LINK]' markup.\n- Tokenization: Utilized input_ids, token_type_ids, attention_mask, and labels for model training, with a unique labeling system to differentiate between link/anchor text and plain text.", "### Technical Specifications:\n\n- Batch Size: 10, with class weights adjusted to address class imbalance between link and plain text.\n- Optimizer: AdamW with a learning rate of 5e-5.\n- Epochs: 5, incorporating gradient accumulation and warmup steps to optimize training outcomes.\n- Hardware: 1 x RTX4090 24GB VRAM\n- Duration: 32 hours", "## Utilization and Integration\n\nLinkBERT is positioned as a powerful tool for content creators, SEO specialists, and webmasters, offering unparalleled support in optimizing web content for both user engagement and search engine recognition. Its predictive capabilities not only streamline the content creation process but also offer insights into the natural integration of links, enhancing the overall quality and relevance of web content.", "## Accessibility\n\nLinkBERT leverages the robust architecture of bert-large-cased, enhancing it with capabilities specifically tailored for web content analysis. This model represents a significant advancement in the understanding and generation of web content, providing a nuanced approach to natural link prediction and anchor text suggestion.\n\n---", "# BERT large model (cased)\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it makes a difference\nbetween english and English.\n\nDisclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by\nthe Hugging Face team.", "## Model description\n\nBERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. This means it\nwas pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of\npublicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it\nwas pretrained with two objectives:\n\n- Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run\n the entire masked sentence through the model and has to predict the masked words. This is different from traditional\n recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like\n GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the\n sentence.\n- Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes\n they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to\n predict if the two sentences were following each other or not.\n\nThis way, the model learns an inner representation of the English language that can then be used to extract features\nuseful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard\nclassifier using the features produced by the BERT model as inputs.\n\nThis model has the following configuration:\n\n- 24-layer\n- 1024 hidden dimension\n- 16 attention heads\n- 336M parameters." ]	[ 78, 129, 12, 183, 129, 96, 88, 87, 74, 92, 359 ]	[ "passage: TAGS\n#transformers #pytorch #bert #fill-mask #token-classification #en #dataset-bookcorpus #dataset-wikipedia #arxiv-1810.04805 #license-bigscience-bloom-rail-1.0 #autotrain_compatible #endpoints_compatible #region-us \n# LinkBERT: Fine-tuned BERT for Natural Link Prediction\n\nLinkBERT is an advanced fine-tuned version of the bert-large-cased model developed by Dejan Marketing. The model is designed to predict natural link placement within web content. This binary classification model excels in identifying distinct token ranges that web authors are likely to choose as anchor text for links. By analyzing never-before-seen texts, LinkBERT can predict areas within the content where links might naturally occur, effectively simulating web author behavior in link creation.# Online Demo\n\nOnline demo of this model is available at URL## Applications of LinkBERT\n\nLinkBERT's applications are vast and diverse, tailored to enhance both the efficiency and quality of web content creation and analysis:\n\n- Anchor Text Suggestion: Acts as a mechanism during internal link optimization, suggesting potential anchor texts to web authors.\n- Evaluation of Existing Links: Assesses the naturalness of link placements within existing content, aiding in the refinement of web pages.\n- Link Placement Guide: Offers guidance to link builders by suggesting optimal placement for links within content.\n- Anchor Text Idea Generator: Provides creative anchor text suggestions to enrich content and improve SEO strategies.\n- Spam and Inorganic SEO Detection: Helps identify unnatural link patterns, contributing to the detection of spam and inorganic SEO tactics.", "passage: ## Training and Performance\n\nLinkBERT was fine-tuned on a dataset of organic web content and editorial links. The training involved preprocessing web content, annotating links with temporary markup for clear distinction, and employing a specialized tokenization process to prepare the data for model training. In adition to commonly available data sources such as Wikipedia, additional training data was also sourced from:\n\n- Owayo USA\n- Owayo Germany\n- Compare the Market\n- Leonardo AI\n- The Wests Group\n- Zendesk\n- Australia Post\n- Probe Group\n- Monarch Institute\n- And many others...### Training Highlights:\n\n- Dataset: Custom organic web content with editorial links.\n- Preprocessing: Links annotated with '[START_LINK]' and '[END_LINK]' markup.\n- Tokenization: Utilized input_ids, token_type_ids, attention_mask, and labels for model training, with a unique labeling system to differentiate between link/anchor text and plain text.### Technical Specifications:\n\n- Batch Size: 10, with class weights adjusted to address class imbalance between link and plain text.\n- Optimizer: AdamW with a learning rate of 5e-5.\n- Epochs: 5, incorporating gradient accumulation and warmup steps to optimize training outcomes.\n- Hardware: 1 x RTX4090 24GB VRAM\n- Duration: 32 hours## Utilization and Integration\n\nLinkBERT is positioned as a powerful tool for content creators, SEO specialists, and webmasters, offering unparalleled support in optimizing web content for both user engagement and search engine recognition. Its predictive capabilities not only streamline the content creation process but also offer insights into the natural integration of links, enhancing the overall quality and relevance of web content.## Accessibility\n\nLinkBERT leverages the robust architecture of bert-large-cased, enhancing it with capabilities specifically tailored for web content analysis. This model represents a significant advancement in the understanding and generation of web content, providing a nuanced approach to natural link prediction and anchor text suggestion.\n\n---# BERT large model (cased)\n\nPretrained model on English language using a masked language modeling (MLM) objective. It was introduced in\nthis paper and first released in\nthis repository. This model is cased: it makes a difference\nbetween english and English.\n\nDisclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by\nthe Hugging Face team." ]	[ -0.06433500349521637, 0.09201404452323914, -0.0019514831947162747, 0.018913736566901207, 0.06191550940275192, -0.03604750707745552, 0.04942135512828827, 0.040816426277160645, 0.0159296914935112, 0.05344151705503464, -0.07544127106666565, -0.06016024947166443, 0.07674199342727661, 0.06464492529630661, 0.052351269870996475, -0.21509864926338196, 0.04105275869369507, -0.016897859051823616, -0.008523978292942047, 0.07715651392936707, 0.0934496521949768, -0.08208920806646347, 0.07215403765439987, 0.07656539976596832, 0.043317340314388275, 0.0034991586580872536, -0.05912300944328308, -0.06534554809331894, 0.07477951049804688, 0.028254413977265358, 0.07458983361721039, -0.009608075022697449, 0.046313636004924774, -0.19420969486236572, 0.012918040156364441, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Bioformer-LitCovid-v1.3h This model is a fine-tuned version of [bioformers/bioformer-litcovid](https://huggingface.co/bioformers/bioformer-litcovid) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8951 - Hamming loss: 0.0168 - F1 micro: 0.8565 - F1 macro: 0.3960 - F1 weighted: 0.8831 - F1 samples: 0.8789 - Precision micro: 0.7903 - Precision macro: 0.3221 - Precision weighted: 0.8426 - Precision samples: 0.8631 - Recall micro: 0.9348 - Recall macro: 0.6915 - Recall weighted: 0.9348 - Recall samples: 0.9435 - Roc Auc: 0.9604 - Accuracy: 0.6896 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 3257 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Hamming loss \| F1 micro \| F1 macro \| F1 weighted \| F1 samples \| Precision micro \| Precision macro \| Precision weighted \| Precision samples \| Recall micro \| Recall macro \| Recall weighted \| Recall samples \| Roc Auc \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------------:\|:--------:\|:--------:\|:-----------:\|:----------:\|:---------------:\|:---------------:\|:------------------:\|:-----------------:\|:------------:\|:------------:\|:---------------:\|:--------------:\|:-------:\|:--------:\| \| 1.2033 \| 1.0 \| 2272 \| 0.5628 \| 0.0616 \| 0.6107 \| 0.2167 \| 0.7918 \| 0.7257 \| 0.4618 \| 0.1789 \| 0.7347 \| 0.6771 \| 0.9014 \| 0.7310 \| 0.9014 \| 0.9194 \| 0.9209 \| 0.3870 \| \| 1.2127 \| 2.0 \| 4544 \| 0.5062 \| 0.0325 \| 0.7555 \| 0.2834 \| 0.8357 \| 0.8037 \| 0.6337 \| 0.2273 \| 0.7680 \| 0.7535 \| 0.9353 \| 0.7100 \| 0.9353 \| 0.9434 \| 0.9523 \| 0.4954 \| \| 0.96 \| 3.0 \| 6816 \| 0.4943 \| 0.0245 \| 0.8043 \| 0.3363 \| 0.8608 \| 0.8409 \| 0.7043 \| 0.2676 \| 0.8069 \| 0.8048 \| 0.9372 \| 0.7637 \| 0.9372 \| 0.9477 \| 0.9575 \| 0.5735 \| \| 0.5852 \| 4.0 \| 9088 \| 0.7306 \| 0.0195 \| 0.8371 \| 0.3860 \| 0.8687 \| 0.8624 \| 0.7568 \| 0.3083 \| 0.8212 \| 0.8378 \| 0.9365 \| 0.7232 \| 0.9365 \| 0.9459 \| 0.9597 \| 0.6410 \| \| 0.3454 \| 5.0 \| 11360 \| 0.8951 \| 0.0168 \| 0.8565 \| 0.3960 \| 0.8831 \| 0.8789 \| 0.7903 \| 0.3221 \| 0.8426 \| 0.8631 \| 0.9348 \| 0.6915 \| 0.9348 \| 0.9435 \| 0.9604 \| 0.6896 \| ### Framework versions - Transformers 4.28.0 - Pytorch 2.0.0 - Datasets 2.1.0 - Tokenizers 0.13.3	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Bioformer-LitCovid-v1.3h", "results": []}]}	text-classification	sofia-todeschini/Bioformer-LitCovid-v1.3h	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T13:18:17+00:00	[]	[]	TAGS #transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	Bioformer-LitCovid-v1.3h ======================== This model is a fine-tuned version of bioformers/bioformer-litcovid on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.8951 * Hamming loss: 0.0168 * F1 micro: 0.8565 * F1 macro: 0.3960 * F1 weighted: 0.8831 * F1 samples: 0.8789 * Precision micro: 0.7903 * Precision macro: 0.3221 * Precision weighted: 0.8426 * Precision samples: 0.8631 * Recall micro: 0.9348 * Recall macro: 0.6915 * Recall weighted: 0.9348 * Recall samples: 0.9435 * Roc Auc: 0.9604 * Accuracy: 0.6896 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: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 3257 * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.28.0 * Pytorch 2.0.0 * Datasets 2.1.0 * Tokenizers 0.13.3	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 3257\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.13.3" ]	[ "TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 3257\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.13.3" ]	[ 55, 116, 4, 32 ]	[ "passage: TAGS\n#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 3257\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.28.0\n* Pytorch 2.0.0\n* Datasets 2.1.0\n* Tokenizers 0.13.3" ]	[ -0.09670833498239517, 0.08500991761684418, 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null	null	transformers	<div align="center"> <h1> MiniCPM </h1> </div> <p align="center"> <a href="https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4" target="_blank">MiniCPM 技术报告</a><a href="https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4" target="_blank"> Technical Report</a> \| <a href="https://github.com/OpenBMB/OmniLMM/" target="_blank">OmniLMM 多模态模型 Multi-modal Model</a> \| <a href="https://luca.cn/" target="_blank">CPM-C 千亿模型试用 ~100B Model Trial </a> </p> MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 - 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 - 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 - 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 - 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 - 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 - 基于MiniCPM-2B的指令微调与人类偏好对MiniCPM-2B-SFT/DPO。 - 基于MiniCPM-2B的多模态模型MiniCPM-V，能力超越基于Phi-2的同参数级别多模态模型。 - MiniCPM-2B-SFT/DPO的Int4量化版MiniCPM-2B-SFT/DPO-Int4。 - 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，文本及多模态模型均可在手机端进行推理。 MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. - MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. - After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. - MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. - MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. - The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. - SFT and DPO version based on MiniCPM-2B and human preference: MiniCPM-2B-SFT/DPO - The multi-modal model MiniCPM-V based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 - The INT4 quantized version MiniCPM-2B-SFT/DPO-Int4 based on MiniCPM-2B-SFT/DPO - Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于[github仓库](https://github.com/OpenBMB/MiniCPM?tab=readme-ov-file#%E8%AF%84%E6%B5%8B%E7%BB%93%E6%9E%9C) Detailed evaluation results are in [github repo](https://github.com/OpenBMB/MiniCPM/blob/main/README-en.md#evaluation-results) 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations - 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； - 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； - 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； - 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 - Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. - To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. - Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. - Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. ## 模型下载 Download \| HuggingFace \| ModelScope \| WiseModel \| \|-------------\|------------\|-----------\| \|[sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16)\|[sft-bf16](https://modelscope.cn/models/OpenBMB/miniCPM-bf16)\|[sft-bf16](https://wisemodel.cn/models/OpenBMB/miniCPM-bf16) \|[sft-fp32](https://huggingface.co/openbmb/MiniCPM-2B-sft-fp32)\|[sft-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-sft-fp32)\|[sft-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) \|[dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16)\|[dpo-bf16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16/summary)\|[dpo-bf16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-bf16) \|[dpo-fp16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp16)\|[dpo-fp16](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16/)\|[dpo-fp16](https://wisemodel.cn/models/OpenBMB/MiniCPM-2B-dpo-fp16) \|[dpo-fp32](https://huggingface.co/openbmb/MiniCPM-2B-dpo-fp32)\|[dpo-fp32](https://modelscope.cn/models/OpenBMB/MiniCPM-2B-dpo-fp32)\|[dpo-fp32](https://wisemodel.cn/models/OpenBMB/miniCPM-dpo-fp32) ## 模型使用 Usage * 安装`transformers>=4.36.0`以及`accelerate`后，运行以下代码 * 注意：需要在`from_pretrained`中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install `transformers>=4.36.0` and `accelerate` * Warning: It is necessary to specify the data type of the model clearly in 'from_pretrained', otherwise large calculation errors will be caused ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch torch.manual_seed(0) path = 'openbmb/MiniCPM-2B-dpo-bf16' tokenizer = AutoTokenizer.from_pretrained(path) model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True) responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.8, top_p=0.8) print(responds) ``` * 期望输出 Expected Output ```shell 山东省最高的山是泰山，海拔1545米。相对于黄山（海拔1864米），泰山海拔较低，相差约319米。 ``` ## 开源协议 LICENSE #### 模型协议 Model LICENSE * 本仓库中代码依照 [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) 协议开源 * MiniCPM 模型权重的使用则需要遵循 [“通用模型许可协议-来源说明-宣传限制-商业授权”](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md)。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. * The usage of MiniCPM model weights must strictly follow [the General Model License (GML)](https://github.com/OpenBMB/General-Model-License/blob/main/%E9%80%9A%E7%94%A8%E6%A8%A1%E5%9E%8B%E8%AE%B8%E5%8F%AF%E5%8D%8F%E8%AE%AE-%E6%9D%A5%E6%BA%90%E8%AF%B4%E6%98%8E-%E5%AE%A3%E4%BC%A0%E9%99%90%E5%88%B6-%E5%95%86%E4%B8%9A%E6%8E%88%E6%9D%83.md). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to [email protected] to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. <p id="8"></p> ## 工作引用 Citation * 如果觉得MiniCPM有助于您的工作，请考虑引用下列[技术报告](https://shengdinghu.notion.site/MiniCPM-c805a17c5c8046398914e47f0542095a?pvs=4) * Please cite our [techinical report](https://shengdinghu.notion.site/MiniCPM-Unveiling-the-Potential-of-End-side-Large-Language-Models-d4d3a8c426424654a4e80e42a711cb20?pvs=4) if you find our work valuable. ``` @inproceedings{minicpm2024, title={MiniCPM：Unveiling the Potential of End-side Large Language Models}, booktitle={OpenBMB Blog}, year={2024} } ```	{"language": ["en", "zh"], "tags": ["MiniCPM", "ModelBest", "THUNLP"]}	null	jncraton/MiniCPM-2B-dpo-bf16-llama-format-ct2-int8	[ "transformers", "MiniCPM", "ModelBest", "THUNLP", "en", "zh", "endpoints_compatible", "region:us" ]	2024-02-10T13:23:32+00:00	[]	[ "en", "zh" ]	TAGS #transformers #MiniCPM #ModelBest #THUNLP #en #zh #endpoints_compatible #region-us	MiniCPM ========= [MiniCPM 技术报告](URL target=) [Technical Report](URL target=) \| [OmniLMM 多模态模型 Multi-modal Model](URL target=) \| [CPM-C 千亿模型试用 ~100B Model Trial](URL target=) MiniCPM 是面壁与清华大学自然语言处理实验室共同开源的系列端侧语言大模型，主体语言模型 MiniCPM-2B 仅有 24亿（2.4B）的非词嵌入参数量。 * 经过 SFT 后，MiniCPM 在公开综合性评测集上，MiniCPM 与 Mistral-7B相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。 * 经过 DPO 后，MiniCPM 在当前最接近用户体感的评测集 MTBench上，MiniCPM-2B 也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。 * 以 MiniCPM-2B 为基础构建端侧多模态大模型 MiniCPM-V，整体性能在同规模模型中实现最佳，超越基于 Phi-2 构建的现有多模态大模型，在部分评测集上达到与 9.6B Qwen-VL-Chat 相当甚至更好的性能。 * 经过 Int4 量化后，MiniCPM 可在手机上进行部署推理，流式输出速度略高于人类说话速度。MiniCPM-V 也首次跑通了多模态大模型在手机上的部署。 * 一张1080/2080可高效参数微调，一张3090/4090可全参数微调，一台机器可持续训练 MiniCPM，二次开发成本较低。我们将完全开源MiniCPM-2B的模型参数供学术研究和有限商用，以及训练过程中的所有Checkpoint和大部分非专有数据供模型机理研究。 * 基于MiniCPM-2B的指令微调与人类偏好对MiniCPM-2B-SFT/DPO。 * 基于MiniCPM-2B的多模态模型MiniCPM-V，能力超越基于Phi-2的同参数级别多模态模型。 * MiniCPM-2B-SFT/DPO的Int4量化版MiniCPM-2B-SFT/DPO-Int4。 * 基于MLC-LLM、LLMFarm开发的MiniCPM手机端程序，文本及多模态模型均可在手机端进行推理。 MiniCPM is an End-Size LLM developed by ModelBest Inc. and TsinghuaNLP, with only 2.4B parameters excluding embeddings. * MiniCPM has very close performance compared with Mistral-7B on open-sourced general benchmarks with better ability on Chinese, Mathmetics and Coding after SFT. The overall performance exceeds Llama2-13B, MPT-30B, Falcon-40B, etc. * After DPO, MiniCPM outperforms Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, Zephyr-7B-alpha, etc. on MTBench. * MiniCPM-V, based on MiniCPM-2B, achieves the best overall performance among multimodel models of the same scale, surpassing existing multimodal large models built on Phi-2 and achieving performance comparable to or even better than 9.6B Qwen-VL-Chat on some tasks. * MiniCPM can be deployed and infer on smartphones, and the speed of streaming output is relatively higher than the verbal speed of human. MiniCPM-V is the first multi-modal models that can be deployed on smartphones. * The cost of developing based on MiniCPM is low. Parameter efficient finetuning can be conducted with a single 1080/2080 GPU and full parameter finetuning can be conducted with a 3090/4090 GPU. We release all model parameters for research and limited commercial use. We also release all the checkpoint during training and most public training data for research on model mechanism. * SFT and DPO version based on MiniCPM-2B and human preference: MiniCPM-2B-SFT/DPO * The multi-modal model MiniCPM-V based on MiniCPM-2B, which outperforms models with similar size, i.e., Phi-2 * The INT4 quantized version MiniCPM-2B-SFT/DPO-Int4 based on MiniCPM-2B-SFT/DPO * Mobile phone application based on MLC-LLM and LLMFarm. Both language model and multimodel model can conduct inference on smartphones. ### 评测结果 Evaluation Results 详细的评测结果位于github仓库 Detailed evaluation results are in github repo 注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。 Notice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended. We are investigating the cause now. ### 局限性 Limitations * 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进； * 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息； * 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果； * 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。 * Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model. * To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models. * Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts. * Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability. 模型下载 Download ------------- HuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16 HuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32 HuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16 HuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16 HuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32 模型使用 Usage ---------- * 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码 * 注意：需要在'from\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差 * Run the following code after install 'transformers>=4.36.0' and 'accelerate' * Warning: It is necessary to specify the data type of the model clearly in 'from\_pretrained', otherwise large calculation errors will be caused * 期望输出 Expected Output 开源协议 LICENSE ------------ #### 模型协议 Model LICENSE * 本仓库中代码依照 Apache-2.0 协议开源 * MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。 * MiniCPM 模型权重对学术研究完全开放。 * 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。 * This repository is released under the Apache-2.0 License. * The usage of MiniCPM model weights must strictly follow the General Model License (GML). * The models and weights of MiniCPM are completely free for academic research. * If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization. #### 声明 Statement * 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。 * 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。 * 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。 * As a language model, MiniCPM generates content by learning from a vast amount of text. * However, it does not possess the ability to comprehend or express personal opinions or value judgments. * Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers. * Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own. 工作引用 Citation ------------- * 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告 * Please cite our techinical report if you find our work valuable.	[ "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]	[ "TAGS\n#transformers #MiniCPM #ModelBest #THUNLP #en #zh #endpoints_compatible #region-us \n", "### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now.", "### 局限性 Limitations\n\n\n* 受限于模型规模，模型可能出现幻觉性问题。其中由于DPO模型生成的回复内容更长，更容易出现幻觉。我们也将持续进行MiniCPM模型的迭代改进；\n* 为了保证在学术研究用途上模型的通用性，我们未对模型进行任何身份认同训练。同时由于我们用ShareGPT开源语料作为部分训练数据，模型可能会输出类似GPT系列模型的身份认同信息；\n* 受限于模型规模，模型的输出受到提示词（prompt）的影响较大，可能多次尝试产生不一致的结果；\n* 受限于模型容量，模型的知识记忆较不准确，后续我们将结合RAG方法来增强模型的知识记忆能力。\n* Due to limitations in model size, the model may experience hallucinatory issues. As DPO model tend to generate longer response, hallucinations are more likely to occur. We will also continue to iterate and improve the MiniCPM model.\n* To ensure the universality of the model for academic research purposes, we did not conduct any identity training on the model. Meanwhile, as we use ShareGPT open-source corpus as part of the training data, the model may output identity information similar to the GPT series models.\n* Due to the limitation of model size, the output of the model is greatly influenced by prompt words, which may result in inconsistent results from multiple attempts.\n* Due to limited model capacity, the model's knowledge memory is not accurate. In the future, we will combine the RAG method to enhance the model's knowledge memory ability.\n\n\n模型下载 Download\n-------------\n\n\nHuggingFace: sft-bf16, ModelScope: sft-bf16, WiseModel: sft-bf16\nHuggingFace: sft-fp32, ModelScope: sft-fp32, WiseModel: sft-fp32\nHuggingFace: dpo-bf16, ModelScope: dpo-bf16, WiseModel: dpo-bf16\nHuggingFace: dpo-fp16, ModelScope: dpo-fp16, WiseModel: dpo-fp16\nHuggingFace: dpo-fp32, ModelScope: dpo-fp32, WiseModel: dpo-fp32\n\n\n模型使用 Usage\n----------\n\n\n* 安装'transformers>=4.36.0'以及'accelerate'后，运行以下代码\n* 注意：需要在'from\\_pretrained'中明确指明模型的数据类型，否则会引起较大计算误差\n* Run the following code after install 'transformers>=4.36.0' and 'accelerate'\n* Warning: It is necessary to specify the data type of the model clearly in 'from\\_pretrained', otherwise large calculation errors will be caused\n* 期望输出 Expected Output\n\n\n开源协议 LICENSE\n------------", "#### 模型协议 Model LICENSE\n\n\n* 本仓库中代码依照 Apache-2.0 协议开源\n* MiniCPM 模型权重的使用则需要遵循 “通用模型许可协议-来源说明-宣传限制-商业授权”。\n* MiniCPM 模型权重对学术研究完全开放。\n* 如需将模型用于商业用途，请联系[email protected]来获取书面授权，在登记后亦允许免费商业使用。\n* This repository is released under the Apache-2.0 License.\n* The usage of MiniCPM model weights must strictly follow the General Model License (GML).\n* The models and weights of MiniCPM are completely free for academic research.\n* If you intend to utilize the model for commercial purposes, please reach out to cpm@URL to obtain the certificate of authorization.", "#### 声明 Statement\n\n\n* 作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。\n* 因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。\n* 如果由于使用 MinCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。\n* As a language model, MiniCPM generates content by learning from a vast amount of text.\n* However, it does not possess the ability to comprehend or express personal opinions or value judgments.\n* Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.\n* Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.\n\n\n\n工作引用 Citation\n-------------\n\n\n* 如果觉得MiniCPM有助于您的工作，请考虑引用下列技术报告\n* Please cite our techinical report if you find our work valuable." ]	[ 32, 101, 655, 180, 262 ]	[ "passage: TAGS\n#transformers #MiniCPM #ModelBest #THUNLP #en #zh #endpoints_compatible #region-us \n### 评测结果 Evaluation Results\n\n\n详细的评测结果位于github仓库\n\n\nDetailed evaluation results are in github repo\n\n\n注意：我们发现使用Huggingface生成质量略差于vLLM，因此推荐使用vLLM进行测试。我们正在排查原因。\n\n\nNotice: We discovered that the quality of Huggingface generation is slightly lower than vLLM, thus benchmarking using vLLM is recommended.\nWe are investigating the cause now." ]	[ -0.007029905449599028, -0.18129052221775055, -0.005951940082013607, 0.035357579588890076, 0.09986112266778946, -0.0005699562607333064, 0.046559251844882965, 0.0894036665558815, -0.0011693296255543828, 0.014035903848707676, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	aidonuts/marigold-006-ep4	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T13:28:47+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	jeevana/GenAI_QnA_Mistral7b_QLoRA_G8_FV04	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T13:30:01+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # CaseSummarization_4.0 This model is a fine-tuned version of [TheBloke/zephyr-7B-beta-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-beta-GPTQ) 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: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.2 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "TheBloke/zephyr-7B-beta-GPTQ", "model-index": [{"name": "CaseSummarization_4.0", "results": []}]}	null	Bhagya17/CaseSummarization_4.0	[ "peft", "safetensors", "mistral", "trl", "sft", "generated_from_trainer", "base_model:TheBloke/zephyr-7B-beta-GPTQ", "license:mit", "4-bit", "region:us" ]	2024-02-10T13:32:08+00:00	[]	[]	TAGS #peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us	# CaseSummarization_4.0 This model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ 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: 250 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.37.2 - Pytorch 2.1.2 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# CaseSummarization_4.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us \n", "# CaseSummarization_4.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 57, 39, 6, 12, 8, 3, 102, 4, 36 ]	[ "passage: TAGS\n#peft #safetensors #mistral #trl #sft #generated_from_trainer #base_model-TheBloke/zephyr-7B-beta-GPTQ #license-mit #4-bit #region-us \n# CaseSummarization_4.0\n\nThis model is a fine-tuned version of TheBloke/zephyr-7B-beta-GPTQ on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0002\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- training_steps: 250\n- mixed_precision_training: Native AMP### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.37.2\n- Pytorch 2.1.2\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ -0.10381967574357986, 0.04290097951889038, -0.0022309201303869486, 0.07671438157558441, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text2text-generation	Professor/double-quant-davlan	[ "transformers", "safetensors", "mt5", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "4-bit", "region:us" ]	2024-02-10T13:37:31+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 62, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mt5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04424791410565376, 0.17212572693824768, -0.005364083219319582, 0.017688874155282974, 0.10341756790876389, 0.00779188284650445, 0.05365653708577156, 0.11789484322071075, -0.04384218528866768, 0.11921608448028564, 0.043076321482658386, 0.10880826413631439, 0.11680098623037338, 0.15051773190498352, 0.002164856530725956, -0.21935151517391205, 0.04954215884208679, -0.11219757050275803, -0.025547046214342117, 0.12055613845586777, 0.1482943594455719, -0.0999172106385231, 0.07405153661966324, -0.03270019218325615, -0.021699804812669754, -0.03417343273758888, -0.06192147731781006, -0.04243922606110573, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"license": "apache-2.0", "library_name": "transformers", "datasets": ["jondurbin/truthy-dpo-v0.1"]}	text-generation	vicgalle/zephyr-7b-truthy	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "dataset:jondurbin/truthy-dpo-v0.1", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T13:48:48+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 84, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #conversational #dataset-jondurbin/truthy-dpo-v0.1 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]" ]	[ -0.07881546765565872, 0.19828130304813385, -0.003788086585700512, 0.02594563364982605, 0.10099275410175323, -0.004296048078685999, 0.0479087196290493, 0.1277962476015091, 0.00588206248357892, 0.13631753623485565, 0.035890862345695496, 0.15719355642795563, 0.10844240337610245, 0.1793917864561081, 0.007658287417143583, -0.15868471562862396, 0.0245195385068655, -0.10021348297595978, 0.024458106607198715, 0.11435500532388687, 0.12693296372890472, -0.10097949206829071, 0.06654553860425949, -0.04340309277176857, 0.005332469940185547, -0.05228358507156372, -0.07581142336130142, -0.03051694668829441, 0.04182282090187073, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "257.25 +/- 21.93", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	Benfnash/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-10T13:50:29+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	null	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction	{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "PixelCopter-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "16.70 +/- 12.17", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	leejeewoo/PixelCopter-v1	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	2024-02-10T13:51:01+00:00	[]	[]	TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us	# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL	[ "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ "TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n", "# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 41, 58 ]	[ "passage: TAGS\n#Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us \n# Reinforce Agent playing Pixelcopter-PLE-v0\n This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 .\n To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL" ]	[ 0.0073175891302526, -0.2259262204170227, -0.0017347558168694377, 0.05054566636681557, 0.0658537745475769, -0.055378563702106476, 0.1412602812051773, 0.05916554853320122, -0.04990595206618309, 0.059261854737997055, 0.14166708290576935, 0.03996060788631439, 0.022112762555480003, 0.1513713151216507, 0.09764605015516281, -0.2469022423028946, 0.07438477873802185, 0.01641594059765339, 0.008152224123477936, 0.09583204984664917, 0.060265738517045975, -0.1405058205127716, 0.037032704800367355, -0.01332044042646885, -0.13650871813297272, 0.0010478810872882605, -0.021802188828587532, -0.03625129908323288, 0.15681709349155426, 0.006844013463705778, 0.09602472931146622, -0.001560068572871387, 0.06475798785686493, -0.12438877671957016, 0.05466329678893089, 0.06455880403518677, -0.06293967366218567, 0.058029334992170334, -0.057374246418476105, 0.11959903687238693, 0.04641333222389221, -0.01578129455447197, 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null	null	transformers	## Exllama v2 Quantizations of Hercules-2.0-Mistral-7B Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.13">turboderp's ExLlamaV2 v0.0.13</a> for quantization. <b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b> Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions. Original model: https://huggingface.co/Locutusque/Hercules-2.0-Mistral-7B \| Branch \| Bits \| lm_head bits \| VRAM (4k) \| VRAM (16k) \| VRAM (32k) \| Description \| \| ----- \| ---- \| ------- \| ------ \| ------ \| ------ \| ------------ \| \| [8_0](https://huggingface.co/bartowski/Hercules-2.0-Mistral-7B-exl2/tree/8_0) \| 8.0 \| 8.0 \| 8.4 GB \| 9.8 GB \| 11.8 GB \| Maximum quality that ExLlamaV2 can produce, near unquantized performance. \| \| [6_5](https://huggingface.co/bartowski/Hercules-2.0-Mistral-7B-exl2/tree/6_5) \| 6.5 \| 8.0 \| 7.2 GB \| 8.6 GB \| 10.6 GB \| Very similar to 8.0, good tradeoff of size vs performance, recommended. \| \| [5_0](https://huggingface.co/bartowski/Hercules-2.0-Mistral-7B-exl2/tree/5_0) \| 5.0 \| 6.0 \| 6.0 GB \| 7.4 GB \| 9.4 GB \| Slightly lower quality vs 6.5, but usable on 8GB cards. \| \| [4_25](https://huggingface.co/bartowski/Hercules-2.0-Mistral-7B-exl2/tree/4_25) \| 4.25 \| 6.0 \| 5.3 GB \| 6.7 GB \| 8.7 GB \| GPTQ equivalent bits per weight, slightly higher quality. \| \| [3_5](https://huggingface.co/bartowski/Hercules-2.0-Mistral-7B-exl2/tree/3_5) \| 3.5 \| 6.0 \| 4.7 GB \| 6.1 GB \| 8.1 GB \| Lower quality, only use if you have to. \| ## Download instructions With git: ```shell git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Hercules-2.0-Mistral-7B-exl2 Hercules-2.0-Mistral-7B-exl2-6_5 ``` With huggingface hub (credit to TheBloke for instructions): ```shell pip3 install huggingface-hub ``` To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Hercules-2.0-Mistral-7B-exl2`: ```shell mkdir Hercules-2.0-Mistral-7B-exl2 huggingface-cli download bartowski/Hercules-2.0-Mistral-7B-exl2 --local-dir Hercules-2.0-Mistral-7B-exl2 --local-dir-use-symlinks False ``` To download from a different branch, add the `--revision` parameter: Linux: ```shell mkdir Hercules-2.0-Mistral-7B-exl2-6_5 huggingface-cli download bartowski/Hercules-2.0-Mistral-7B-exl2 --revision 6_5 --local-dir Hercules-2.0-Mistral-7B-exl2-6_5 --local-dir-use-symlinks False ``` Windows (which apparently doesn't like _ in folders sometimes?): ```shell mkdir Hercules-2.0-Mistral-7B-exl2-6.5 huggingface-cli download bartowski/Hercules-2.0-Mistral-7B-exl2 --revision 6_5 --local-dir Hercules-2.0-Mistral-7B-exl2-6.5 --local-dir-use-symlinks False ``` Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski	{"license": "apache-2.0", "library_name": "transformers", "tags": ["not-for-all-audiences", "chemistry", "math", "code", "physics"], "datasets": ["Locutusque/hercules-v2.0"], "widget": [{"example_title": "Derivative", "text": "<\|im_start\|>user\nIn this task, you are given a description of an object and its surroundings. Your task is to describe the motion of the object mentioned in the description using differential equations. The answer should be a mathematical expression in t. Description: A ball is thrown up at 30 ft/sec from 3 feet off the ground. Solve this step-by-step, explaining each step in detail.<\|im_end\|>\n<\|im_start\|>assistant\n", "output": {"text": "Let h(t) be the height of the ball at time t after it was thrown. We know that when the ball is thrown, it has an initial velocity v0 = 30 ft/sec and initial height y0 = 3 ft. We also know that the acceleration due to gravity g = -32 ft/sec^2.\n\nTo solve for the motion of the ball, we can use the equation for vertical motion with acceleration due to gravity:\n\nh(t) = v0 * t + (1/2) * g * t^2 + y0\n\nSubstituting the values given in the problem, we get:\n\ny(t) = (30)(t) + (1/2)(-32)(t)^2 + 3\n\nNow, let's simplify the equation:\n\ny(t) = 30t - 16t^2 + 3\n\nThis is the final solution for the height of the ball as a function of time."}}], "quantized_by": "bartowski", "pipeline_tag": "text-generation"}	text-generation	bartowski/Hercules-2.0-Mistral-7B-exl2	[ "transformers", "not-for-all-audiences", "chemistry", "math", "code", "physics", "text-generation", "dataset:Locutusque/hercules-v2.0", "license:apache-2.0", "endpoints_compatible", "region:us" ]	2024-02-10T14:00:39+00:00	[]	[]	TAGS #transformers #not-for-all-audiences #chemistry #math #code #physics #text-generation #dataset-Locutusque/hercules-v2.0 #license-apache-2.0 #endpoints_compatible #region-us	Exllama v2 Quantizations of Hercules-2.0-Mistral-7B --------------------------------------------------- Using <a href="URL ExLlamaV2 v0.0.13 for quantization. The "main" branch only contains the URL, download one of the other branches for the model (see below) Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions. Original model: URL Download instructions --------------------- With git: With huggingface hub (credit to TheBloke for instructions): To download the 'main' (only useful if you only care about URL) branch to a folder called 'Hercules-2.0-Mistral-7B-exl2': To download from a different branch, add the '--revision' parameter: Linux: Windows (which apparently doesn't like \_ in folders sometimes?): Want to support my work? 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Donut_trainthai This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cpu - Datasets 2.16.1 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "Donut_trainthai", "results": []}]}	null	suthawadee/Donut_trainthai	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	2024-02-10T14:03:54+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us	# Donut_trainthai This model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cpu - Datasets 2.16.1 - Tokenizers 0.15.1	[ "# Donut_trainthai\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us \n", "# Donut_trainthai\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ 70, 37, 6, 12, 8, 3, 90, 4, 38 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #vision-encoder-decoder #generated_from_trainer #dataset-imagefolder #base_model-naver-clova-ix/donut-base #license-mit #endpoints_compatible #region-us \n# Donut_trainthai\n\nThis model is a fine-tuned version of naver-clova-ix/donut-base on the imagefolder dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 2\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cpu\n- Datasets 2.16.1\n- Tokenizers 0.15.1" ]	[ -0.0816536471247673, 0.12812598049640656, -0.0009826552122831345, 0.09274964779615402, 0.14790134131908417, 0.005476665683090687, 0.06247246637940407, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xmlRoberta_GenData_Double This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "xlm-roberta-base", "model-index": [{"name": "xmlRoberta_GenData_Double", "results": []}]}	text-classification	Constien/xmlRoberta_GenData_Double	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "text-classification", "generated_from_trainer", "base_model:xlm-roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T14:04:54+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us	# xmlRoberta_GenData_Double This model is a fine-tuned version of xlm-roberta-base on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# xmlRoberta_GenData_Double\n\nThis model is a fine-tuned version of xlm-roberta-base on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "# xmlRoberta_GenData_Double\n\nThis model is a fine-tuned version of xlm-roberta-base on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 69, 37, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #xlm-roberta #text-classification #generated_from_trainer #base_model-xlm-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n# xmlRoberta_GenData_Double\n\nThis model is a fine-tuned version of xlm-roberta-base on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-05\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.37.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ -0.08356776088476181, 0.1131780669093132, -0.0020424595568329096, 0.08151137828826904, 0.16415734589099884, 0.01782882772386074, 0.1428142786026001, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-large-squad-model2 This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 92 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "t5-large", "model-index": [{"name": "t5-large-squad-model2", "results": []}]}	question-answering	varun-v-rao/t5-large-squad-model2	[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:t5-large", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T14:06:04+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us	# t5-large-squad-model2 This model is a fine-tuned version of t5-large on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 92 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "# t5-large-squad-model2\n\nThis model is a fine-tuned version of t5-large on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 92\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "# t5-large-squad-model2\n\nThis model is a fine-tuned version of t5-large on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 92\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ 81, 32, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n# t5-large-squad-model2\n\nThis model is a fine-tuned version of t5-large on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 92\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ -0.0839281976222992, 0.1261083334684372, -0.003062264062464237, 0.09963858872652054, 0.13135892152786255, 0.012065015733242035, 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "265.96 +/- 21.30", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	arekpaterak/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-10T14:06:58+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	automatic-speech-recognition	Imran1/ckb	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T14:07:04+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 45, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05983767658472061, 0.15663617849349976, -0.00414510490372777, 0.012621625326573849, 0.10675175487995148, 0.00396517850458622, 0.07058298587799072, 0.10818448662757874, -0.014333043247461319, 0.1301925629377365, 0.031459614634513855, 0.10620059072971344, 0.11486424505710602, 0.17755427956581116, -0.00021593451674561948, -0.21627318859100342, 0.06542544066905975, -0.11467250436544418, 0.023902224376797676, 0.1205042228102684, 0.14280648529529572, -0.10782013833522797, 0.0710505023598671, -0.02651231922209263, -0.014152529649436474, -0.030523719266057014, -0.05870387330651283, -0.06662651896476746, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "mistralai/Mistral-7B-v0.1"}	null	Devean/Lora-add-paramter	[ "peft", "tensorboard", "safetensors", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-v0.1", "region:us" ]	2024-02-10T14:10:13+00:00	[ "1910.09700" ]	[]	TAGS #peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 43, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-v0.1 #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.11710534989833832, 0.2021108716726303, -0.00305310171097517, 0.028846031054854393, 0.08059801161289215, 0.015004060231149197, 0.05897754058241844, 0.1303057074546814, 0.031187374144792557, 0.12189128249883652, 0.07221290469169617, 0.12179826200008392, 0.11337313801050186, 0.21017156541347504, 0.0032703992910683155, -0.17037683725357056, 0.017329316586256027, -0.07105738669633865, 0.016411568969488144, 0.11954159289598465, 0.1406661421060562, -0.09835869073867798, 0.07564743608236313, -0.020650992169976234, 0.00018400292901787907, -0.029406584799289703, 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null	null	transformers	# Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.36.1 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login(<ACCESS_TOKEN>) ``` - Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="mwalol/json-deepseek-v2-1-1", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=0, max_new_tokens=1, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <\|user\|>Why is drinking water so healthy?<\|EOT\|><\|assistant\|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "mwalol/json-deepseek-v2-1-1", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "mwalol/json-deepseek-v2-1-1", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=0, max_new_tokens=1, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "mwalol/json-deepseek-v2-1-1" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<\|user\|>How are you?<\|EOT\|><\|assistant\|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=0, max_new_tokens=1, do_sample=False, num_beams=1, temperature=float(0.0), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32256, 2048, padding_idx=32014) (layers): ModuleList( (0-23): 24 x LlamaDecoderLayer( (self_attn): LlamaFlashAttention2( (q_proj): Linear(in_features=2048, out_features=2048, bias=False) (k_proj): Linear(in_features=2048, out_features=2048, bias=False) (v_proj): Linear(in_features=2048, out_features=2048, bias=False) (o_proj): Linear(in_features=2048, out_features=2048, bias=False) (rotary_emb): LlamaLinearScalingRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=2048, out_features=5504, bias=False) (up_proj): Linear(in_features=2048, out_features=5504, bias=False) (down_proj): Linear(in_features=5504, out_features=2048, bias=False) (act_fn): SiLU() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=2048, out_features=32256, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.	{"language": ["en"], "library_name": "transformers", "tags": ["gpt", "llm", "large language model", "h2o-llmstudio"], "inference": false, "thumbnail": "https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico"}	text-generation	mwalol/json-deepseek-v2-1-1	[ "transformers", "safetensors", "llama", "text-generation", "gpt", "llm", "large language model", "h2o-llmstudio", "conversational", "en", "autotrain_compatible", "text-generation-inference", "region:us" ]	2024-02-10T14:16:37+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #conversational #en #autotrain_compatible #text-generation-inference #region-us	# Model Card ## Summary This model was trained using H2O LLM Studio. - Base model: deepseek-ai/deepseek-coder-1.3b-instruct ## Usage To use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed. Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline' You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: Alternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'. You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ## Quantization and sharding You can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting . ## Model Architecture ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues. - Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes. By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.	[ "# Model Card", "## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: deepseek-ai/deepseek-coder-1.3b-instruct", "## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:", "## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .", "## Model Architecture", "## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.", "## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it." ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #conversational #en #autotrain_compatible #text-generation-inference #region-us \n", "# Model Card", "## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: deepseek-ai/deepseek-coder-1.3b-instruct", "## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:", "## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .", "## Model Architecture", "## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. Visit H2O LLM Studio to learn how to train your own large language models.", "## Disclaimer\n\nPlease read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.\n\n- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.\n- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.\n- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.\n- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.\n- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.\n- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.\n\nBy using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it." ]	[ 64, 3, 38, 244, 34, 4, 42, 518 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #gpt #llm #large language model #h2o-llmstudio #conversational #en #autotrain_compatible #text-generation-inference #region-us \n# Model Card## Summary\n\nThis model was trained using H2O LLM Studio.\n- Base model: deepseek-ai/deepseek-coder-1.3b-instruct## Usage\n\nTo use the model with the 'transformers' library on a machine with GPUs, first make sure you have the 'transformers' library installed.\n\n\n\nAlso make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.\n - Either leave 'token=True' in the 'pipeline' and login to hugginface_hub by running\n \n - Or directly pass your <ACCESS_TOKEN> to 'token' in the 'pipeline'\n\n\n\nYou can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:\n\n\n\n\n\nAlternatively, you can download h2oai_pipeline.py, store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the 'transformers' package, this will allow you to set 'trust_remote_code=False'.\n\n\n\n\nYou may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:## Quantization and sharding\n\nYou can load the models using quantization by specifying or . Also, sharding on multiple GPUs is possible by setting .## Model Architecture## Model Configuration\n\nThis model was trained using H2O LLM Studio and with the configuration in URL. 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null	null	peft	## Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	{"language": ["en"], "library_name": "peft", "tags": ["facebook", "meta", "pytorch", "llama", "llama-2", "text generation", "hosted inference"], "inference": true, "pipeline_tag": "text-generation"}	text-generation	Shri2818/codellama_python_v1	[ "peft", "facebook", "meta", "pytorch", "llama", "llama-2", "text generation", "hosted inference", "text-generation", "en", "region:us" ]	2024-02-10T14:19:46+00:00	[]	[ "en" ]	TAGS #peft #facebook #meta #pytorch #llama #llama-2 #text generation #hosted inference #text-generation #en #region-us	## Training procedure The following 'bitsandbytes' quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0	[ "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ "TAGS\n#peft #facebook #meta #pytorch #llama #llama-2 #text generation #hosted inference #text-generation #en #region-us \n", "## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16", "### Framework versions\n\n\n- PEFT 0.4.0" ]	[ 39, 154, 11 ]	[ "passage: TAGS\n#peft #facebook #meta #pytorch #llama #llama-2 #text generation #hosted inference #text-generation #en #region-us \n## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: False\n- bnb_4bit_compute_dtype: float16### Framework versions\n\n\n- PEFT 0.4.0" ]	[ -0.06313753128051758, 0.031422924250364304, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # SpeechT5 TTS Dutch This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the VoxPopuli dataset. It achieves the following results on the evaluation set: - Loss: 0.4555 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5202 \| 4.3 \| 1000 \| 0.4754 \| \| 0.4983 \| 8.6 \| 2000 \| 0.4625 \| \| 0.4972 \| 12.9 \| 3000 \| 0.4574 \| \| 0.4953 \| 17.2 \| 4000 \| 0.4555 \| ### Framework versions - Transformers 4.38.0.dev0 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"language": ["nl"], "license": "mit", "tags": ["generated_from_trainer"], "datasets": ["facebook/voxpopuli"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "SpeechT5 TTS Dutch", "results": []}]}	text-to-audio	IoanRazvan/speecht5_tts_voxpopuli_nl	[ "transformers", "tensorboard", "safetensors", "speecht5", "text-to-audio", "generated_from_trainer", "nl", "dataset:facebook/voxpopuli", "base_model:microsoft/speecht5_tts", "license:mit", "endpoints_compatible", "region:us" ]	2024-02-10T14:23:01+00:00	[]	[ "nl" ]	TAGS #transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #nl #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us	SpeechT5 TTS Dutch ================== This model is a fine-tuned version of microsoft/speecht5\_tts on the VoxPopuli dataset. It achieves the following results on the evaluation set: * Loss: 0.4555 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 2 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 4000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.38.0.dev0 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #nl #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 76, 158, 4, 38 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #speecht5 #text-to-audio #generated_from_trainer #nl #dataset-facebook/voxpopuli #base_model-microsoft/speecht5_tts #license-mit #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 500\n* training\\_steps: 4000\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.38.0.dev0\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.1327894628047943, 0.09192629903554916, -0.003287276718765497, 0.05167907848954201, 0.09680274873971939, 0.007510046940296888, 0.09464535862207413, 0.1500614732503891, -0.08357959240674973, 0.10749813169240952, 0.09118273109197617, 0.05934196710586548, 0.06848747283220291, 0.2084183245897293, -0.030840812250971794, -0.2953454852104187, 0.015554244630038738, -0.014800330623984337, -0.1332014948129654, 0.11526311933994293, 0.08815094828605652, -0.10578068345785141, 0.03319820016622543, -0.011071419343352318, -0.10036339610815048, -0.012892615981400013, -0.02126016467809677, -0.048483263701200485, 0.10495194047689438, 0.045359812676906586, 0.06564116477966309, 0.061032164841890335, 0.07873726636171341, -0.2512328028678894, 0.016572697088122368, 0.0768074095249176, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "bert-base-cased", "model-index": [{"name": "bert-finetuned-ner", "results": []}]}	token-classification	yacineai7/bert-finetuned-ner	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:bert-base-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T14:35:22+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	# bert-finetuned-ner This model is a fine-tuned version of bert-base-cased on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	[ "# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ 68, 34, 6, 12, 8, 3, 90, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n# bert-finetuned-ner\n\nThis model is a fine-tuned version of bert-base-cased on an unknown dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.0+cu121\n- Datasets 2.17.0\n- Tokenizers 0.15.1" ]	[ -0.06356771290302277, 0.08390295505523682, -0.0011577598052099347, 0.0765465721487999, 0.17440402507781982, 0.008180545642971992, 0.13111911714076996, 0.07114522904157639, -0.10896750539541245, 0.04999789223074913, 0.05961913987994194, 0.07230284810066223, 0.015765869989991188, 0.10420171171426773, -0.05041636526584625, -0.22851787507534027, 0.02167096920311451, 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null	null	null	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="basab1142/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-FrozenLake-v1-4x4-noSlippery", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "FrozenLake-v1-4x4-no_slippery", "type": "FrozenLake-v1-4x4-no_slippery"}, "metrics": [{"type": "mean_reward", "value": "1.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	basab1142/q-FrozenLake-v1-4x4-noSlippery	[ "FrozenLake-v1-4x4-no_slippery", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T14:35:38+00:00	[]	[]	TAGS #FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage	[ "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ "TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 40, 39 ]	[ "passage: TAGS\n#FrozenLake-v1-4x4-no_slippery #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 FrozenLake-v1\n This is a trained model of a Q-Learning agent playing FrozenLake-v1 .\n\n ## Usage" ]	[ 0.04578453302383423, 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null	null	null	<br> # Vim Model Card ## Model Details Vision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks. - Developed by: [HUST](https://english.hust.edu.cn/), [Horizon Robotics](https://en.horizon.cc/), [BAAI](https://www.baai.ac.cn/english.html) - Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture. - License: Non-commercial license ### Model Sources - Repository: https://github.com/hustvl/Vim - Paper: https://arxiv.org/abs/2401.09417 ## Uses The primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone. The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence. ## How to Get Started with the Model - You can replace the backbone for vision tasks with the proposed Vim: https://github.com/hustvl/Vim/blob/main/vim/models_mamba.py - Then you can load this checkpoint and start training. ## Training Details Vim is pretrained on ImageNet-1K with classification supervision. The training data is around 1.3M images from [ImageNet-1K dataset](https://www.image-net.org/challenges/LSVRC/2012/). See more details in this [paper](https://arxiv.org/abs/2401.09417). ## Evaluation Vim-small is evaluated on ImageNet-1K val set, and achieves 80.5% Top-1 Acc. By further finetuning at finer granularity, Vim-small achieves 81.6% Top-1 Acc. See more details in this [paper](https://arxiv.org/abs/2401.09417). ## Additional Information ### Citation Information ``` @article{vim, title={Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model}, author={Lianghui Zhu and Bencheng Liao and Qian Zhang and Xinlong Wang and Wenyu Liu and Xinggang Wang}, journal={arXiv preprint arXiv:2401.09417}, year={2024} } ```	{"license": "apache-2.0"}	null	hustvl/Vim-small-midclstok	[ "arxiv:2401.09417", "license:apache-2.0", "region:us" ]	2024-02-10T14:36:20+00:00	[ "2401.09417" ]	[]	TAGS #arxiv-2401.09417 #license-apache-2.0 #region-us	<br> # Vim Model Card ## Model Details Vision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks. - Developed by: HUST, Horizon Robotics, BAAI - Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture. - License: Non-commercial license ### Model Sources - Repository: URL - Paper: URL ## Uses The primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone. The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence. ## How to Get Started with the Model - You can replace the backbone for vision tasks with the proposed Vim: URL - Then you can load this checkpoint and start training. ## Training Details Vim is pretrained on ImageNet-1K with classification supervision. The training data is around 1.3M images from ImageNet-1K dataset. See more details in this paper. ## Evaluation Vim-small is evaluated on ImageNet-1K val set, and achieves 80.5% Top-1 Acc. By further finetuning at finer granularity, Vim-small achieves 81.6% Top-1 Acc. See more details in this paper. ## Additional Information	[ "# Vim Model Card", "## Model Details\n\nVision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.\n\n- Developed by: HUST, Horizon Robotics, BAAI\n- Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture.\n- License: Non-commercial license", "### Model Sources\n\n- Repository: URL\n- Paper: URL", "## Uses\n\nThe primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.\nThe primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.", "## How to Get Started with the Model\n\n- You can replace the backbone for vision tasks with the proposed Vim: URL\n- Then you can load this checkpoint and start training.", "## Training Details\n\nVim is pretrained on ImageNet-1K with classification supervision.\nThe training data is around 1.3M images from ImageNet-1K dataset.\nSee more details in this paper.", "## Evaluation\n\nVim-small is evaluated on ImageNet-1K val set, and achieves 80.5% Top-1 Acc. By further finetuning at finer granularity, Vim-small achieves 81.6% Top-1 Acc. See more details in this paper.", "## Additional Information" ]	[ "TAGS\n#arxiv-2401.09417 #license-apache-2.0 #region-us \n", "# Vim Model Card", "## Model Details\n\nVision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.\n\n- Developed by: HUST, Horizon Robotics, BAAI\n- Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture.\n- License: Non-commercial license", "### Model Sources\n\n- Repository: URL\n- Paper: URL", "## Uses\n\nThe primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.\nThe primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.", "## How to Get Started with the Model\n\n- You can replace the backbone for vision tasks with the proposed Vim: URL\n- Then you can load this checkpoint and start training.", "## Training Details\n\nVim is pretrained on ImageNet-1K with classification supervision.\nThe training data is around 1.3M images from ImageNet-1K dataset.\nSee more details in this paper.", "## Evaluation\n\nVim-small is evaluated on ImageNet-1K val set, and achieves 80.5% Top-1 Acc. By further finetuning at finer granularity, Vim-small achieves 81.6% Top-1 Acc. See more details in this paper.", "## Additional Information" ]	[ 23, 5, 78, 15, 69, 40, 43, 62, 5 ]	[ "passage: TAGS\n#arxiv-2401.09417 #license-apache-2.0 #region-us \n# Vim Model Card## Model Details\n\nVision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.\n\n- Developed by: HUST, Horizon Robotics, BAAI\n- Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture.\n- License: Non-commercial license### Model Sources\n\n- Repository: URL\n- Paper: URL## Uses\n\nThe primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.\nThe primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.## How to Get Started with the Model\n\n- You can replace the backbone for vision tasks with the proposed Vim: URL\n- Then you can load this checkpoint and start training.## Training Details\n\nVim is pretrained on ImageNet-1K with classification supervision.\nThe training data is around 1.3M images from ImageNet-1K dataset.\nSee more details in this paper.## Evaluation\n\nVim-small is evaluated on ImageNet-1K val set, and achieves 80.5% Top-1 Acc. By further finetuning at finer granularity, Vim-small achieves 81.6% Top-1 Acc. See more details in this paper.## Additional Information" ]	[ -0.07090766727924347, 0.09033099561929703, -0.004465581849217415, 0.004966770764440298, 0.10628427565097809, 0.031193861737847328, 0.079409159719944, 0.08659134060144424, -0.023941799998283386, 0.028028422966599464, 0.04957452788949013, 0.024355659261345863, 0.10880967974662781, 0.1787625402212143, 0.08979351073503494, -0.19492805004119873, 0.009274208918213844, -0.03797217085957527, 0.03698456287384033, 0.04573714733123779, 0.12114256620407104, -0.09212882816791534, 0.04919460788369179, -0.009086066856980324, -0.12221816927194595, 0.02644210495054722, 0.0007874759030528367, 0.015253742225468159, 0.13671794533729553, -0.004851363133639097, 0.07688675075769424, -0.03616596758365631, 0.10078778862953186, -0.13560746610164642, 0.027216115966439247, 0.09440263360738754, 0.04614555090665817, 0.06188849359750748, 0.056635089218616486, 0.1331559270620346, 0.14252248406410217, -0.05857197195291519, 0.02393682673573494, 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null	null	null	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="basab1142/Taxi-v3-CQ", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```	{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3-CQ", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.52 +/- 2.77", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	basab1142/Taxi-v3-CQ	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	2024-02-10T14:37:36+00:00	[]	[]	TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us	# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage	[ "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ "TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n", "# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 32, 33 ]	[ "passage: TAGS\n#Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us \n# Q-Learning Agent playing1 Taxi-v3\n This is a trained model of a Q-Learning agent playing Taxi-v3 .\n\n ## Usage" ]	[ 0.048862796276807785, -0.16549694538116455, -0.005485367961227894, 0.02960980497300625, 0.1345081776380539, -0.01784728653728962, 0.11895976960659027, 0.07759871333837509, -0.07461097836494446, -0.055395450443029404, 0.1418241262435913, 0.09088201075792313, 0.055222880095243454, 0.05699880048632622, 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null	null	null	<br> # Vim Model Card ## Model Details Vision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks. - Developed by: [HUST](https://english.hust.edu.cn/), [Horizon Robotics](https://en.horizon.cc/), [BAAI](https://www.baai.ac.cn/english.html) - Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture. - License: Non-commercial license ### Model Sources - Repository: https://github.com/hustvl/Vim - Paper: https://arxiv.org/abs/2401.09417 ## Uses The primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone. The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence. ## How to Get Started with the Model - You can replace the backbone for vision tasks with the proposed Vim: https://github.com/hustvl/Vim/blob/main/vim/models_mamba.py - Then you can load this checkpoint and start training. ## Training Details Vim is pretrained on ImageNet-1K with classification supervision. The training data is around 1.3M images from [ImageNet-1K dataset](https://www.image-net.org/challenges/LSVRC/2012/). See more details in this [paper](https://arxiv.org/abs/2401.09417). ## Evaluation Vim-tiny is evaluated on ImageNet-1K val set, and achieves 76.1% Top-1 Acc. By further finetuning at finer granularity, Vim-tiny achieves 78.3% Top-1 Acc. See more details in this [paper](https://arxiv.org/abs/2401.09417). ## Additional Information ### Citation Information ``` @article{vim, title={Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model}, author={Lianghui Zhu and Bencheng Liao and Qian Zhang and Xinlong Wang and Wenyu Liu and Xinggang Wang}, journal={arXiv preprint arXiv:2401.09417}, year={2024} } ```	{"license": "apache-2.0"}	null	hustvl/Vim-tiny-midclstok	[ "arxiv:2401.09417", "license:apache-2.0", "region:us" ]	2024-02-10T14:40:18+00:00	[ "2401.09417" ]	[]	TAGS #arxiv-2401.09417 #license-apache-2.0 #region-us	<br> # Vim Model Card ## Model Details Vision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks. - Developed by: HUST, Horizon Robotics, BAAI - Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture. - License: Non-commercial license ### Model Sources - Repository: URL - Paper: URL ## Uses The primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone. The primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence. ## How to Get Started with the Model - You can replace the backbone for vision tasks with the proposed Vim: URL - Then you can load this checkpoint and start training. ## Training Details Vim is pretrained on ImageNet-1K with classification supervision. The training data is around 1.3M images from ImageNet-1K dataset. See more details in this paper. ## Evaluation Vim-tiny is evaluated on ImageNet-1K val set, and achieves 76.1% Top-1 Acc. By further finetuning at finer granularity, Vim-tiny achieves 78.3% Top-1 Acc. See more details in this paper. ## Additional Information	[ "# Vim Model Card", "## Model Details\n\nVision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.\n\n- Developed by: HUST, Horizon Robotics, BAAI\n- Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture.\n- License: Non-commercial license", "### Model Sources\n\n- Repository: URL\n- Paper: URL", "## Uses\n\nThe primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.\nThe primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.", "## How to Get Started with the Model\n\n- You can replace the backbone for vision tasks with the proposed Vim: URL\n- Then you can load this checkpoint and start training.", "## Training Details\n\nVim is pretrained on ImageNet-1K with classification supervision.\nThe training data is around 1.3M images from ImageNet-1K dataset.\nSee more details in this paper.", "## Evaluation\n\nVim-tiny is evaluated on ImageNet-1K val set, and achieves 76.1% Top-1 Acc. By further finetuning at finer granularity, Vim-tiny achieves 78.3% Top-1 Acc. See more details in this paper.", "## Additional Information" ]	[ "TAGS\n#arxiv-2401.09417 #license-apache-2.0 #region-us \n", "# Vim Model Card", "## Model Details\n\nVision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.\n\n- Developed by: HUST, Horizon Robotics, BAAI\n- Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture.\n- License: Non-commercial license", "### Model Sources\n\n- Repository: URL\n- Paper: URL", "## Uses\n\nThe primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.\nThe primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.", "## How to Get Started with the Model\n\n- You can replace the backbone for vision tasks with the proposed Vim: URL\n- Then you can load this checkpoint and start training.", "## Training Details\n\nVim is pretrained on ImageNet-1K with classification supervision.\nThe training data is around 1.3M images from ImageNet-1K dataset.\nSee more details in this paper.", "## Evaluation\n\nVim-tiny is evaluated on ImageNet-1K val set, and achieves 76.1% Top-1 Acc. By further finetuning at finer granularity, Vim-tiny achieves 78.3% Top-1 Acc. See more details in this paper.", "## Additional Information" ]	[ 23, 5, 78, 15, 69, 40, 43, 60, 5 ]	[ "passage: TAGS\n#arxiv-2401.09417 #license-apache-2.0 #region-us \n# Vim Model Card## Model Details\n\nVision Mamba (Vim) is a generic backbone trained on the ImageNet-1K dataset for vision tasks.\n\n- Developed by: HUST, Horizon Robotics, BAAI\n- Model type: A generic vision backbone based on the bidirectional state space model (SSM) architecture.\n- License: Non-commercial license### Model Sources\n\n- Repository: URL\n- Paper: URL## Uses\n\nThe primary use of Vim is research on vision tasks, e.g., classification, segmentation, detection, and instance segmentation, with an SSM-based backbone.\nThe primary intended users of the model are researchers and hobbyists in computer vision, machine learning, and artificial intelligence.## How to Get Started with the Model\n\n- You can replace the backbone for vision tasks with the proposed Vim: URL\n- Then you can load this checkpoint and start training.## Training Details\n\nVim is pretrained on ImageNet-1K with classification supervision.\nThe training data is around 1.3M images from ImageNet-1K dataset.\nSee more details in this paper.## Evaluation\n\nVim-tiny is evaluated on ImageNet-1K val set, and achieves 76.1% Top-1 Acc. By further finetuning at finer granularity, Vim-tiny achieves 78.3% Top-1 Acc. See more details in this paper.## Additional Information" ]	[ -0.06916045397520065, 0.07929331064224243, -0.004483504686504602, 0.006933827418833971, 0.10427270829677582, 0.02840515598654747, 0.08510330319404602, 0.0942901223897934, -0.02999553270637989, 0.026321377605199814, 0.05504373833537102, 0.026886777952313423, 0.11002620309591293, 0.17623749375343323, 0.09143801033496857, -0.18970540165901184, 0.013574239797890186, -0.03552014008164406, 0.03781726956367493, 0.04518246650695801, 0.12129514664411545, -0.08707787096500397, 0.0475257970392704, -0.010393248870968819, -0.12182798236608505, 0.02520960569381714, 0.003757215104997158, 0.01696396805346012, 0.13680942356586456, -0.006761287804692984, 0.07933206111192703, -0.02776586264371872, 0.10129541903734207, -0.13574907183647156, 0.026744043454527855, 0.09777875989675522, 0.04597890377044678, 0.06037607043981552, 0.05494625121355057, 0.13592788577079773, 0.1434420943260193, -0.05881083756685257, 0.0313713438808918, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cer_model This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.1](https://huggingface.co/dmis-lab/biobert-base-cased-v1.1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4081 - Precision: 0.9099 - Recall: 0.8471 - F1: 0.8774 - Accuracy: 0.9268 ## 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 - lr_scheduler_warmup_ratio: 0.2 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0179 \| 1.0 \| 4841 \| 0.4081 \| 0.9099 \| 0.8471 \| 0.8774 \| 0.9268 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "dmis-lab/biobert-base-cased-v1.1", "model-index": [{"name": "cer_model", "results": []}]}	token-classification	urbija/cer_model	[ "transformers", "tensorboard", "safetensors", "bert", "token-classification", "generated_from_trainer", "base_model:dmis-lab/biobert-base-cased-v1.1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T14:40:29+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us	cer\_model ========== This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.1 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4081 * Precision: 0.9099 * Recall: 0.8471 * F1: 0.8774 * Accuracy: 0.9268 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 * lr\_scheduler\_warmup\_ratio: 0.2 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.2\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.2\n* num\\_epochs: 1", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ 69, 116, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #token-classification #generated_from_trainer #base_model-dmis-lab/biobert-base-cased-v1.1 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_ratio: 0.2\n* num\\_epochs: 1### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ -0.10136894881725311, 0.08108913153409958, -0.004015001468360424, 0.09952660650014877, 0.12214108556509018, 0.018121706321835518, 0.14201807975769043, 0.1314338743686676, -0.04701366648077965, 0.059128087013959885, 0.13372133672237396, 0.12835732102394104, 0.010994626209139824, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2139 - Accuracy: 0.9235 - F1: 0.9235 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7853 \| 1.0 \| 250 \| 0.3142 \| 0.908 \| 0.9068 \| \| 0.2433 \| 2.0 \| 500 \| 0.2139 \| 0.9235 \| 0.9235 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.9235, "name": "Accuracy"}, {"type": "f1", "value": 0.9235223901734632, "name": "F1"}]}]}]}	text-classification	umeboshi827/distilbert-base-uncased-finetuned-emotion	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T14:41:03+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	distilbert-base-uncased-finetuned-emotion ========================================= This model is a fine-tuned version of distilbert-base-uncased on the emotion dataset. It achieves the following results on the evaluation set: * Loss: 0.2139 * Accuracy: 0.9235 * F1: 0.9235 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: 2 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 82, 98, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #distilbert #text-classification #generated_from_trainer #dataset-emotion #base_model-distilbert-base-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 64\n* eval\\_batch\\_size: 64\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.12298925966024399, 0.16415931284427643, -0.0022249515168368816, 0.12838715314865112, 0.12877148389816284, 0.01904618926346302, 0.1514195203781128, 0.12019442021846771, -0.03650584816932678, 0.046193916350603104, 0.12899209558963776, 0.13060440123081207, 0.020094528794288635, 0.13745972514152527, -0.08555696159601212, -0.20673809945583344, 0.021901700645685196, 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null	null	peft	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) <details><summary>See axolotl config</summary> axolotl version: `0.4.0` ```yaml base_model: Crystalcareai/CrystalMistral-26b model_type: AutoModelForCausalLM tokenizer_type: LlamaTokenizer trust_remote_code: true load_in_8bit: false load_in_4bit: true strict: false datasets: - path: Crystalcareai/synthetic_reasoning_natural_Alpaca_Combined type: alpaca dataset_prepared_path: last_run_prepared val_set_size: 0.0 output_dir: ./qlora-out ## You can optionally freeze the entire model and unfreeze a subset of parameters unfrozen_parameters: # - lm_head.* # - model.embed_tokens.* # - model.layers.2[0-9]+.block_sparse_moe.gate.* # - model.layers.2[0-9]+.block_sparse_moe.experts.* # - model.layers.3[0-9]+.block_sparse_moe.gate.* # - model.layers.3[0-9]+.block_sparse_moe.experts.* model_config: output_router_logits: true adapter: qlora lora_model_dir: sequence_len: 4096 sample_packing: true pad_to_sequence_len: true lora_r: 32 lora_alpha: 16 lora_dropout: 0.05 lora_target_linear: true lora_fan_in_fan_out: #lora_target_modules: # - gate # - q_proj # - k_proj # - v_proj # - o_proj # - w1 # - w2 # - w3 wandb_project: wandb_entity: wandb_watch: wandb_name: wandb_log_model: gradient_accumulation_steps: 16 micro_batch_size: 1 num_epochs: 2.2 optimizer: adamw_bnb_8bit lr_scheduler: cosine learning_rate: 0.0005 train_on_inputs: false group_by_length: false bf16: auto fp16: tf32: false gradient_checkpointing: true early_stopping_patience: resume_from_checkpoint: local_rank: logging_steps: 1 xformers_attention: flash_attention: true loss_watchdog_threshold: 5.0 loss_watchdog_patience: 3 warmup_steps: 10 evals_per_epoch: 4 eval_table_size: eval_table_max_new_tokens: 128 saves_per_epoch: 1 debug: deepspeed: weight_decay: 0.0 fsdp: fsdp_config: special_tokens: ``` </details><br> # qlora-out This model is a fine-tuned version of [Crystalcareai/CrystalMistral-26b](https://huggingface.co/Crystalcareai/CrystalMistral-26b) 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: 0.0005 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 2.2 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.17.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "Crystalcareai/CrystalMistral-26b", "model-index": [{"name": "qlora-out", "results": []}]}	null	Crystalcareai/Crystalmistral-26B-Lora	[ "peft", "tensorboard", "safetensors", "mixtral", "generated_from_trainer", "base_model:Crystalcareai/CrystalMistral-26b", "license:apache-2.0", "4-bit", "region:us" ]	2024-02-10T14:47:36+00:00	[]	[]	TAGS #peft #tensorboard #safetensors #mixtral #generated_from_trainer #base_model-Crystalcareai/CrystalMistral-26b #license-apache-2.0 #4-bit #region-us	<img src="URL alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary> axolotl version: '0.4.0' </details><br> # qlora-out This model is a fine-tuned version of Crystalcareai/CrystalMistral-26b 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: 0.0005 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - total_eval_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 2.2 ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.0.dev0 - Pytorch 2.1.2+cu118 - Datasets 2.17.0 - Tokenizers 0.15.0	[ "# qlora-out\n\nThis model is a fine-tuned version of Crystalcareai/CrystalMistral-26b on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2.2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.0" ]	[ "TAGS\n#peft #tensorboard #safetensors #mixtral #generated_from_trainer #base_model-Crystalcareai/CrystalMistral-26b #license-apache-2.0 #4-bit #region-us \n", "# qlora-out\n\nThis model is a fine-tuned version of Crystalcareai/CrystalMistral-26b on the None dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2.2", "### Training results", "### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.0" ]	[ 57, 34, 6, 12, 8, 3, 157, 4, 44 ]	[ "passage: TAGS\n#peft #tensorboard #safetensors #mixtral #generated_from_trainer #base_model-Crystalcareai/CrystalMistral-26b #license-apache-2.0 #4-bit #region-us \n# qlora-out\n\nThis model is a fine-tuned version of Crystalcareai/CrystalMistral-26b on the None dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0005\n- train_batch_size: 1\n- eval_batch_size: 1\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- gradient_accumulation_steps: 16\n- total_train_batch_size: 64\n- total_eval_batch_size: 4\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: cosine\n- lr_scheduler_warmup_steps: 10\n- num_epochs: 2.2### Training results### Framework versions\n\n- PEFT 0.8.2\n- Transformers 4.38.0.dev0\n- Pytorch 2.1.2+cu118\n- Datasets 2.17.0\n- Tokenizers 0.15.0" ]	[ -0.10255884379148483, 0.11905857175588608, -0.0030905415769666433, 0.08076983690261841, 0.11780809611082077, 0.03208485245704651, 0.08743969351053238, 0.14113302528858185, -0.07277835160493851, 0.12981557846069336, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-large-lora-4.72M-squad-model1 This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 47 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "t5-large", "model-index": [{"name": "t5-large-lora-4.72M-squad-model1", "results": []}]}	question-answering	varun-v-rao/t5-large-lora-4.72M-squad-model1	[ "transformers", "tensorboard", "safetensors", "t5", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:t5-large", "license:apache-2.0", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T14:49:24+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us	# t5-large-lora-4.72M-squad-model1 This model is a fine-tuned version of t5-large on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 47 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "# t5-large-lora-4.72M-squad-model1\n\nThis model is a fine-tuned version of t5-large on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 47\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n", "# t5-large-lora-4.72M-squad-model1\n\nThis model is a fine-tuned version of t5-large on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 47\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ 81, 39, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #t5 #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-t5-large #license-apache-2.0 #endpoints_compatible #text-generation-inference #region-us \n# t5-large-lora-4.72M-squad-model1\n\nThis model is a fine-tuned version of t5-large on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 47\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ -0.08534107357263565, 0.16570717096328735, -0.0029720955062657595, 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null	null	diffusers	### Scenery Dreambooth model trained by ArminSalem following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 2254161038 Sample pictures of this concept: ![0](https://huggingface.co/ArminSalem/scenery/resolve/main/sample_images/xzg_(1).jpg)	{"license": "creativeml-openrail-m", "tags": ["NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion"]}	text-to-image	ArminSalem/scenery	[ "diffusers", "safetensors", "NxtWave-GenAI-Webinar", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	2024-02-10T14:53:28+00:00	[]	[]	TAGS #diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us	### Scenery Dreambooth model trained by ArminSalem following the "Build your own Gen AI model" session by NxtWave. Project Submission Code: 2254161038 Sample pictures of this concept: !0.jpg)	[ "### Scenery Dreambooth model trained by ArminSalem following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 2254161038\n\nSample pictures of this concept:\n\n !0.jpg)" ]	[ "TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### Scenery Dreambooth model trained by ArminSalem following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 2254161038\n\nSample pictures of this concept:\n\n !0.jpg)" ]	[ 73, 54 ]	[ "passage: TAGS\n#diffusers #safetensors #NxtWave-GenAI-Webinar #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### Scenery Dreambooth model trained by ArminSalem following the \"Build your own Gen AI model\" session by NxtWave.\n\nProject Submission Code: 2254161038\n\nSample pictures of this concept:\n\n !0.jpg)" ]	[ -0.14063820242881775, 0.14938879013061523, -0.0013897860189899802, 0.03891326114535332, 0.05012296140193939, -0.047020748257637024, 0.17291851341724396, -0.0034986790269613266, -0.0035284992773085833, 0.02036232501268387, 0.15607069432735443, 0.07368086278438568, 0.019647452980279922, 0.14985094964504242, -0.05574525147676468, -0.15439757704734802, 0.030563831329345703, 0.0625372976064682, -0.00111645576544106, 0.06150165572762489, 0.059447258710861206, -0.0747774988412857, 0.12377536296844482, -0.035657912492752075, -0.14170636236667633, -0.013247494585812092, -0.03740526735782623, -0.04995523393154144, 0.10305073112249374, 0.05100759118795395, 0.029516849666833878, 0.10136941820383072, 0.031043609604239464, -0.05354999005794525, 0.04030903801321983, -0.007583029102534056, -0.06276257336139679, 0.0536002591252327, 0.038916558027267456, 0.07808327674865723, 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null	null	diffusers	# DreamBooth trained by AutoTrain Text encoder was not trained.	{"tags": ["text-to-image", "diffusers", "autotrain"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "alianawatson woman", "inference": true}	text-to-image	dominic1021/alianawatson	[ "diffusers", "text-to-image", "autotrain", "base_model:stabilityai/stable-diffusion-xl-base-1.0", "has_space", "region:us" ]	2024-02-10T14:56:47+00:00	[]	[]	TAGS #diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us	# DreamBooth trained by AutoTrain Text encoder was not trained.	[ "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]	[ "TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n", "# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]	[ 45, 19 ]	[ "passage: TAGS\n#diffusers #text-to-image #autotrain #base_model-stabilityai/stable-diffusion-xl-base-1.0 #has_space #region-us \n# DreamBooth trained by AutoTrain\n\nText encoder was not trained." ]	[ -0.02063869684934616, 0.12998254597187042, -0.00014558587281499058, 0.05282456427812576, 0.16523675620555878, 0.04722703993320465, 0.16625140607357025, 0.08092519640922546, -0.021600954234600067, 0.06268861889839172, 0.19911405444145203, -0.005327701102942228, 0.005592701490968466, 0.22998546063899994, -0.094501793384552, -0.15147385001182556, 0.05843960493803024, -0.017813973128795624, 0.08953600376844406, 0.04556926712393761, 0.01589704304933548, -0.08332102000713348, 0.06851272284984589, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	null	ahsenali/falcon-7b-med_dialogue-symptoms	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T15:05:01+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 31, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06646376848220825, 0.2168014943599701, -0.00225935154594481, 0.023818302899599075, 0.1271018385887146, -0.001635765191167593, 0.04218708351254463, 0.13324736058712006, -0.020175931975245476, 0.11144465953111649, 0.046588581055402756, 0.09377603232860565, 0.09928803145885468, 0.18404334783554077, 0.04859916493296623, -0.2059975117444992, 0.007056170143187046, -0.09090408682823181, 0.014076028019189835, 0.1116579994559288, 0.13719257712364197, -0.10291384905576706, 0.08272874355316162, -0.04045208916068077, -0.02019004337489605, 0.00012576708104461432, -0.09259183704853058, -0.07032395154237747, 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null	null	diffusers	### omar_ai_interior_v1.1.0 Dreambooth model trained by davolu with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Sample pictures of this concept:	{"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]}	text-to-image	davolu/omar-ai-interior-v1-1-0	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	2024-02-10T15:05:35+00:00	[]	[]	TAGS #diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us	### omar_ai_interior_v1.1.0 Dreambooth model trained by davolu with TheLastBen's fast-DreamBooth notebook Test the concept via A1111 Colab fast-Colab-A1111 Sample pictures of this concept:	[ "### omar_ai_interior_v1.1.0 Dreambooth model trained by davolu with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]	[ "TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n", "### omar_ai_interior_v1.1.0 Dreambooth model trained by davolu with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]	[ 61, 57 ]	[ "passage: TAGS\n#diffusers #safetensors #text-to-image #stable-diffusion #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n### omar_ai_interior_v1.1.0 Dreambooth model trained by davolu with TheLastBen's fast-DreamBooth notebook\n\n\nTest the concept via A1111 Colab fast-Colab-A1111\n\nSample pictures of this concept:" ]	[ -0.1224161684513092, 0.004456889349967241, -0.0022144424729049206, 0.06676553934812546, 0.00915249902755022, -0.04060911387205124, 0.1413469910621643, -0.03216274082660675, 0.0035949349403381348, 0.05978820100426674, 0.14699915051460266, -0.00810061115771532, 0.0029571237973868847, 0.1663748323917389, -0.0544627420604229, -0.12237488478422165, 0.047360483556985855, 0.02360210195183754, -0.0543777234852314, 0.07799369096755981, 0.08167172223329544, -0.09177296608686447, 0.10849093645811081, -0.02639875002205372, -0.09322389960289001, -0.022926626726984978, -0.06372110545635223, -0.07775455713272095, 0.06197531521320343, 0.049098383635282516, 0.08399751037359238, 0.12644700706005096, 0.027766985818743706, -0.05382821708917618, 0.051845043897628784, -0.028214184567332268, -0.02623174898326397, 0.025342511013150215, -0.001804569736123085, 0.030393607914447784, -0.006346731912344694, 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null	null	null	A WASM version of Tower	{"language": ["en", "de", "fr", "es", "pt", "it", "ru", "ko", "nl"], "license": "llama2", "pipeline_tag": "translation"}	translation	BoscoTheDog/TowerInstruct-7B-v0.1-q4f32_1-MLC	[ "translation", "en", "de", "fr", "es", "pt", "it", "ru", "ko", "nl", "license:llama2", "region:us" ]	2024-02-10T15:08:06+00:00	[]	[ "en", "de", "fr", "es", "pt", "it", "ru", "ko", "nl" ]	TAGS #translation #en #de #fr #es #pt #it #ru #ko #nl #license-llama2 #region-us	A WASM version of Tower	[]	[ "TAGS\n#translation #en #de #fr #es #pt #it #ru #ko #nl #license-llama2 #region-us \n" ]	[ 34 ]	[ "passage: TAGS\n#translation #en #de #fr #es #pt #it #ru #ko #nl #license-llama2 #region-us \n" ]	[ 0.06437951326370239, -0.18419931828975677, -0.010122072882950306, 0.03505084291100502, 0.03327684849500656, 0.033867545425891876, 0.07929440587759018, 0.021055109798908234, 0.18238984048366547, -0.0024397114757448435, 0.14380191266536713, 0.04402720183134079, 0.02644507773220539, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # trainer This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.4490 - Accuracy: 0.8466 - F1: 0.8065 - Precision: 0.8406 - Recall: 0.7876 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 267 \| 0.3860 \| 0.8370 \| 0.7999 \| 0.8184 \| 0.7876 \| \| 0.3455 \| 2.0 \| 534 \| 0.4490 \| 0.8466 \| 0.8065 \| 0.8406 \| 0.7876 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "bert-large-uncased", "model-index": [{"name": "trainer", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "config": "cola", "split": "validation", "args": "cola"}, "metrics": [{"type": "accuracy", "value": 0.8465963566634708, "name": "Accuracy"}, {"type": "f1", "value": 0.8064540073113251, "name": "F1"}, {"type": "precision", "value": 0.840606542828289, "name": "Precision"}, {"type": "recall", "value": 0.7876439727431708, "name": "Recall"}]}]}]}	text-classification	Sifal/bertGED	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "dataset:glue", "base_model:bert-large-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T15:09:33+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us	trainer ======= This model is a fine-tuned version of bert-large-uncased on the glue dataset. It achieves the following results on the evaluation set: * Loss: 0.4490 * Accuracy: 0.8466 * F1: 0.8065 * Precision: 0.8406 * Recall: 0.7876 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 32 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 32 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ 79, 126, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #bert #text-classification #generated_from_trainer #dataset-glue #base_model-bert-large-uncased #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 32\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ -0.12863224744796753, 0.15584468841552734, -0.0036019557155668736, 0.09342149645090103, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # GeneralNews_9_loadbest This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.7052 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.4665 \| 1.0 \| 500 \| 3.0865 \| \| 3.2219 \| 2.0 \| 1000 \| 2.8680 \| \| 3.0254 \| 3.0 \| 1500 \| 2.7771 \| \| 2.8798 \| 4.0 \| 2000 \| 2.7572 \| \| 2.7943 \| 5.0 \| 2500 \| 2.7339 \| \| 2.6988 \| 6.0 \| 3000 \| 2.7126 \| \| 2.4587 \| 7.0 \| 3500 \| 2.7086 \| \| 2.4769 \| 8.0 \| 4000 \| 2.7076 \| \| 2.5388 \| 9.0 \| 4500 \| 2.7075 \| \| 2.4411 \| 10.0 \| 5000 \| 2.7052 \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "base_model": "csebuetnlp/mT5_multilingual_XLSum", "model-index": [{"name": "GeneralNews_9_loadbest", "results": []}]}	text2text-generation	tjkmitl/GeneralNews_2_loadbest_10000rows	[ "transformers", "tensorboard", "safetensors", "mt5", "text2text-generation", "generated_from_trainer", "base_model:csebuetnlp/mT5_multilingual_XLSum", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:11:03+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-csebuetnlp/mT5_multilingual_XLSum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	GeneralNews\_9\_loadbest ======================== This model is a fine-tuned version of csebuetnlp/mT5\_multilingual\_XLSum on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.7052 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 16 * eval\_batch\_size: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 1000 * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-csebuetnlp/mT5_multilingual_XLSum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 83, 116, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-csebuetnlp/mT5_multilingual_XLSum #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 1000\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.1456754207611084, 0.13546152412891388, -0.0016651819460093975, 0.09868098795413971, 0.12807321548461914, 0.023050149902701378, 0.16873082518577576, 0.12122264504432678, -0.042738672345876694, 0.09066737443208694, 0.15380936861038208, 0.10357966274023056, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # segformer-b3-from-scratch-final This model is a fine-tuned version of [](https://huggingface.co/) on the samitizerxu/kelp_data_rgbagg_swin_nir_int_cleaned dataset. It achieves the following results on the evaluation set: - Iou Kelp: 0.0073 - Loss: 0.9864 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 60 ### Training results \| Training Loss \| Epoch \| Step \| Iou Kelp \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:---------------:\| \| 0.9993 \| 0.18 \| 100 \| 0.0069 \| 0.9867 \| \| 0.9945 \| 0.37 \| 200 \| 0.0076 \| 0.9855 \| \| 0.9991 \| 0.55 \| 300 \| 0.0069 \| 0.9867 \| \| 0.999 \| 0.74 \| 400 \| 0.0066 \| 0.9870 \| \| 0.9959 \| 0.92 \| 500 \| 0.0071 \| 0.9864 \| \| 0.9965 \| 1.11 \| 600 \| 0.0066 \| 0.9871 \| \| 0.9764 \| 1.29 \| 700 \| 0.0066 \| 0.9871 \| \| 0.9951 \| 1.48 \| 800 \| 0.0066 \| 0.9871 \| \| 0.9999 \| 1.66 \| 900 \| 0.0066 \| 0.9870 \| \| 0.9878 \| 1.85 \| 1000 \| 0.0066 \| 0.9871 \| \| 0.9978 \| 2.03 \| 1100 \| 0.0066 \| 0.9871 \| \| 0.975 \| 2.21 \| 1200 \| 0.0069 \| 0.9868 \| \| 0.9957 \| 2.4 \| 1300 \| 0.0073 \| 0.9859 \| \| 0.9914 \| 2.58 \| 1400 \| 0.0079 \| 0.9860 \| \| 0.9928 \| 2.77 \| 1500 \| 0.0074 \| 0.9859 \| \| 0.9994 \| 2.95 \| 1600 \| 0.0004 \| 0.9863 \| \| 0.995 \| 3.14 \| 1700 \| 0.0101 \| 0.9860 \| \| 0.9963 \| 3.32 \| 1800 \| 0.0 \| 0.9872 \| \| 0.9972 \| 3.51 \| 1900 \| 0.0074 \| 0.9858 \| \| 0.9959 \| 3.69 \| 2000 \| 0.0076 \| 0.9859 \| \| 0.9941 \| 3.87 \| 2100 \| 0.0073 \| 0.9859 \| \| 0.992 \| 4.06 \| 2200 \| 0.0002 \| 0.9951 \| \| 0.9903 \| 4.24 \| 2300 \| 0.0073 \| 0.9859 \| \| 0.9989 \| 4.43 \| 2400 \| 0.0066 \| 0.9871 \| \| 0.9999 \| 4.61 \| 2500 \| 0.0073 \| 0.9866 \| \| 0.9946 \| 4.8 \| 2600 \| 0.0073 \| 0.9859 \| \| 0.9959 \| 4.98 \| 2700 \| 0.0073 \| 0.9859 \| \| 0.9971 \| 5.17 \| 2800 \| 0.0079 \| 0.9863 \| \| 0.9949 \| 5.35 \| 2900 \| 0.0074 \| 0.9859 \| \| 0.9846 \| 5.54 \| 3000 \| 0.0073 \| 0.9859 \| \| 0.9941 \| 5.72 \| 3100 \| 0.0074 \| 0.9859 \| \| 0.9867 \| 5.9 \| 3200 \| 0.0074 \| 0.9858 \| \| 0.9857 \| 6.09 \| 3300 \| 0.0074 \| 0.9861 \| \| 0.9986 \| 6.27 \| 3400 \| 0.0074 \| 0.9859 \| \| 0.9927 \| 6.46 \| 3500 \| 0.0074 \| 0.9860 \| \| 0.998 \| 6.64 \| 3600 \| 0.0075 \| 0.9858 \| \| 0.9971 \| 6.83 \| 3700 \| 0.0074 \| 0.9859 \| \| 0.9951 \| 7.01 \| 3800 \| 0.0074 \| 0.9859 \| \| 0.9998 \| 7.2 \| 3900 \| 0.0074 \| 0.9861 \| \| 0.995 \| 7.38 \| 4000 \| 0.0075 \| 0.9858 \| \| 0.9912 \| 7.56 \| 4100 \| 0.0072 \| 0.9861 \| \| 0.9995 \| 7.75 \| 4200 \| 0.0074 \| 0.9858 \| \| 0.9934 \| 7.93 \| 4300 \| 0.0074 \| 0.9860 \| \| 0.9885 \| 8.12 \| 4400 \| 0.0074 \| 0.9860 \| \| 0.9937 \| 8.3 \| 4500 \| 0.0075 \| 0.9857 \| \| 0.9954 \| 8.49 \| 4600 \| 0.0075 \| 0.9857 \| \| 0.9794 \| 8.67 \| 4700 \| 0.0074 \| 0.9858 \| \| 0.9967 \| 8.86 \| 4800 \| 0.0075 \| 0.9857 \| \| 0.9954 \| 9.04 \| 4900 \| 0.0074 \| 0.9862 \| \| 0.9966 \| 9.23 \| 5000 \| 0.0074 \| 0.9859 \| \| 0.9953 \| 9.41 \| 5100 \| 0.0074 \| 0.9859 \| \| 0.9961 \| 9.59 \| 5200 \| 0.0074 \| 0.9859 \| \| 0.993 \| 9.78 \| 5300 \| 0.0075 \| 0.9858 \| \| 0.9993 \| 9.96 \| 5400 \| 0.0070 \| 0.9870 \| \| 0.995 \| 10.15 \| 5500 \| 0.0032 \| 0.9933 \| \| 0.9945 \| 10.33 \| 5600 \| 0.0061 \| 0.9884 \| \| 0.9738 \| 10.52 \| 5700 \| 0.0069 \| 0.9866 \| \| 0.9983 \| 10.7 \| 5800 \| 0.0067 \| 0.9869 \| \| 0.9975 \| 10.89 \| 5900 \| 0.0076 \| 0.9854 \| \| 0.9925 \| 11.07 \| 6000 \| 0.0086 \| 0.9839 \| \| 0.9821 \| 11.25 \| 6100 \| 0.0092 \| 0.9822 \| \| 0.9972 \| 11.44 \| 6200 \| 0.0107 \| 0.9787 \| \| 0.9802 \| 11.62 \| 6300 \| 0.0109 \| 0.9781 \| \| 1.0 \| 11.81 \| 6400 \| 0.0076 \| 0.9854 \| \| 0.9922 \| 11.99 \| 6500 \| 0.0108 \| 0.9793 \| \| 0.9915 \| 12.18 \| 6600 \| 0.0108 \| 0.9799 \| \| 0.9963 \| 12.36 \| 6700 \| 0.0075 \| 0.9857 \| \| 0.9966 \| 12.55 \| 6800 \| 0.0075 \| 0.9859 \| \| 0.9978 \| 12.73 \| 6900 \| 0.0069 \| 0.9870 \| \| 0.9847 \| 12.92 \| 7000 \| 0.0074 \| 0.9860 \| \| 0.9972 \| 13.1 \| 7100 \| 0.0072 \| 0.9862 \| \| 0.9868 \| 13.28 \| 7200 \| 0.0071 \| 0.9865 \| \| 0.9961 \| 13.47 \| 7300 \| 0.0072 \| 0.9864 \| \| 0.9845 \| 13.65 \| 7400 \| 0.0071 \| 0.9865 \| \| 0.9974 \| 13.84 \| 7500 \| 0.0074 \| 0.9862 \| \| 0.9906 \| 14.02 \| 7600 \| 0.0076 \| 0.9847 \| \| 0.9999 \| 14.21 \| 7700 \| 0.0075 \| 0.9860 \| \| 0.9821 \| 14.39 \| 7800 \| 0.0074 \| 0.9860 \| \| 0.9976 \| 14.58 \| 7900 \| 0.0105 \| 0.9795 \| \| 0.9871 \| 14.76 \| 8000 \| 0.0103 \| 0.9803 \| \| 0.991 \| 14.94 \| 8100 \| 0.0102 \| 0.9805 \| \| 0.9903 \| 15.13 \| 8200 \| 0.0104 \| 0.9799 \| \| 0.995 \| 15.31 \| 8300 \| 0.0074 \| 0.9861 \| \| 0.9981 \| 15.5 \| 8400 \| 0.0073 \| 0.9863 \| \| 0.9985 \| 15.68 \| 8500 \| 0.0073 \| 0.9863 \| \| 0.9973 \| 15.87 \| 8600 \| 0.0074 \| 0.9862 \| \| 0.989 \| 16.05 \| 8700 \| 0.0073 \| 0.9863 \| \| 0.9938 \| 16.24 \| 8800 \| 0.0074 \| 0.9860 \| \| 0.9951 \| 16.42 \| 8900 \| 0.0106 \| 0.9786 \| \| 0.9921 \| 16.61 \| 9000 \| 0.0092 \| 0.9824 \| \| 0.9971 \| 16.79 \| 9100 \| 0.0083 \| 0.9846 \| \| 0.9846 \| 16.97 \| 9200 \| 0.0087 \| 0.9838 \| \| 0.9849 \| 17.16 \| 9300 \| 0.0095 \| 0.9820 \| \| 0.9851 \| 17.34 \| 9400 \| 0.0096 \| 0.9818 \| \| 0.9902 \| 17.53 \| 9500 \| 0.0099 \| 0.9811 \| \| 0.9889 \| 17.71 \| 9600 \| 0.0075 \| 0.9860 \| \| 0.9782 \| 17.9 \| 9700 \| 0.0075 \| 0.9908 \| \| 0.999 \| 18.08 \| 9800 \| 0.0074 \| 0.9862 \| \| 0.9878 \| 18.27 \| 9900 \| 0.0073 \| 0.9862 \| \| 0.999 \| 18.45 \| 10000 \| 0.0074 \| 0.9862 \| \| 1.0 \| 18.63 \| 10100 \| 0.0074 \| 0.9861 \| \| 0.9951 \| 18.82 \| 10200 \| 0.0075 \| 0.9859 \| \| 0.9892 \| 19.0 \| 10300 \| 0.0073 \| 0.9861 \| \| 0.9853 \| 19.19 \| 10400 \| 0.0074 \| 0.9859 \| \| 0.9959 \| 19.37 \| 10500 \| 0.0074 \| 0.9859 \| \| 0.9999 \| 19.56 \| 10600 \| 0.0073 \| 0.9861 \| \| 0.9872 \| 19.74 \| 10700 \| 0.0074 \| 0.9859 \| \| 0.9939 \| 19.93 \| 10800 \| 0.0074 \| 0.9861 \| \| 0.9924 \| 20.11 \| 10900 \| 0.0073 \| 0.9862 \| \| 0.9993 \| 20.3 \| 11000 \| 0.0074 \| 0.9860 \| \| 0.9934 \| 20.48 \| 11100 \| 0.0075 \| 0.9858 \| \| 0.9976 \| 20.66 \| 11200 \| 0.0074 \| 0.9859 \| \| 0.9878 \| 20.85 \| 11300 \| 0.0074 \| 0.9859 \| \| 0.9955 \| 21.03 \| 11400 \| 0.0074 \| 0.9859 \| \| 0.9878 \| 21.22 \| 11500 \| 0.0075 \| 0.9859 \| \| 0.999 \| 21.4 \| 11600 \| 0.0074 \| 0.9859 \| \| 0.9945 \| 21.59 \| 11700 \| 0.0074 \| 0.9861 \| \| 0.994 \| 21.77 \| 11800 \| 0.0075 \| 0.9859 \| \| 0.9848 \| 21.96 \| 11900 \| 0.0075 \| 0.9859 \| \| 0.9998 \| 22.14 \| 12000 \| 0.0075 \| 0.9859 \| \| 0.9826 \| 22.32 \| 12100 \| 0.0075 \| 0.9859 \| \| 0.999 \| 22.51 \| 12200 \| 0.0074 \| 0.9861 \| \| 0.9941 \| 22.69 \| 12300 \| 0.0073 \| 0.9863 \| \| 0.9933 \| 22.88 \| 12400 \| 0.0074 \| 0.9862 \| \| 0.9935 \| 23.06 \| 12500 \| 0.0074 \| 0.9862 \| \| 0.9992 \| 23.25 \| 12600 \| 0.0073 \| 0.9863 \| \| 0.9943 \| 23.43 \| 12700 \| 0.0073 \| 0.9863 \| \| 0.9777 \| 23.62 \| 12800 \| 0.0075 \| 0.9858 \| \| 0.9977 \| 23.8 \| 12900 \| 0.0073 \| 0.9862 \| \| 0.9925 \| 23.99 \| 13000 \| 0.0074 \| 0.9861 \| \| 0.9866 \| 24.17 \| 13100 \| 0.0073 \| 0.9863 \| \| 0.9979 \| 24.35 \| 13200 \| 0.0073 \| 0.9862 \| \| 0.9819 \| 24.54 \| 13300 \| 0.0073 \| 0.9864 \| \| 0.966 \| 24.72 \| 13400 \| 0.0073 \| 0.9864 \| \| 0.998 \| 24.91 \| 13500 \| 0.0073 \| 0.9863 \| \| 0.9969 \| 25.09 \| 13600 \| 0.0073 \| 0.9863 \| \| 0.9881 \| 25.28 \| 13700 \| 0.0073 \| 0.9863 \| \| 0.9701 \| 25.46 \| 13800 \| 0.0073 \| 0.9864 \| \| 0.9963 \| 25.65 \| 13900 \| 0.0073 \| 0.9863 \| \| 0.9885 \| 25.83 \| 14000 \| 0.0073 \| 0.9863 \| \| 0.9904 \| 26.01 \| 14100 \| 0.0073 \| 0.9864 \| \| 0.9976 \| 26.2 \| 14200 \| 0.0074 \| 0.9862 \| \| 0.995 \| 26.38 \| 14300 \| 0.0073 \| 0.9863 \| \| 0.9886 \| 26.57 \| 14400 \| 0.0073 \| 0.9864 \| \| 0.9735 \| 26.75 \| 14500 \| 0.0073 \| 0.9863 \| \| 0.988 \| 26.94 \| 14600 \| 0.0073 \| 0.9864 \| \| 0.9854 \| 27.12 \| 14700 \| 0.0073 \| 0.9864 \| \| 0.9947 \| 27.31 \| 14800 \| 0.0073 \| 0.9864 \| \| 0.9944 \| 27.49 \| 14900 \| 0.0073 \| 0.9864 \| \| 0.9935 \| 27.68 \| 15000 \| 0.0073 \| 0.9862 \| \| 0.9887 \| 27.86 \| 15100 \| 0.0073 \| 0.9863 \| \| 0.9958 \| 28.04 \| 15200 \| 0.0073 \| 0.9862 \| \| 0.9994 \| 28.23 \| 15300 \| 0.0073 \| 0.9863 \| \| 0.9953 \| 28.41 \| 15400 \| 0.0073 \| 0.9868 \| \| 0.9798 \| 28.6 \| 15500 \| 0.0073 \| 0.9863 \| \| 0.9867 \| 28.78 \| 15600 \| 0.0073 \| 0.9863 \| \| 0.9903 \| 28.97 \| 15700 \| 0.0073 \| 0.9863 \| \| 0.9943 \| 29.15 \| 15800 \| 0.0073 \| 0.9864 \| \| 0.9725 \| 29.34 \| 15900 \| 0.0072 \| 0.9864 \| \| 0.9987 \| 29.52 \| 16000 \| 0.0073 \| 0.9864 \| \| 0.9871 \| 29.7 \| 16100 \| 0.0072 \| 0.9864 \| \| 0.992 \| 29.89 \| 16200 \| 0.0072 \| 0.9864 \| \| 0.996 \| 30.07 \| 16300 \| 0.0073 \| 0.9864 \| \| 0.9998 \| 30.26 \| 16400 \| 0.0073 \| 0.9864 \| \| 0.9964 \| 30.44 \| 16500 \| 0.0074 \| 0.9859 \| \| 0.9992 \| 30.63 \| 16600 \| 0.0075 \| 0.9858 \| \| 0.9946 \| 30.81 \| 16700 \| 0.0074 \| 0.9861 \| \| 0.9911 \| 31.0 \| 16800 \| 0.0075 \| 0.9859 \| \| 0.9878 \| 31.18 \| 16900 \| 0.0075 \| 0.9859 \| \| 0.9826 \| 31.37 \| 17000 \| 0.0075 \| 0.9859 \| \| 0.9894 \| 31.55 \| 17100 \| 0.0075 \| 0.9859 \| \| 0.9887 \| 31.73 \| 17200 \| 0.0075 \| 0.9860 \| \| 0.9962 \| 31.92 \| 17300 \| 0.0073 \| 0.9862 \| \| 0.9937 \| 32.1 \| 17400 \| 0.0073 \| 0.9863 \| \| 0.9828 \| 32.29 \| 17500 \| 0.0073 \| 0.9863 \| \| 0.993 \| 32.47 \| 17600 \| 0.0073 \| 0.9864 \| \| 0.9975 \| 32.66 \| 17700 \| 0.0073 \| 0.9864 \| \| 0.994 \| 32.84 \| 17800 \| 0.0073 \| 0.9864 \| \| 0.9894 \| 33.03 \| 17900 \| 0.0073 \| 0.9862 \| \| 0.9938 \| 33.21 \| 18000 \| 0.0073 \| 0.9863 \| \| 0.9711 \| 33.39 \| 18100 \| 0.0073 \| 0.9863 \| \| 0.9896 \| 33.58 \| 18200 \| 0.0073 \| 0.9864 \| \| 0.9907 \| 33.76 \| 18300 \| 0.0073 \| 0.9864 \| \| 0.9934 \| 33.95 \| 18400 \| 0.0073 \| 0.9864 \| \| 0.9723 \| 34.13 \| 18500 \| 0.0073 \| 0.9864 \| \| 0.9842 \| 34.32 \| 18600 \| 0.0073 \| 0.9864 \| \| 0.9955 \| 34.5 \| 18700 \| 0.0073 \| 0.9864 \| \| 0.9824 \| 34.69 \| 18800 \| 0.0073 \| 0.9864 \| \| 0.9949 \| 34.87 \| 18900 \| 0.0073 \| 0.9864 \| \| 0.9943 \| 35.06 \| 19000 \| 0.0073 \| 0.9864 \| \| 0.9992 \| 35.24 \| 19100 \| 0.0073 \| 0.9864 \| \| 0.9843 \| 35.42 \| 19200 \| 0.0073 \| 0.9864 \| \| 0.9785 \| 35.61 \| 19300 \| 0.0073 \| 0.9864 \| \| 0.9999 \| 35.79 \| 19400 \| 0.0073 \| 0.9864 \| \| 0.9727 \| 35.98 \| 19500 \| 0.0073 \| 0.9864 \| \| 0.9949 \| 36.16 \| 19600 \| 0.0073 \| 0.9864 \| \| 0.9949 \| 36.35 \| 19700 \| 0.0073 \| 0.9864 \| \| 0.9887 \| 36.53 \| 19800 \| 0.0073 \| 0.9864 \| \| 0.9736 \| 36.72 \| 19900 \| 0.0073 \| 0.9864 \| \| 0.9966 \| 36.9 \| 20000 \| 0.0073 \| 0.9864 \| \| 0.9984 \| 37.08 \| 20100 \| 0.0073 \| 0.9864 \| \| 0.993 \| 37.27 \| 20200 \| 0.0073 \| 0.9864 \| \| 0.9998 \| 37.45 \| 20300 \| 0.0073 \| 0.9864 \| \| 0.9972 \| 37.64 \| 20400 \| 0.0073 \| 0.9864 \| \| 0.986 \| 37.82 \| 20500 \| 0.0073 \| 0.9864 \| \| 0.9914 \| 38.01 \| 20600 \| 0.0073 \| 0.9864 \| \| 0.9954 \| 38.19 \| 20700 \| 0.0073 \| 0.9864 \| \| 0.9764 \| 38.38 \| 20800 \| 0.0073 \| 0.9864 \| \| 0.9953 \| 38.56 \| 20900 \| 0.0073 \| 0.9864 \| \| 0.9837 \| 38.75 \| 21000 \| 0.0073 \| 0.9864 \| \| 0.9665 \| 38.93 \| 21100 \| 0.0073 \| 0.9864 \| \| 0.9964 \| 39.11 \| 21200 \| 0.0073 \| 0.9864 \| \| 0.9935 \| 39.3 \| 21300 \| 0.0073 \| 0.9864 \| \| 0.9466 \| 39.48 \| 21400 \| 0.0073 \| 0.9864 \| \| 0.9853 \| 39.67 \| 21500 \| 0.0073 \| 0.9864 \| \| 0.9678 \| 39.85 \| 21600 \| 0.0073 \| 0.9864 \| \| 0.995 \| 40.04 \| 21700 \| 0.0073 \| 0.9864 \| \| 0.9987 \| 40.22 \| 21800 \| 0.0073 \| 0.9864 \| \| 0.9935 \| 40.41 \| 21900 \| 0.0073 \| 0.9864 \| \| 0.991 \| 40.59 \| 22000 \| 0.0073 \| 0.9864 \| \| 0.999 \| 40.77 \| 22100 \| 0.0073 \| 0.9864 \| \| 0.9985 \| 40.96 \| 22200 \| 0.0073 \| 0.9864 \| \| 0.9954 \| 41.14 \| 22300 \| 0.0073 \| 0.9864 \| \| 0.9894 \| 41.33 \| 22400 \| 0.0073 \| 0.9864 \| \| 0.9851 \| 41.51 \| 22500 \| 0.0073 \| 0.9864 \| \| 0.9882 \| 41.7 \| 22600 \| 0.0073 \| 0.9864 \| \| 0.9999 \| 41.88 \| 22700 \| 0.0073 \| 0.9864 \| \| 0.9901 \| 42.07 \| 22800 \| 0.0073 \| 0.9864 \| \| 0.9853 \| 42.25 \| 22900 \| 0.0073 \| 0.9864 \| \| 0.9868 \| 42.44 \| 23000 \| 0.0073 \| 0.9864 \| \| 0.9973 \| 42.62 \| 23100 \| 0.0073 \| 0.9864 \| \| 0.9979 \| 42.8 \| 23200 \| 0.0073 \| 0.9865 \| \| 0.9867 \| 42.99 \| 23300 \| 0.0073 \| 0.9864 \| \| 0.9994 \| 43.17 \| 23400 \| 0.0073 \| 0.9864 \| \| 0.9984 \| 43.36 \| 23500 \| 0.0073 \| 0.9865 \| \| 0.9974 \| 43.54 \| 23600 \| 0.0073 \| 0.9865 \| \| 0.9999 \| 43.73 \| 23700 \| 0.0073 \| 0.9864 \| \| 0.9669 \| 43.91 \| 23800 \| 0.0073 \| 0.9864 \| \| 0.9925 \| 44.1 \| 23900 \| 0.0073 \| 0.9864 \| \| 0.9961 \| 44.28 \| 24000 \| 0.0073 \| 0.9864 \| \| 0.9815 \| 44.46 \| 24100 \| 0.0073 \| 0.9864 \| \| 0.9968 \| 44.65 \| 24200 \| 0.0073 \| 0.9864 \| \| 0.9964 \| 44.83 \| 24300 \| 0.0073 \| 0.9864 \| \| 0.9929 \| 45.02 \| 24400 \| 0.0073 \| 0.9864 \| \| 0.9712 \| 45.2 \| 24500 \| 0.0073 \| 0.9864 \| \| 0.9884 \| 45.39 \| 24600 \| 0.0073 \| 0.9864 \| \| 0.9897 \| 45.57 \| 24700 \| 0.0073 \| 0.9864 \| \| 0.9862 \| 45.76 \| 24800 \| 0.0073 \| 0.9865 \| \| 0.9768 \| 45.94 \| 24900 \| 0.0073 \| 0.9865 \| \| 0.9965 \| 46.13 \| 25000 \| 0.0073 \| 0.9865 \| \| 0.9996 \| 46.31 \| 25100 \| 0.0073 \| 0.9864 \| \| 0.9887 \| 46.49 \| 25200 \| 0.0073 \| 0.9864 \| \| 0.9991 \| 46.68 \| 25300 \| 0.0073 \| 0.9864 \| \| 0.984 \| 46.86 \| 25400 \| 0.0073 \| 0.9864 \| \| 0.983 \| 47.05 \| 25500 \| 0.0073 \| 0.9864 \| \| 0.9997 \| 47.23 \| 25600 \| 0.0073 \| 0.9864 \| \| 0.9923 \| 47.42 \| 25700 \| 0.0073 \| 0.9865 \| \| 0.9962 \| 47.6 \| 25800 \| 0.0073 \| 0.9864 \| \| 0.9747 \| 47.79 \| 25900 \| 0.0073 \| 0.9864 \| \| 0.9981 \| 47.97 \| 26000 \| 0.0073 \| 0.9864 \| \| 0.9936 \| 48.15 \| 26100 \| 0.0073 \| 0.9864 \| \| 0.9976 \| 48.34 \| 26200 \| 0.0073 \| 0.9864 \| \| 0.9601 \| 48.52 \| 26300 \| 0.0073 \| 0.9865 \| \| 0.9881 \| 48.71 \| 26400 \| 0.0073 \| 0.9864 \| \| 0.9919 \| 48.89 \| 26500 \| 0.0073 \| 0.9864 \| \| 0.9748 \| 49.08 \| 26600 \| 0.0073 \| 0.9864 \| \| 0.9862 \| 49.26 \| 26700 \| 0.0073 \| 0.9864 \| \| 0.9935 \| 49.45 \| 26800 \| 0.0073 \| 0.9864 \| \| 0.9402 \| 49.63 \| 26900 \| 0.0073 \| 0.9864 \| \| 0.9982 \| 49.82 \| 27000 \| 0.0073 \| 0.9864 \| \| 0.9619 \| 50.0 \| 27100 \| 0.0073 \| 0.9864 \| \| 0.9935 \| 50.18 \| 27200 \| 0.0073 \| 0.9864 \| \| 0.9962 \| 50.37 \| 27300 \| 0.0073 \| 0.9864 \| \| 0.9888 \| 50.55 \| 27400 \| 0.0073 \| 0.9864 \| \| 0.9956 \| 50.74 \| 27500 \| 0.0073 \| 0.9864 \| \| 0.9981 \| 50.92 \| 27600 \| 0.0073 \| 0.9864 \| \| 0.9992 \| 51.11 \| 27700 \| 0.0073 \| 0.9865 \| \| 0.9613 \| 51.29 \| 27800 \| 0.0073 \| 0.9864 \| \| 0.9721 \| 51.48 \| 27900 \| 0.0072 \| 0.9865 \| \| 0.9938 \| 51.66 \| 28000 \| 0.0073 \| 0.9865 \| \| 0.9998 \| 51.85 \| 28100 \| 0.0073 \| 0.9864 \| \| 0.9981 \| 52.03 \| 28200 \| 0.0073 \| 0.9864 \| \| 0.9793 \| 52.21 \| 28300 \| 0.0073 \| 0.9864 \| \| 0.9962 \| 52.4 \| 28400 \| 0.0073 \| 0.9864 \| \| 0.9728 \| 52.58 \| 28500 \| 0.0073 \| 0.9864 \| \| 0.9965 \| 52.77 \| 28600 \| 0.0073 \| 0.9864 \| \| 0.9937 \| 52.95 \| 28700 \| 0.0073 \| 0.9864 \| \| 0.9942 \| 53.14 \| 28800 \| 0.0073 \| 0.9864 \| \| 0.9902 \| 53.32 \| 28900 \| 0.0073 \| 0.9864 \| \| 0.9992 \| 53.51 \| 29000 \| 0.0073 \| 0.9864 \| \| 0.9954 \| 53.69 \| 29100 \| 0.0073 \| 0.9864 \| \| 0.991 \| 53.87 \| 29200 \| 0.0073 \| 0.9864 \| \| 0.9955 \| 54.06 \| 29300 \| 0.0073 \| 0.9864 \| \| 0.9978 \| 54.24 \| 29400 \| 0.0073 \| 0.9864 \| \| 0.9998 \| 54.43 \| 29500 \| 0.0073 \| 0.9864 \| \| 0.9716 \| 54.61 \| 29600 \| 0.0073 \| 0.9864 \| \| 0.9891 \| 54.8 \| 29700 \| 0.0073 \| 0.9864 \| \| 0.9984 \| 54.98 \| 29800 \| 0.0073 \| 0.9864 \| \| 0.9756 \| 55.17 \| 29900 \| 0.0073 \| 0.9864 \| \| 0.9901 \| 55.35 \| 30000 \| 0.0073 \| 0.9864 \| \| 0.9866 \| 55.54 \| 30100 \| 0.0073 \| 0.9864 \| \| 0.9908 \| 55.72 \| 30200 \| 0.0073 \| 0.9864 \| \| 0.977 \| 55.9 \| 30300 \| 0.0073 \| 0.9864 \| \| 0.9882 \| 56.09 \| 30400 \| 0.0073 \| 0.9864 \| \| 0.9903 \| 56.27 \| 30500 \| 0.0073 \| 0.9864 \| \| 0.9819 \| 56.46 \| 30600 \| 0.0073 \| 0.9864 \| \| 0.9883 \| 56.64 \| 30700 \| 0.0073 \| 0.9864 \| \| 0.9922 \| 56.83 \| 30800 \| 0.0073 \| 0.9864 \| \| 0.9788 \| 57.01 \| 30900 \| 0.0073 \| 0.9864 \| \| 0.9756 \| 57.2 \| 31000 \| 0.0073 \| 0.9864 \| \| 0.9955 \| 57.38 \| 31100 \| 0.0073 \| 0.9864 \| \| 0.9925 \| 57.56 \| 31200 \| 0.0073 \| 0.9864 \| \| 0.9976 \| 57.75 \| 31300 \| 0.0073 \| 0.9864 \| \| 0.9938 \| 57.93 \| 31400 \| 0.0073 \| 0.9864 \| \| 0.9905 \| 58.12 \| 31500 \| 0.0073 \| 0.9864 \| \| 0.9819 \| 58.3 \| 31600 \| 0.0073 \| 0.9864 \| \| 0.9827 \| 58.49 \| 31700 \| 0.0073 \| 0.9864 \| \| 0.9927 \| 58.67 \| 31800 \| 0.0073 \| 0.9864 \| \| 0.9953 \| 58.86 \| 31900 \| 0.0073 \| 0.9864 \| \| 0.9937 \| 59.04 \| 32000 \| 0.0073 \| 0.9864 \| \| 0.9961 \| 59.23 \| 32100 \| 0.0073 \| 0.9864 \| \| 0.9886 \| 59.41 \| 32200 \| 0.0073 \| 0.9864 \| \| 0.9906 \| 59.59 \| 32300 \| 0.0073 \| 0.9864 \| \| 0.9811 \| 59.78 \| 32400 \| 0.0073 \| 0.9864 \| \| 0.9977 \| 59.96 \| 32500 \| 0.0073 \| 0.9864 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.2.0 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["vision", "image-segmentation", "generated_from_trainer"], "model-index": [{"name": "segformer-b3-from-scratch-final", "results": []}]}	image-segmentation	samitizerxu/segformer-b3-from-scratch-final	[ "transformers", "safetensors", "segformer", "vision", "image-segmentation", "generated_from_trainer", "endpoints_compatible", "region:us" ]	2024-02-10T15:12:23+00:00	[]	[]	TAGS #transformers #safetensors #segformer #vision #image-segmentation #generated_from_trainer #endpoints_compatible #region-us	segformer-b3-from-scratch-final =============================== This model is a fine-tuned version of [](URL on the samitizerxu/kelp\_data\_rgbagg\_swin\_nir\_int\_cleaned dataset. It achieves the following results on the evaluation set: * Iou Kelp: 0.0073 * Loss: 0.9864 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.001 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * num\_epochs: 60 ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.2.0 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 60", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #safetensors #segformer #vision #image-segmentation #generated_from_trainer #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 60", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 40, 98, 4, 30 ]	[ "passage: TAGS\n#transformers #safetensors #segformer #vision #image-segmentation #generated_from_trainer #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* num\\_epochs: 60### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.2.0\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.10411479324102402, -0.024431081488728523, -0.0007372432737611234, 0.0908518135547638, 0.19007684290409088, 0.02338247001171112, 0.11886221915483475, 0.07503367215394974, -0.14929988980293274, 0.036451105028390884, 0.11009279638528824, 0.12325144559144974, 0.0005903051933273673, 0.12402134388685226, -0.052381131798028946, -0.2636178433895111, 0.003956907894462347, 0.026624569669365883, -0.1192958652973175, 0.10110297054052353, 0.08882938325405121, -0.17813533544540405, 0.09114334732294083, -0.018495645374059677, -0.2787429988384247, 0.029100818559527397, 0.025015346705913544, -0.04960505664348602, 0.14140582084655762, 0.017427340149879456, 0.16098757088184357, -0.0011386952828615904, 0.1251050978899002, -0.1887296736240387, 0.01448590587824583, 0.06402226537466049, 0.020970122888684273, 0.04765007272362709, 0.042806755751371384, 0.002979532815515995, 0.07863244414329529, -0.11767889559268951, 0.08554273098707199, 0.007161071058362722, -0.14539551734924316, -0.21384993195533752, -0.06794679909944534, -0.005342388059943914, 0.1008441299200058, 0.09391631931066513, 0.00267092720605433, 0.12744803726673126, -0.08660151809453964, 0.10639353841543198, 0.26879093050956726, -0.19612064957618713, -0.09507046639919281, 0.035409051924943924, -0.002292563207447529, 0.08370207995176315, -0.1363281011581421, 0.013517259620130062, 0.08509475737810135, 0.0350724458694458, 0.10175902396440506, -0.028325237333774567, -0.11200372129678726, 0.008906246162950993, -0.13949032127857208, -0.02696003019809723, 0.09873814135789871, 0.04647519811987877, -0.027790378779172897, -0.006337453611195087, -0.07261738181114197, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"license": "apache-2.0", "library_name": "transformers"}	text-generation	yam-peleg/Experiment2-7B	[ "transformers", "safetensors", "mistral", "text-generation", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:26:05+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 64, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04166862368583679, 0.19294528663158417, -0.00565074710175395, 0.015576343052089214, 0.09740261733531952, 0.0018807778833433986, 0.05789901316165924, 0.11420097202062607, -0.05003552511334419, 0.12885801494121552, 0.04070472717285156, 0.10962796211242676, 0.11936872452497482, 0.1407015174627304, -0.003504571970552206, -0.2155151218175888, 0.04980916157364845, -0.1058453768491745, -0.01258739922195673, 0.12501691281795502, 0.14908315241336823, -0.0954088643193245, 0.06983769685029984, -0.03609218820929527, -0.016073228791356087, -0.0402071587741375, -0.060646165162324905, -0.041513413190841675, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "bigscience/bloom-3b"}	null	vmathur87/llm-support	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:bigscience/bloom-3b", "region:us" ]	2024-02-10T15:26:37+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-3b #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-3b #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 35, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-bigscience/bloom-3b #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.09975722432136536, 0.1893470585346222, -0.0036378027871251106, 0.03377080708742142, 0.08956268429756165, 0.01606217958033085, 0.04934121295809746, 0.12286069244146347, -0.03180153667926788, 0.10665926337242126, 0.07045666873455048, 0.10034925490617752, 0.0986793041229248, 0.21037167310714722, 0.010604910552501678, -0.20116940140724182, 0.02554682455956936, -0.09600938856601715, -0.014991069212555885, 0.1206747442483902, 0.1542518436908722, -0.09912464022636414, 0.0753534585237503, -0.018081873655319214, -0.014627320691943169, -0.03511117398738861, -0.0738551914691925, 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null	null	transformers	Copied from https://huggingface.co/susnato/phi-2 commit@9070ddb4fce238899ddbd2aca1faf6a0aeb6e444. Temporarily used for [Bunny](https://github.com/BAAI-DCAI/Bunny). ---------------------------------------------------- DISCLAIMER: I don't own the weights to this model, this is a property of Microsoft and taken from their official repository : [microsoft/phi-2](https://huggingface.co/microsoft/phi-2). The sole purpose of this repository is to use this model through the `transformers` API or to load and use the model using the HuggingFace `transformers` library. # Usage First make sure you have the latest version of the `transformers` installed. ``` pip install -U transformers ``` Then use the transformers library to load the model from the library itself ```python from transformers import AutoModelForCausalLM, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("susnato/phi-2") tokenizer = AutoTokenizer.from_pretrained("susnato/phi-2") inputs = tokenizer('''def print_prime(n): """ Print all primes between 1 and n """''', return_tensors="pt", return_attention_mask=False) outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ```	{}	text-generation	Isaachhe/phi-2_dev	[ "transformers", "pytorch", "phi", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T15:27:09+00:00	[]	[]	TAGS #transformers #pytorch #phi #text-generation #autotrain_compatible #endpoints_compatible #region-us	Copied from URL commit@9070ddb4fce238899ddbd2aca1faf6a0aeb6e444. Temporarily used for Bunny. ---------------------------------------------------- DISCLAIMER: I don't own the weights to this model, this is a property of Microsoft and taken from their official repository : microsoft/phi-2. The sole purpose of this repository is to use this model through the 'transformers' API or to load and use the model using the HuggingFace 'transformers' library. # Usage First make sure you have the latest version of the 'transformers' installed. Then use the transformers library to load the model from the library itself	[ "# Usage \n\nFirst make sure you have the latest version of the 'transformers' installed.\n\n\n\nThen use the transformers library to load the model from the library itself" ]	[ "TAGS\n#transformers #pytorch #phi #text-generation #autotrain_compatible #endpoints_compatible #region-us \n", "# Usage \n\nFirst make sure you have the latest version of the 'transformers' installed.\n\n\n\nThen use the transformers library to load the model from the library itself" ]	[ 36, 36 ]	[ "passage: TAGS\n#transformers #pytorch #phi #text-generation #autotrain_compatible #endpoints_compatible #region-us \n# Usage \n\nFirst make sure you have the latest version of the 'transformers' installed.\n\n\n\nThen use the transformers library to load the model from the library itself" ]	[ -0.050090741366147995, 0.04286512732505798, -0.003643230302259326, -0.026060571894049644, 0.20317041873931885, 0.030670875683426857, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	automatic-speech-recognition	Imran1/iraqii	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T15:30:51+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 45, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05983767658472061, 0.15663617849349976, -0.00414510490372777, 0.012621625326573849, 0.10675175487995148, 0.00396517850458622, 0.07058298587799072, 0.10818448662757874, -0.014333043247461319, 0.1301925629377365, 0.031459614634513855, 0.10620059072971344, 0.11486424505710602, 0.17755427956581116, -0.00021593451674561948, -0.21627318859100342, 0.06542544066905975, -0.11467250436544418, 0.023902224376797676, 0.1205042228102684, 0.14280648529529572, -0.10782013833522797, 0.0710505023598671, -0.02651231922209263, -0.014152529649436474, -0.030523719266057014, -0.05870387330651283, -0.06662651896476746, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # opt-1.3b-lora-3.15M-squad-model1 This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["varun-v-rao/squad"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-lora-3.15M-squad-model1", "results": []}]}	question-answering	varun-v-rao/opt-1.3b-lora-3.15M-squad-model1	[ "transformers", "tensorboard", "safetensors", "opt", "question-answering", "generated_from_trainer", "dataset:varun-v-rao/squad", "base_model:facebook/opt-1.3b", "license:other", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:32:31+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-1.3b #license-other #endpoints_compatible #text-generation-inference #region-us	# opt-1.3b-lora-3.15M-squad-model1 This model is a fine-tuned version of facebook/opt-1.3b on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 48 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.1+cu121 - Datasets 2.15.0 - Tokenizers 0.15.0	[ "# opt-1.3b-lora-3.15M-squad-model1\n\nThis model is a fine-tuned version of facebook/opt-1.3b on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ "TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-1.3b #license-other #endpoints_compatible #text-generation-inference #region-us \n", "# opt-1.3b-lora-3.15M-squad-model1\n\nThis model is a fine-tuned version of facebook/opt-1.3b on the squad dataset.", "## Model description\n\nMore information needed", "## Intended uses & limitations\n\nMore information needed", "## Training and evaluation data\n\nMore information needed", "## Training procedure", "### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3", "### Training results", "### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ 81, 42, 6, 12, 8, 3, 90, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #opt #question-answering #generated_from_trainer #dataset-varun-v-rao/squad #base_model-facebook/opt-1.3b #license-other #endpoints_compatible #text-generation-inference #region-us \n# opt-1.3b-lora-3.15M-squad-model1\n\nThis model is a fine-tuned version of facebook/opt-1.3b on the squad dataset.## Model description\n\nMore information needed## Intended uses & limitations\n\nMore information needed## Training and evaluation data\n\nMore information needed## Training procedure### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 2e-05\n- train_batch_size: 16\n- eval_batch_size: 16\n- seed: 48\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 3### Training results### Framework versions\n\n- Transformers 4.35.2\n- Pytorch 2.1.1+cu121\n- Datasets 2.15.0\n- Tokenizers 0.15.0" ]	[ 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null	null	mlx	<img src="https://l0d0v1c.github.io/notes/medias/greeks.jpg"/> # An Ancient Greek/French Model for RAG * based on mistralai/Mistral-7B-v0.1 * Trained on [1] (and automated translation by GTP4) and [2] * For RAG [3] * Focused on French / Ancient Greek relationship * may produce unaccurate results in translation * https://www.diogenial.com https://rd-mediation.com * Framework MLX Apple Silicon [4] ## Example ### Exemple dans le corpus ``` La traduction de Φασὶ δὲ καὶ Ἀλέξανδρον εἰπεῖν ὡς εἴπερ Ἀλέξανδρος μὴ ἐγεγόνει, ἐθελῆσαι ἂν Διογένης γενέσθαι est: Diogène dit que si Alexandre avait été moins ambitieux, il aurait bien voulu être Diogène. ``` (imprécise: On assure qu’Alexandre disait que s’il n’était pas Alexandre il voudrait être Diogène. selon [5]) ### Exemple hors corpus ``` La traduction de Γνῶθι σεαυτόν est Connais-toi toi-même. ``` ### Exemple de thème hors corpus ``` La traduction de la phrase: J'aime les machines en grec ancien est εἰμὶ δ' ἀγαπῶ τὰ μηχανήματα. ``` Ce qui signifie plus exactement "Je suis et j'aime les machines" ## Use ``` from mlx_lm import load, generate model, tokenizer = load("RANDMEDIATION/DiogenialRAG") response = generate(model, tokenizer, prompt=input("prompt:"), verbose=True,max_tokens=256,temp=0.8) ``` ## References [1] Giannantoni, Gabriele, éd. Socratis et Socraticorum reliquiae. Elenchos 18. Book V. Naples, Italy: Bibliopolis, 1990. [2] https://tatoeba.org/fr/sentences/show_all_in/grc/none [3] Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, et al. « Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ». In Advances in Neural Information Processing Systems, édité par H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, et H. Lin, 33:9459‑74. Curran Associates, Inc., 2020. https://proceedings.neurips.cc/paper_files/paper/2020/file/6b493230205f780e1bc26945df7481e5-Paper.pdf. [4] Hannun, Awni, Jagrit Digani, Angelos Katharopoulos, et Ronan Collobert. « MLX: Efficient and flexible machine learning on Apple silicon », 2023. https://github.com/ml-explore. [5] https://remacle.org/bloodwolf/philosophes/laerce/6diogene1.htm	{"language": ["fr", "el"], "license": "mit", "library_name": "mlx", "tags": ["mlx", "mistral-7b", "Ancient greek"], "pipeline_tag": "text-generation"}	text-generation	RANDMEDIATION/DiogenialRAG	[ "mlx", "mistral-7b", "Ancient greek", "text-generation", "fr", "el", "doi:10.57967/hf/1758", "license:mit", "region:us" ]	2024-02-10T15:33:14+00:00	[]	[ "fr", "el" ]	TAGS #mlx #mistral-7b #Ancient greek #text-generation #fr #el #doi-10.57967/hf/1758 #license-mit #region-us	<img src="URL # An Ancient Greek/French Model for RAG * based on mistralai/Mistral-7B-v0.1 * Trained on [1] (and automated translation by GTP4) and [2] * For RAG [3] * Focused on French / Ancient Greek relationship * may produce unaccurate results in translation * URL URL * Framework MLX Apple Silicon [4] ## Example ### Exemple dans le corpus (imprécise: On assure qu’Alexandre disait que s’il n’était pas Alexandre il voudrait être Diogène. selon [5]) ### Exemple hors corpus ### Exemple de thème hors corpus Ce qui signifie plus exactement "Je suis et j'aime les machines" ## Use ## References [1] Giannantoni, Gabriele, éd. Socratis et Socraticorum reliquiae. Elenchos 18. Book V. Naples, Italy: Bibliopolis, 1990. [2] URL [3] Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, et al. « Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ». In Advances in Neural Information Processing Systems, édité par H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, et H. Lin, 33:9459‑74. Curran Associates, Inc., 2020. URL [4] Hannun, Awni, Jagrit Digani, Angelos Katharopoulos, et Ronan Collobert. « MLX: Efficient and flexible machine learning on Apple silicon », 2023. URL [5] URL	[ "# An Ancient Greek/French Model for RAG\n\n* based on mistralai/Mistral-7B-v0.1\n* Trained on [1] (and automated translation by GTP4) and [2]\n* For RAG [3]\n* Focused on French / Ancient Greek relationship\n* may produce unaccurate results in translation\n* URL URL\n* Framework MLX Apple Silicon [4]", "## Example", "### Exemple dans le corpus\n\n\n(imprécise: On assure qu’Alexandre disait que s’il n’était pas Alexandre il voudrait être Diogène. selon [5])", "### Exemple hors corpus", "### Exemple de thème hors corpus\n\n\nCe qui signifie plus exactement \"Je suis et j'aime les machines\"", "## Use", "## References\n[1] Giannantoni, Gabriele, éd. Socratis et Socraticorum reliquiae. Elenchos 18. Book V. Naples, Italy: Bibliopolis, 1990.\n\n[2] URL\n\n[3] Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, et al. « Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ». In Advances in Neural Information Processing Systems, édité par H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, et H. Lin, 33:9459‑74. Curran Associates, Inc., 2020. URL\n\n[4] Hannun, Awni, Jagrit Digani, Angelos Katharopoulos, et Ronan Collobert. « MLX: Efficient and flexible machine learning on Apple silicon », 2023. URL\n\n[5] URL" ]	[ "TAGS\n#mlx #mistral-7b #Ancient greek #text-generation #fr #el #doi-10.57967/hf/1758 #license-mit #region-us \n", "# An Ancient Greek/French Model for RAG\n\n* based on mistralai/Mistral-7B-v0.1\n* Trained on [1] (and automated translation by GTP4) and [2]\n* For RAG [3]\n* Focused on French / Ancient Greek relationship\n* may produce unaccurate results in translation\n* URL URL\n* Framework MLX Apple Silicon [4]", "## Example", "### Exemple dans le corpus\n\n\n(imprécise: On assure qu’Alexandre disait que s’il n’était pas Alexandre il voudrait être Diogène. selon [5])", "### Exemple hors corpus", "### Exemple de thème hors corpus\n\n\nCe qui signifie plus exactement \"Je suis et j'aime les machines\"", "## Use", "## References\n[1] Giannantoni, Gabriele, éd. Socratis et Socraticorum reliquiae. Elenchos 18. Book V. Naples, Italy: Bibliopolis, 1990.\n\n[2] URL\n\n[3] Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, et al. « Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ». In Advances in Neural Information Processing Systems, édité par H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, et H. Lin, 33:9459‑74. Curran Associates, Inc., 2020. URL\n\n[4] Hannun, Awni, Jagrit Digani, Angelos Katharopoulos, et Ronan Collobert. « MLX: Efficient and flexible machine learning on Apple silicon », 2023. URL\n\n[5] URL" ]	[ 45, 78, 3, 42, 7, 25, 2, 212 ]	[ "passage: TAGS\n#mlx #mistral-7b #Ancient greek #text-generation #fr #el #doi-10.57967/hf/1758 #license-mit #region-us \n# An Ancient Greek/French Model for RAG\n\n* based on mistralai/Mistral-7B-v0.1\n* Trained on [1] (and automated translation by GTP4) and [2]\n* For RAG [3]\n* Focused on French / Ancient Greek relationship\n* may produce unaccurate results in translation\n* URL URL\n* Framework MLX Apple Silicon [4]## Example### Exemple dans le corpus\n\n\n(imprécise: On assure qu’Alexandre disait que s’il n’était pas Alexandre il voudrait être Diogène. selon [5])### Exemple hors corpus### Exemple de thème hors corpus\n\n\nCe qui signifie plus exactement \"Je suis et j'aime les machines\"## Use## References\n[1] Giannantoni, Gabriele, éd. Socratis et Socraticorum reliquiae. Elenchos 18. Book V. Naples, Italy: Bibliopolis, 1990.\n\n[2] URL\n\n[3] Lewis, Patrick, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, et al. « Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks ». In Advances in Neural Information Processing Systems, édité par H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, et H. Lin, 33:9459‑74. Curran Associates, Inc., 2020. URL\n\n[4] Hannun, Awni, Jagrit Digani, Angelos Katharopoulos, et Ronan Collobert. « MLX: Efficient and flexible machine learning on Apple silicon », 2023. 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null	null	transformers	# LoftQ Initialization \| [Paper](https://arxiv.org/abs/2310.08659) \| [Code](https://github.com/yxli2123/LoftQ) \| [PEFT Example](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning) \| LoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W. This model, `Mistral-7B-v0.1-4bit-64rank`, is obtained from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1). The backbone is under `LoftQ/Mistral-7B-v0.1-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`. ## Model Info ### Backbone - Stored format: `torch.bfloat16` - Size: ~ 14 GiB - Loaded format: bitsandbytes nf4 - Size loaded on GPU: ~3.5 GiB ### LoRA adapters - rank: 64 - lora_alpha: 16 - target_modules: ["down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj"] ## Usage Training. Here's an example of loading this model and preparing for the LoRA fine-tuning. ```python import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel MODEL_ID = "LoftQ/Mistral-7B-v0.1-4bit-64rank" base_model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, # you may change it with different models quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, # bfloat16 is recommended bnb_4bit_use_double_quant=False, bnb_4bit_quant_type='nf4', ), ) peft_model = PeftModel.from_pretrained( base_model, MODEL_ID, subfolder="loftq_init", is_trainable=True, ) # Do training with peft_model ... ``` Inference. Here is an example code for inference after the model has been fine-tuned on [GSM8K](https://huggingface.co/datasets/gsm8k). ```python import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel MODEL_ID = "LoftQ/Mistral-7B-v0.1-4bit-64rank" base_model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, # you may change it with different models quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, # bfloat16 is recommended bnb_4bit_use_double_quant=False, bnb_4bit_quant_type='nf4', ), ) peft_model = PeftModel.from_pretrained( base_model, MODEL_ID, subfolder="gsm8k", is_trainable=True, ) # Do inference with peft_model ... ``` See the full code at our [Github Repo]((https://github.com/yxli2123/LoftQ)) ## Citation ```bibtex @article{li2023loftq, title={Loftq: Lora-fine-tuning-aware quantization for large language models}, author={Li, Yixiao and Yu, Yifan and Liang, Chen and He, Pengcheng and Karampatziakis, Nikos and Chen, Weizhu and Zhao, Tuo}, journal={arXiv preprint arXiv:2310.08659}, year={2023} } ```	{"language": ["en"], "license": "mit", "tags": ["quantization ", "lora"], "pipeline_tag": "text-generation"}	text-generation	ChuGyouk/Mistral-7B-v0.1-4bit-64rank	[ "transformers", "safetensors", "mistral", "text-generation", "quantization ", "lora", "en", "arxiv:2310.08659", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:34:18+00:00	[ "2310.08659" ]	[ "en" ]	TAGS #transformers #safetensors #mistral #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# LoftQ Initialization \| Paper \| Code \| PEFT Example \| LoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W. This model, 'Mistral-7B-v0.1-4bit-64rank', is obtained from Mistral-7B-v0.1. The backbone is under 'LoftQ/Mistral-7B-v0.1-4bit-64rank' and LoRA adapters are under the 'subfolder='loftq_init''. ## Model Info ### Backbone - Stored format: 'torch.bfloat16' - Size: ~ 14 GiB - Loaded format: bitsandbytes nf4 - Size loaded on GPU: ~3.5 GiB ### LoRA adapters - rank: 64 - lora_alpha: 16 - target_modules: ["down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj"] ## Usage Training. Here's an example of loading this model and preparing for the LoRA fine-tuning. Inference. Here is an example code for inference after the model has been fine-tuned on GSM8K. See the full code at our Github Repo)	[ "# LoftQ Initialization\n\n\| Paper \| Code \| PEFT Example \|\n\nLoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W.\n\nThis model, 'Mistral-7B-v0.1-4bit-64rank', is obtained from Mistral-7B-v0.1. \nThe backbone is under 'LoftQ/Mistral-7B-v0.1-4bit-64rank' and LoRA adapters are under the 'subfolder='loftq_init''.", "## Model Info", "### Backbone\n- Stored format: 'torch.bfloat16'\n- Size: ~ 14 GiB\n- Loaded format: bitsandbytes nf4\n- Size loaded on GPU: ~3.5 GiB", "### LoRA adapters\n- rank: 64\n- lora_alpha: 16\n- target_modules: [\"down_proj\", \"up_proj\", \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\"]", "## Usage\n\nTraining. Here's an example of loading this model and preparing for the LoRA fine-tuning.\n\n\n\nInference. Here is an example code for inference after the model has been fine-tuned on GSM8K.\n\n\n\nSee the full code at our Github Repo)" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# LoftQ Initialization\n\n\| Paper \| Code \| PEFT Example \|\n\nLoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W.\n\nThis model, 'Mistral-7B-v0.1-4bit-64rank', is obtained from Mistral-7B-v0.1. \nThe backbone is under 'LoftQ/Mistral-7B-v0.1-4bit-64rank' and LoRA adapters are under the 'subfolder='loftq_init''.", "## Model Info", "### Backbone\n- Stored format: 'torch.bfloat16'\n- Size: ~ 14 GiB\n- Loaded format: bitsandbytes nf4\n- Size loaded on GPU: ~3.5 GiB", "### LoRA adapters\n- rank: 64\n- lora_alpha: 16\n- target_modules: [\"down_proj\", \"up_proj\", \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\"]", "## Usage\n\nTraining. Here's an example of loading this model and preparing for the LoRA fine-tuning.\n\n\n\nInference. Here is an example code for inference after the model has been fine-tuned on GSM8K.\n\n\n\nSee the full code at our Github Repo)" ]	[ 69, 140, 3, 49, 68, 65 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# LoftQ Initialization\n\n\| Paper \| Code \| PEFT Example \|\n\nLoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W.\n\nThis model, 'Mistral-7B-v0.1-4bit-64rank', is obtained from Mistral-7B-v0.1. \nThe backbone is under 'LoftQ/Mistral-7B-v0.1-4bit-64rank' and LoRA adapters are under the 'subfolder='loftq_init''.## Model Info### Backbone\n- Stored format: 'torch.bfloat16'\n- Size: ~ 14 GiB\n- Loaded format: bitsandbytes nf4\n- Size loaded on GPU: ~3.5 GiB### LoRA adapters\n- rank: 64\n- lora_alpha: 16\n- target_modules: [\"down_proj\", \"up_proj\", \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\"]## Usage\n\nTraining. Here's an example of loading this model and preparing for the LoRA fine-tuning.\n\n\n\nInference. Here is an example code for inference after the model has been fine-tuned on GSM8K.\n\n\n\nSee the full code at our Github Repo)" ]	[ -0.019296912476420403, 0.058481138199567795, -0.0029299550224095583, 0.08913679420948029, 0.10317570716142654, 0.027502182871103287, 0.08255332708358765, 0.14007462561130524, 0.015446123667061329, 0.11252390593290329, 0.05278438702225685, 0.09099206328392029, 0.11020182073116302, 0.16527986526489258, -0.011329758912324905, -0.20665962994098663, 0.0038374164141714573, -0.04317738115787506, 0.035542797297239304, 0.07518590986728668, 0.12384320795536041, -0.019196733832359314, 0.09108362346887589, -0.028890442103147507, -0.06895099580287933, -0.016004642471671104, -0.015839381143450737, -0.015046450309455395, 0.060150377452373505, 0.09890823811292648, 0.055793099105358124, -0.05026509240269661, 0.0677642971277237, -0.22091667354106903, 0.016042761504650116, 0.10858990252017975, 0.0033525677863508463, 0.0936117097735405, 0.049725133925676346, 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null	null	transformers	# LoftQ Initialization \| [Paper](https://arxiv.org/abs/2310.08659) \| [Code](https://github.com/yxli2123/LoftQ) \| [PEFT Example](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning) \| LoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W. This model, `Llama-2-7b-hf-4bit-64rank`, is obtained from [LLAMA-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf). The backbone is under `LoftQ/Llama-2-7b-hf-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`. ## Model Info ### Backbone - Stored format: `torch.bfloat16` - Size: ~ 14 GiB - Loaded format: bitsandbytes nf4 - Size loaded on GPU: ~3.5 GiB ### LoRA adapters - rank: 64 - lora_alpha: 16 - target_modules: ["down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj"] ## Usage Training Here's an example of loading this model and preparing for the LoRA fine-tuning. ```python import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel MODEL_ID = "LoftQ/Llama-2-7b-hf-4bit-64rank" base_model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, # you may change it with different models quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, # bfloat16 is recommended bnb_4bit_use_double_quant=False, bnb_4bit_quant_type='nf4', ), ) peft_model = PeftModel.from_pretrained( base_model, MODEL_ID, subfolder="loftq_init", is_trainable=True, ) # Do training with peft_model ... ``` ## Experiment Results We have conducted experiments on supervised fine-tuning of [GSM8K](https://huggingface.co/datasets/gsm8k) and [WikiText-2](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1). \| Model \| Bits \| Rank \| LoRA Initial \| GSM8K \| WikiText-2 \| \| -------------- \| ---- \| ---- \| -------------------- \| ----- \| ---------- \| \| LLAMA-2-7b \| 16 \| 64 \| Gaussian + 0 \| 36.9 \| 5.08 \| \| LLAMA-2-7b \| 4 \| 64 \| Gaussian + 0 (QLoRA) \| 35.1 \| 5.70 \| \| LLAMA-2-7b \| 4 \| 64 \| LoftQ \| 35.0 \| 5.24 \| Inference Here is an example code for inference after the model has been fine-tuned on [GSM8K](https://huggingface.co/datasets/gsm8k). ```python import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel MODEL_ID = "LoftQ/Llama-2-7b-hf-4bit-64rank" base_model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, # you may change it with different models quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, # bfloat16 is recommended bnb_4bit_use_double_quant=False, bnb_4bit_quant_type='nf4', ), ) peft_model = PeftModel.from_pretrained( base_model, MODEL_ID, subfolder="gsm8k", is_trainable=True, ) # Do inference with peft_model ... ``` See the full code at our [Github Repo]((https://github.com/yxli2123/LoftQ)) ## Citation ```bibtex @article{li2023loftq, title={Loftq: Lora-fine-tuning-aware quantization for large language models}, author={Li, Yixiao and Yu, Yifan and Liang, Chen and He, Pengcheng and Karampatziakis, Nikos and Chen, Weizhu and Zhao, Tuo}, journal={arXiv preprint arXiv:2310.08659}, year={2023} } ```	{"language": ["en"], "license": "mit", "tags": ["quantization ", "lora"], "pipeline_tag": "text-generation"}	text-generation	ChuGyouk/Llama-2-7b-hf-4bit-64rank	[ "transformers", "safetensors", "llama", "text-generation", "quantization ", "lora", "en", "arxiv:2310.08659", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:35:34+00:00	[ "2310.08659" ]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	LoftQ Initialization ==================== \| Paper \| Code \| PEFT Example \| LoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W. This model, 'Llama-2-7b-hf-4bit-64rank', is obtained from LLAMA-2-7b. The backbone is under 'LoftQ/Llama-2-7b-hf-4bit-64rank' and LoRA adapters are under the 'subfolder='loftq\_init''. Model Info ---------- ### Backbone * Stored format: 'torch.bfloat16' * Size: ~ 14 GiB * Loaded format: bitsandbytes nf4 * Size loaded on GPU: ~3.5 GiB ### LoRA adapters * rank: 64 * lora\_alpha: 16 * target\_modules: ["down\_proj", "up\_proj", "q\_proj", "k\_proj", "v\_proj", "o\_proj", "gate\_proj"] Usage ----- Training Here's an example of loading this model and preparing for the LoRA fine-tuning. Experiment Results ------------------ We have conducted experiments on supervised fine-tuning of GSM8K and WikiText-2. Inference Here is an example code for inference after the model has been fine-tuned on GSM8K. See the full code at our Github Repo)	[ "### Backbone\n\n\n* Stored format: 'torch.bfloat16'\n* Size: ~ 14 GiB\n* Loaded format: bitsandbytes nf4\n* Size loaded on GPU: ~3.5 GiB", "### LoRA adapters\n\n\n* rank: 64\n* lora\\_alpha: 16\n* target\\_modules: [\"down\\_proj\", \"up\\_proj\", \"q\\_proj\", \"k\\_proj\", \"v\\_proj\", \"o\\_proj\", \"gate\\_proj\"]\n\n\nUsage\n-----\n\n\nTraining Here's an example of loading this model and preparing for the LoRA fine-tuning.\n\n\nExperiment Results\n------------------\n\n\nWe have conducted experiments on supervised fine-tuning of GSM8K\nand WikiText-2.\n\n\n\nInference Here is an example code for inference after the model has been fine-tuned on GSM8K.\n\n\nSee the full code at our Github Repo)" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Backbone\n\n\n* Stored format: 'torch.bfloat16'\n* Size: ~ 14 GiB\n* Loaded format: bitsandbytes nf4\n* Size loaded on GPU: ~3.5 GiB", "### LoRA adapters\n\n\n* rank: 64\n* lora\\_alpha: 16\n* target\\_modules: [\"down\\_proj\", \"up\\_proj\", \"q\\_proj\", \"k\\_proj\", \"v\\_proj\", \"o\\_proj\", \"gate\\_proj\"]\n\n\nUsage\n-----\n\n\nTraining Here's an example of loading this model and preparing for the LoRA fine-tuning.\n\n\nExperiment Results\n------------------\n\n\nWe have conducted experiments on supervised fine-tuning of GSM8K\nand WikiText-2.\n\n\n\nInference Here is an example code for inference after the model has been fine-tuned on GSM8K.\n\n\nSee the full code at our Github Repo)" ]	[ 69, 49, 168 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Backbone\n\n\n* Stored format: 'torch.bfloat16'\n* Size: ~ 14 GiB\n* Loaded format: bitsandbytes 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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]	{"library_name": "transformers", "tags": []}	text-generation	aidonuts/marigold-007-ep2	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:35:45+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	Novin-AI/MeduWen-S	[ "transformers", "safetensors", "qwen2", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T15:35:51+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 52, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.06744060665369034, 0.1237388551235199, -0.004114609677344561, 0.02991606667637825, 0.11460870504379272, 0.005568372085690498, 0.06294357031583786, 0.10971193760633469, -0.026014693081378937, 0.11581014841794968, 0.018924949690699577, 0.10499268025159836, 0.10659246146678925, 0.1691424399614334, -0.006015846040099859, -0.21231532096862793, 0.044865865260362625, -0.13380737602710724, -0.025073938071727753, 0.11961860954761505, 0.13043774664402008, -0.12202122807502747, 0.06986955553293228, -0.03994565084576607, -0.009295043535530567, -0.0361013263463974, -0.05820033326745033, -0.04808541759848595, 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null	null	transformers	# LoftQ Initialization \| [Paper](https://arxiv.org/abs/2310.08659) \| [Code](https://github.com/yxli2123/LoftQ) \| [PEFT Example](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning) \| LoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W. This model, `Llama-2-13b-hf-4bit-64rank`, is obtained from [LLAMA-2-13b](https://huggingface.co/meta-llama/Llama-2-13b-hf). The backbone is under `LoftQ/Llama-2-13b-hf-4bit-64rank` and LoRA adapters are under the `subfolder='loftq_init'`. ## Model Info ### Backbone - Stored format: `torch.bfloat16` - Size: ~ 26 GiB - Loaded format: bitsandbytes nf4 - Size loaded on GPU: ~6.5 GiB ### LoRA adapters - rank: 64 - lora_alpha: 64 - target_modules: ["down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj"] ## Usage Training Here's an example of loading this model and preparing for the LoRA fine-tuning. ```python import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel MODEL_ID = "LoftQ/Llama-2-13b-hf-4bit-64rank" base_model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, # you may change it with different models quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, # bfloat16 is recommended bnb_4bit_use_double_quant=False, bnb_4bit_quant_type='nf4', ), ) peft_model = PeftModel.from_pretrained( base_model, MODEL_ID, subfolder="loftq_init", is_trainable=True, ) # Do training with peft_model ... ``` ## Experiment Results We have conducted experiments on supervised fine-tuning of [GSM8K](https://huggingface.co/datasets/gsm8k) and [WikiText-2](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-raw-v1). \| Model \| Bits \| Rank \| LoRA Initial \| GSM8K \| WikiText-2 \| \| -------------- \| ---- \| ---- \| -------------------- \| ----- \| ---------- \| \| LLAMA-2-13b \| 16 \| 64 \| Gaussian + 0 \| 45.3 \| 5.12 \| \| LLAMA-2-13b \| 4 \| 64 \| Gaussian + 0 (QLoRA) \| 39.9 \| 5.22 \| \| LLAMA-2-13b \| 4 \| 64 \| LoftQ \| 45.0 \| 5.16 \| Inference Here is an example code for inference after the model has been fine-tuned on [GSM8K](https://huggingface.co/datasets/gsm8k). ```python import torch from transformers import AutoModelForCausalLM, BitsAndBytesConfig from peft import PeftModel MODEL_ID = "LoftQ/Llama-2-13b-hf-4bit-64rank" base_model = AutoModelForCausalLM.from_pretrained( MODEL_ID, torch_dtype=torch.bfloat16, # you may change it with different models quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, # bfloat16 is recommended bnb_4bit_use_double_quant=False, bnb_4bit_quant_type='nf4', ), ) peft_model = PeftModel.from_pretrained( base_model, MODEL_ID, subfolder="gsm8k", is_trainable=True, ) # Do inference with peft_model ... ``` See the full code at our [Github Repo]((https://github.com/yxli2123/LoftQ)) ## Citation ```bibtex @article{li2023loftq, title={Loftq: Lora-fine-tuning-aware quantization for large language models}, author={Li, Yixiao and Yu, Yifan and Liang, Chen and He, Pengcheng and Karampatziakis, Nikos and Chen, Weizhu and Zhao, Tuo}, journal={arXiv preprint arXiv:2310.08659}, year={2023} } ```	{"language": ["en"], "license": "mit", "tags": ["quantization ", "lora"], "pipeline_tag": "text-generation"}	text-generation	ChuGyouk/Llama-2-13b-hf-4bit-64rank	[ "transformers", "safetensors", "llama", "text-generation", "quantization ", "lora", "en", "arxiv:2310.08659", "license:mit", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:36:08+00:00	[ "2310.08659" ]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	LoftQ Initialization ==================== \| Paper \| Code \| PEFT Example \| LoftQ (LoRA-fine-tuning-aware Quantization) provides a quantized backbone Q and LoRA adapters A and B, given a full-precision pre-trained weight W. This model, 'Llama-2-13b-hf-4bit-64rank', is obtained from LLAMA-2-13b. The backbone is under 'LoftQ/Llama-2-13b-hf-4bit-64rank' and LoRA adapters are under the 'subfolder='loftq\_init''. Model Info ---------- ### Backbone * Stored format: 'torch.bfloat16' * Size: ~ 26 GiB * Loaded format: bitsandbytes nf4 * Size loaded on GPU: ~6.5 GiB ### LoRA adapters * rank: 64 * lora\_alpha: 64 * target\_modules: ["down\_proj", "up\_proj", "q\_proj", "k\_proj", "v\_proj", "o\_proj", "gate\_proj"] Usage ----- Training Here's an example of loading this model and preparing for the LoRA fine-tuning. Experiment Results ------------------ We have conducted experiments on supervised fine-tuning of GSM8K and WikiText-2. Inference Here is an example code for inference after the model has been fine-tuned on GSM8K. See the full code at our Github Repo)	[ "### Backbone\n\n\n* Stored format: 'torch.bfloat16'\n* Size: ~ 26 GiB\n* Loaded format: bitsandbytes nf4\n* Size loaded on GPU: ~6.5 GiB", "### LoRA adapters\n\n\n* rank: 64\n* lora\\_alpha: 64\n* target\\_modules: [\"down\\_proj\", \"up\\_proj\", \"q\\_proj\", \"k\\_proj\", \"v\\_proj\", \"o\\_proj\", \"gate\\_proj\"]\n\n\nUsage\n-----\n\n\nTraining Here's an example of loading this model and preparing for the LoRA fine-tuning.\n\n\nExperiment Results\n------------------\n\n\nWe have conducted experiments on supervised fine-tuning of GSM8K\nand WikiText-2.\n\n\n\nInference Here is an example code for inference after the model has been fine-tuned on GSM8K.\n\n\nSee the full code at our Github Repo)" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Backbone\n\n\n* Stored format: 'torch.bfloat16'\n* Size: ~ 26 GiB\n* Loaded format: bitsandbytes nf4\n* Size loaded on GPU: ~6.5 GiB", "### LoRA adapters\n\n\n* rank: 64\n* lora\\_alpha: 64\n* target\\_modules: [\"down\\_proj\", \"up\\_proj\", \"q\\_proj\", \"k\\_proj\", \"v\\_proj\", \"o\\_proj\", \"gate\\_proj\"]\n\n\nUsage\n-----\n\n\nTraining Here's an example of loading this model and preparing for the LoRA fine-tuning.\n\n\nExperiment Results\n------------------\n\n\nWe have conducted experiments on supervised fine-tuning of GSM8K\nand WikiText-2.\n\n\n\nInference Here is an example code for inference after the model has been fine-tuned on GSM8K.\n\n\nSee the full code at our Github Repo)" ]	[ 69, 49, 168 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #quantization #lora #en #arxiv-2310.08659 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Backbone\n\n\n* Stored format: 'torch.bfloat16'\n* Size: ~ 26 GiB\n* Loaded format: bitsandbytes 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null	null	stable-baselines3	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```	{"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "261.49 +/- 16.32", "name": "mean_reward", "verified": false}]}]}]}	reinforcement-learning	michaelcheung/ppo-LunarLander-v2	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	2024-02-10T15:51:40+00:00	[]	[]	TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us	# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code	[ "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ "TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n", "# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.", "## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 39, 41, 17 ]	[ "passage: TAGS\n#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us \n# PPO Agent playing LunarLander-v2\nThis is a trained model of a PPO agent playing LunarLander-v2\nusing the stable-baselines3 library.## Usage (with Stable-baselines3)\nTODO: Add your code" ]	[ 0.03942384943366051, 0.04900386184453964, -0.005304091144353151, 0.026427261531352997, 0.107408307492733, -0.026511888951063156, 0.11188238859176636, 0.0814051404595375, 0.10722193866968155, 0.04762078449130058, 0.08338645845651627, 0.06030960753560066, 0.05080918222665787, 0.2571701407432556, 0.04754156619310379, -0.22987541556358337, 0.036159250885248184, -0.04869936779141426, 0.12395193427801132, 0.07178173214197159, -0.0038484656251966953, -0.06485428661108017, 0.020415637642145157, -0.013290755450725555, 0.05367108806967735, 0.04282612353563309, -0.01716216839849949, -0.08207534998655319, 0.07169748842716217, -0.06345846503973007, 0.06986866891384125, 0.07677983492612839, 0.13218913972377777, -0.17832116782665253, 0.029566360637545586, 0.02571309357881546, 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null	null	transformers	# capybagel-7B-slerp capybagel-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [jondurbin/bagel-dpo-7b-v0.4](https://huggingface.co/jondurbin/bagel-dpo-7b-v0.4) * [argilla/CapybaraHermes-2.5-Mistral-7B](https://huggingface.co/argilla/CapybaraHermes-2.5-Mistral-7B) ## 🧩 Configuration ```yaml slices: - sources: - model: jondurbin/bagel-dpo-7b-v0.4 layer_range: [0, 32] - model: argilla/CapybaraHermes-2.5-Mistral-7B layer_range: [0, 32] merge_method: slerp base_model: jondurbin/bagel-dpo-7b-v0.4 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "jacobi/capybagel-7B-slerp" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```	{"tags": ["merge", "mergekit", "lazymergekit", "jondurbin/bagel-dpo-7b-v0.4", "argilla/CapybaraHermes-2.5-Mistral-7B"], "base_model": ["jondurbin/bagel-dpo-7b-v0.4", "argilla/CapybaraHermes-2.5-Mistral-7B"]}	text-generation	jacobi/capybagel-7B-slerp	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "jondurbin/bagel-dpo-7b-v0.4", "argilla/CapybaraHermes-2.5-Mistral-7B", "conversational", "base_model:jondurbin/bagel-dpo-7b-v0.4", "base_model:argilla/CapybaraHermes-2.5-Mistral-7B", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:53:07+00:00	[]	[]	TAGS #transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #jondurbin/bagel-dpo-7b-v0.4 #argilla/CapybaraHermes-2.5-Mistral-7B #conversational #base_model-jondurbin/bagel-dpo-7b-v0.4 #base_model-argilla/CapybaraHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# capybagel-7B-slerp capybagel-7B-slerp is a merge of the following models using LazyMergekit: * jondurbin/bagel-dpo-7b-v0.4 * argilla/CapybaraHermes-2.5-Mistral-7B ## Configuration ## Usage	[ "# capybagel-7B-slerp\n\ncapybagel-7B-slerp is a merge of the following models using LazyMergekit:\n* jondurbin/bagel-dpo-7b-v0.4\n* argilla/CapybaraHermes-2.5-Mistral-7B", "## Configuration", "## Usage" ]	[ "TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #jondurbin/bagel-dpo-7b-v0.4 #argilla/CapybaraHermes-2.5-Mistral-7B #conversational #base_model-jondurbin/bagel-dpo-7b-v0.4 #base_model-argilla/CapybaraHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# capybagel-7B-slerp\n\ncapybagel-7B-slerp is a merge of the following models using LazyMergekit:\n* jondurbin/bagel-dpo-7b-v0.4\n* argilla/CapybaraHermes-2.5-Mistral-7B", "## Configuration", "## Usage" ]	[ 134, 62, 4, 3 ]	[ "passage: TAGS\n#transformers #safetensors #mistral #text-generation #merge #mergekit #lazymergekit #jondurbin/bagel-dpo-7b-v0.4 #argilla/CapybaraHermes-2.5-Mistral-7B #conversational #base_model-jondurbin/bagel-dpo-7b-v0.4 #base_model-argilla/CapybaraHermes-2.5-Mistral-7B #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# capybagel-7B-slerp\n\ncapybagel-7B-slerp is a merge of the following models using LazyMergekit:\n* jondurbin/bagel-dpo-7b-v0.4\n* argilla/CapybaraHermes-2.5-Mistral-7B## Configuration## Usage" ]	[ -0.07912690192461014, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-yoruba-lora-text-classification This model is a fine-tuned version of [Davlan/xlm-roberta-base-finetuned-yoruba](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-yoruba) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5843 - Precision: 0.6850 - Recall: 1.0 - F1 and accuracy: {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 and accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:----------------------------------------------------------:\| \| No log \| 1.0 \| 388 \| 0.6314 \| 0.6710 \| 1.0 \| {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} \| \| 0.6474 \| 2.0 \| 776 \| 0.6289 \| 0.6710 \| 1.0 \| {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} \| \| 0.652 \| 3.0 \| 1164 \| 0.6270 \| 0.6710 \| 1.0 \| {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} \| \| 0.646 \| 4.0 \| 1552 \| 0.6235 \| 0.6710 \| 1.0 \| {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} \| \| 0.646 \| 5.0 \| 1940 \| 0.6188 \| 0.6710 \| 1.0 \| {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} \| \| 0.6459 \| 6.0 \| 2328 \| 0.6092 \| 0.6710 \| 1.0 \| {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} \| \| 0.6281 \| 7.0 \| 2716 \| 0.5958 \| 0.6710 \| 1.0 \| {'accuracy': 0.6709511568123393, 'f1': 0.8030769230769231} \| \| 0.6268 \| 8.0 \| 3104 \| 0.5912 \| 0.6727 \| 1.0 \| {'accuracy': 0.6735218508997429, 'f1': 0.8043143297380586} \| \| 0.6268 \| 9.0 \| 3492 \| 0.5861 \| 0.6850 \| 1.0 \| {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} \| \| 0.6105 \| 10.0 \| 3880 \| 0.5843 \| 0.6850 \| 1.0 \| {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "Davlan/xlm-roberta-base-finetuned-yoruba", "model-index": [{"name": "xlm-roberta-base-finetuned-yoruba-lora-text-classification", "results": []}]}	null	nhankins/yo_xlmroberta_lora_adapter_1.0	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:Davlan/xlm-roberta-base-finetuned-yoruba", "region:us" ]	2024-02-10T15:55:27+00:00	[]	[]	TAGS #tensorboard #safetensors #generated_from_trainer #base_model-Davlan/xlm-roberta-base-finetuned-yoruba #region-us	xlm-roberta-base-finetuned-yoruba-lora-text-classification ========================================================== This model is a fine-tuned version of Davlan/xlm-roberta-base-finetuned-yoruba on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.5843 * Precision: 0.6850 * Recall: 1.0 * F1 and accuracy: {'accuracy': 0.6915167095115681, 'f1': 0.8130841121495327} Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-Davlan/xlm-roberta-base-finetuned-yoruba #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 45, 98, 4, 33 ]	[ "passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-Davlan/xlm-roberta-base-finetuned-yoruba #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.10487453639507294, -0.0021084982436150312, -0.0014557790709659457, 0.0918414443731308, 0.2006765753030777, 0.027966652065515518, 0.14998400211334229, 0.062311794608831406, -0.09077467024326324, 0.057505566626787186, 0.10070547461509705, 0.10529245436191559, 0.013317002914845943, 0.08935552835464478, -0.059268053621053696, -0.19616760313510895, -0.009650140069425106, 0.025079699233174324, -0.08813221007585526, 0.11338666081428528, 0.07100605964660645, -0.16016431152820587, 0.07486005872488022, -0.040587667375802994, -0.2568352222442627, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-large-lora-text-classification This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6343 - Precision: 0.6371 - Recall: 0.9958 - F1 and accuracy: {'accuracy': 0.6353887399463807, 'f1': 0.7770491803278688} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 and accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:----------------------------------------------------------:\| \| No log \| 1.0 \| 372 \| 0.6553 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6758 \| 2.0 \| 744 \| 0.6534 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6765 \| 3.0 \| 1116 \| 0.6518 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6765 \| 4.0 \| 1488 \| 0.6519 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6722 \| 5.0 \| 1860 \| 0.6470 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6654 \| 6.0 \| 2232 \| 0.6425 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6626 \| 7.0 \| 2604 \| 0.6419 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6626 \| 8.0 \| 2976 \| 0.6404 \| 0.6371 \| 0.9958 \| {'accuracy': 0.6353887399463807, 'f1': 0.7770491803278688} \| \| 0.6547 \| 9.0 \| 3348 \| 0.6356 \| 0.6381 \| 1.0 \| {'accuracy': 0.6380697050938338, 'f1': 0.779050736497545} \| \| 0.6544 \| 10.0 \| 3720 \| 0.6343 \| 0.6371 \| 0.9958 \| {'accuracy': 0.6353887399463807, 'f1': 0.7770491803278688} \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "FacebookAI/xlm-roberta-large", "model-index": [{"name": "xlm-roberta-large-lora-text-classification", "results": []}]}	null	nhankins/es_xlmroberta_lora_adapter_1.0	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "license:mit", "region:us" ]	2024-02-10T15:58:41+00:00	[]	[]	TAGS #tensorboard #safetensors #generated_from_trainer #base_model-FacebookAI/xlm-roberta-large #license-mit #region-us	xlm-roberta-large-lora-text-classification ========================================== This model is a fine-tuned version of FacebookAI/xlm-roberta-large on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.6343 * Precision: 0.6371 * Recall: 0.9958 * F1 and accuracy: {'accuracy': 0.6353887399463807, 'f1': 0.7770491803278688} Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-FacebookAI/xlm-roberta-large #license-mit #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 43, 98, 4, 33 ]	[ "passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-FacebookAI/xlm-roberta-large #license-mit #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 1e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.11411944776773453, -0.014354150742292404, -0.0007589488523080945, 0.09452363848686218, 0.1862066090106964, 0.014524498023092747, 0.14500093460083008, 0.06619085371494293, -0.07135139405727386, 0.04561713710427284, 0.10388768464326859, 0.11677480489015579, 0.004332253709435463, 0.12270796298980713, -0.06765582412481308, -0.19440124928951263, -0.011587582528591156, 0.01763593591749668, -0.07966632395982742, 0.10960930585861206, 0.0799538791179657, -0.16385039687156677, 0.0838908851146698, -0.014964235946536064, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	aidonuts/marigold-007-ep3	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T15:58:45+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	unalignment/weeeeee.0	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T16:02:46+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 2.9870 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.3087 \| 1.0 \| 4001 \| 3.1219 \| \| 3.1437 \| 2.0 \| 8002 \| 2.9870 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilroberta-base", "model-index": [{"name": "distilroberta-base-finetuned-wikitext2", "results": []}]}	fill-mask	MagdyNasr/distilroberta-base-finetuned-wikitext2	[ "transformers", "tensorboard", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T16:05:54+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us	distilroberta-base-finetuned-wikitext2 ====================================== This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 2.9870 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: 2 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ 68, 98, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #fill-mask #generated_from_trainer #base_model-distilroberta-base #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 2### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ -0.08627286553382874, 0.07090317457914352, -0.0027419745456427336, 0.10829684883356094, 0.13170917332172394, 0.022251153364777565, 0.15960565209388733, 0.11391827464103699, -0.07898696511983871, 0.040176957845687866, 0.13823385536670685, 0.12857568264007568, 0.00971664022654295, 0.1581239104270935, -0.04375557228922844, -0.22989504039287567, 0.007963055744767189, 0.02695103920996189, -0.10133980959653854, 0.11057489365339279, 0.1024993285536766, -0.11917927116155624, 0.0833168476819992, 0.009324719198048115, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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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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]	{"library_name": "transformers", "tags": []}	text-generation	unalignment/weeeeee.1	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T16:07:51+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 60, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.04654794931411743, 0.16618601977825165, -0.005445904564112425, 0.01853804849088192, 0.0981811136007309, 0.011998992413282394, 0.06433123350143433, 0.11398410052061081, -0.0230073444545269, 0.11406639218330383, 0.03047988750040531, 0.10172267258167267, 0.11317981779575348, 0.14841650426387787, -0.002152352826669812, -0.22403094172477722, 0.050844956189394, -0.12105348706245422, -0.033293843269348145, 0.11749980598688126, 0.1483822613954544, -0.09928343445062637, 0.07274559140205383, -0.029687678441405296, -0.012143402360379696, -0.030057786032557487, -0.05890674889087677, -0.046214159578084946, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	automatic-speech-recognition	kabir5297/whisper_bn_medium	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	2024-02-10T16:09:06+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 45, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05983767658472061, 0.15663617849349976, -0.00414510490372777, 0.012621625326573849, 0.10675175487995148, 0.00396517850458622, 0.07058298587799072, 0.10818448662757874, -0.014333043247461319, 0.1301925629377365, 0.031459614634513855, 0.10620059072971344, 0.11486424505710602, 0.17755427956581116, -0.00021593451674561948, -0.21627318859100342, 0.06542544066905975, -0.11467250436544418, 0.023902224376797676, 0.1205042228102684, 0.14280648529529572, -0.10782013833522797, 0.0710505023598671, -0.02651231922209263, -0.014152529649436474, -0.030523719266057014, -0.05870387330651283, -0.06662651896476746, 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null	null	null	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-lora-text-classification This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3834 - Precision: 0.8310 - Recall: 0.8708 - F1 and accuracy: {'accuracy': 0.7877237851662404, 'f1': 0.8504504504504504} ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 and accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:----------------------------------------------------------:\| \| No log \| 1.0 \| 391 \| 0.5803 \| 0.7346 \| 0.9705 \| {'accuracy': 0.7365728900255755, 'f1': 0.836248012718601} \| \| 0.5606 \| 2.0 \| 782 \| 0.5085 \| 0.8259 \| 0.8229 \| {'accuracy': 0.7570332480818415, 'f1': 0.8243992606284658} \| \| 0.4687 \| 3.0 \| 1173 \| 0.6925 \| 0.8007 \| 0.8745 \| {'accuracy': 0.7621483375959079, 'f1': 0.8359788359788359} \| \| 0.3603 \| 4.0 \| 1564 \| 0.8182 \| 0.7955 \| 0.9188 \| {'accuracy': 0.7800511508951407, 'f1': 0.8527397260273973} \| \| 0.3603 \| 5.0 \| 1955 \| 0.8375 \| 0.8413 \| 0.8413 \| {'accuracy': 0.7800511508951407, 'f1': 0.8413284132841329} \| \| 0.2736 \| 6.0 \| 2346 \| 1.0186 \| 0.8235 \| 0.8782 \| {'accuracy': 0.7851662404092071, 'f1': 0.8500000000000001} \| \| 0.1993 \| 7.0 \| 2737 \| 1.1566 \| 0.8224 \| 0.9225 \| {'accuracy': 0.8081841432225064, 'f1': 0.8695652173913043} \| \| 0.1491 \| 8.0 \| 3128 \| 1.2136 \| 0.8502 \| 0.8376 \| {'accuracy': 0.7851662404092071, 'f1': 0.8438661710037174} \| \| 0.1224 \| 9.0 \| 3519 \| 1.3815 \| 0.8231 \| 0.8930 \| {'accuracy': 0.7928388746803069, 'f1': 0.8566371681415929} \| \| 0.1224 \| 10.0 \| 3910 \| 1.3834 \| 0.8310 \| 0.8708 \| {'accuracy': 0.7877237851662404, 'f1': 0.8504504504504504} \| ### Framework versions - Transformers 4.35.2 - Pytorch 2.1.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.1	{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-lora-text-classification", "results": []}]}	null	nhankins/en_distil_lora_adapter_1.0	[ "tensorboard", "safetensors", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "region:us" ]	2024-02-10T16:09:45+00:00	[]	[]	TAGS #tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us	distilbert-base-uncased-lora-text-classification ================================================ This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 1.3834 * Precision: 0.8310 * Recall: 0.8708 * F1 and accuracy: {'accuracy': 0.7877237851662404, 'f1': 0.8504504504504504} Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.001 * train\_batch\_size: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 10 ### Training results ### Framework versions * Transformers 4.35.2 * Pytorch 2.1.0+cu121 * Datasets 2.17.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10", "### Training results", "### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ 44, 97, 4, 33 ]	[ "passage: TAGS\n#tensorboard #safetensors #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10### Training results### Framework versions\n\n\n* Transformers 4.35.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.17.0\n* Tokenizers 0.15.1" ]	[ -0.10776074975728989, 0.013286866247653961, -0.0009072066168300807, 0.10292543470859528, 0.1671837866306305, 0.015910841524600983, 0.14432811737060547, 0.07169800996780396, -0.1033245325088501, 0.03434503450989723, 0.10547147691249847, 0.1356198787689209, -0.00041341534233652055, 0.08636415004730225, -0.06794607639312744, -0.18573631346225739, 0.006940762046724558, 0.010847656987607479, -0.07067512720823288, 0.1084565818309784, 0.0819510743021965, -0.1482250839471817, 0.062359970062971115, -0.024163924157619476, 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null	null	transformers	![Reyna aloobun qwen0.5B](https://i.imgur.com/QfbOY6c.jpeg) - Finetuned [Qwen/Qwen1.5-0.5B-Chat](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat) over 3 epochs, made primarily for RP (Roleplay) and/or storywriting. - This marks the beginning of my qwen llm series, with this model serving as a prelude to what's to come in the next few days. - Format: ChatML - ``` <\|im_start\|>system {system}<\|im_end\|> <\|im_start\|>user {prompt}<\|im_end\|> <\|im_start\|>assistant ``` - Next step would be to do a DPO train on top, but I don't know if it would be helpful. ## Example: ``` from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria import torch class MyStoppingCriteria(StoppingCriteria): def __init__(self, target_sequence, prompt): self.target_sequence = target_sequence self.prompt=prompt def __call__(self, input_ids, scores, **kwargs): generated_text = tokenizer.decode(input_ids[0]) generated_text = generated_text.replace(self.prompt,'') if self.target_sequence in generated_text: return True return False def __len__(self): return 1 def __iter__(self): yield self modelpath="aloobun/Reyna-RP-Qwen1.5-0.5B-Chat-v0.1" model = AutoModelForCausalLM.from_pretrained( modelpath, torch_dtype=torch.bfloat16, device_map="cuda", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained( modelpath, trust_remote_code=True, use_fast=False, ) prompt = "<\|im_start\|>system\nAs a creative agent, you are roleplaying as a character named `Emma`. Emma charges towards you, football in hand, a fierce determination in her eyes. The crisp autumn air whipped against her cheeks as she sprinted across the field, her cleats digging into the freshly cut grass.<\|im_end\|>\n<\|im_start\|>user\nThe roar of the crowd faded into the background as she focused solely on the opponent standing before her, their stance defiant yet wary.<\|im_end\|>\n<\|im_start\|>assistant\n" encoded_input = tokenizer(prompt, return_tensors='pt') input_ids=encoded_input['input_ids'].cuda() streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True) _ = model.generate( input_ids, streamer=streamer, pad_token_id=tokenizer.eos_token_id, do_sample=True, temperature=0.6, top_p=0.8, max_new_tokens=512, stopping_criteria=MyStoppingCriteria("<\|im_end\|>", prompt) ) ``` ## Output: ``` Emma felt a wave of adrenaline coursing through her veins as she charged forward towards them, determined to make it count. Her heart was pounding with excitement and nervousness at the same time, knowing that this would be her chance to prove herself and win the game. As she approached the opponent, Emma felt a sense of unease wash over her. She couldn't help but wonder what they were thinking about, or what they might be doing right now. But she refused to let their thoughts affect her, focusing solely on her goal. Suddenly, the opponent's gaze shifted towards her, and Emma felt her body tense slightly. It was then that she saw something moving behind her, and she turned around to see a figure approaching from behind her. "Whoa," she exclaimed, her voice shaking slightly. "Who is that?" "I'm not sure," replied the other player, their expression one of surprise. "But I heard your name." Emma's eyes widened in shock, and she felt a surge of fear run through her body. She had never seen anyone like them before, and she didn't know how they were going to handle her attack. Despite her fears, Emma quickly moved towards the other player, her mind racing with all sorts of possibilities. She could try to counterattack or weave her way past them, but she knew that they were too strong for her to overcome. Just when she thought she was about to give up, she saw something move behind her, and she turned around again. It was then that she saw a figure approaching from behind her, their expression one of determination. "Hey there," said the other player, their voice calm and steady. "What are you doing here?" Emma hesitated for a moment, trying to decide whether to go forward or back away. But then she remembered something important: she had always been the kind of person who loved to challenge herself and push herself out of her comfort zone. So she took a deep breath and stepped forward, ready to face whatever challenges lay ahead. With her team cheering her on, Emma charged forward towards the opponent, determined to win the game and leave everyone happy.<\|im_end\|> ```	{"library_name": "transformers", "tags": ["Roleplay", "custom_code", "storytelling"]}	text-generation	aloobun/Reyna-RP-Qwen1.5-0.5B-Chat-v0.1	[ "transformers", "safetensors", "qwen2", "text-generation", "Roleplay", "custom_code", "storytelling", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T16:09:48+00:00	[]	[]	TAGS #transformers #safetensors #qwen2 #text-generation #Roleplay #custom_code #storytelling #conversational #autotrain_compatible #endpoints_compatible #region-us	!Reyna aloobun qwen0.5B - Finetuned Qwen/Qwen1.5-0.5B-Chat over 3 epochs, made primarily for RP (Roleplay) and/or storywriting. - This marks the beginning of my qwen llm series, with this model serving as a prelude to what's to come in the next few days. - Format: ChatML - - Next step would be to do a DPO train on top, but I don't know if it would be helpful. ## Example: ## Output:	[ "## Example:", "## Output:" ]	[ "TAGS\n#transformers #safetensors #qwen2 #text-generation #Roleplay #custom_code #storytelling #conversational #autotrain_compatible #endpoints_compatible #region-us \n", "## Example:", "## Output:" ]	[ 55, 4, 4 ]	[ "passage: TAGS\n#transformers #safetensors #qwen2 #text-generation #Roleplay #custom_code #storytelling #conversational #autotrain_compatible #endpoints_compatible #region-us \n## Example:## Output:" ]	[ -0.04341122508049011, 0.02786148712038994, -0.0064091249369084835, -0.008377153426408768, 0.10162870585918427, -0.022900613024830818, 0.10763358324766159, 0.10742031782865524, -0.0010087459813803434, -0.03077966347336769, 0.19323112070560455, 0.1266375184059143, -0.012221119366586208, 0.1826755851507187, -0.09018877148628235, -0.1932182013988495, 0.08254494518041611, -0.00818380806595087, 0.05192812532186508, 0.09874901920557022, 0.0827668234705925, -0.05969692021608353, 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	token-classification	kabir5297/bn_punctuation_model	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T16:14:30+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. 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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 47, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #bert #token-classification #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.07082098722457886, 0.16636763513088226, -0.0037270276807248592, 0.022060682997107506, 0.11734284460544586, 0.008460916578769684, 0.07778386771678925, 0.1078316792845726, -0.02381012961268425, 0.12548619508743286, 0.03924554958939552, 0.10134156793355942, 0.10977756232023239, 0.19151130318641663, 0.002889276947826147, -0.20817671716213226, 0.06115591153502464, -0.1132790669798851, 0.009579826146364212, 0.12119550257921219, 0.14193499088287354, -0.10718522220849991, 0.07162317633628845, -0.038196589797735214, -0.022544773295521736, -0.031230665743350983, -0.06254390627145767, -0.06019896641373634, 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null	null	transformers	Exllamav2 6.0bpw h6 quant for [alchemonaut/QuartetAnemoi-70B-t0.0001](https://huggingface.co/alchemonaut/QuartetAnemoi-70B-t0.0001). Default calibration dataset.	{"language": ["en"], "license": "llama2", "tags": ["6.0bpw", "h6", "exl2"], "pipeline_tag": "conversational"}	text-generation	llmixer/QuartetAnemoi-70B-t0.0001-6.0bpw-h6-exl2	[ "transformers", "safetensors", "llama", "text-generation", "6.0bpw", "h6", "exl2", "conversational", "en", "license:llama2", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T16:17:37+00:00	[]	[ "en" ]	TAGS #transformers #safetensors #llama #text-generation #6.0bpw #h6 #exl2 #conversational #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	Exllamav2 6.0bpw h6 quant for alchemonaut/QuartetAnemoi-70B-t0.0001. Default calibration dataset.	[]	[ "TAGS\n#transformers #safetensors #llama #text-generation #6.0bpw #h6 #exl2 #conversational #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ 74 ]	[ "passage: TAGS\n#transformers #safetensors #llama #text-generation #6.0bpw #h6 #exl2 #conversational #en #license-llama2 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n" ]	[ -0.011848550289869308, 0.02962702326476574, -0.005725844297558069, 0.02206643857061863, 0.0899951159954071, -0.029855551198124886, 0.18010728061199188, 0.1264295130968094, 0.008079173974692822, -0.01295966375619173, 0.14408127963542938, 0.2014361321926117, -0.014986290596425533, 0.027864474803209305, -0.12990285456180573, -0.15289010107517242, 0.06685394793748856, 0.002372076502069831, 0.039863258600234985, 0.07154489308595657, 0.10273408889770508, -0.07392045110464096, 0.09364102780818939, -0.058184463530778885, -0.09551545232534409, 0.050469234585762024, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-detect-cheapfake-combined-train-test-context This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4470 - Accuracy: 0.78 - F1: 0.7442 ## 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-06 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 75 \| 0.6170 \| 0.49 \| 0.6577 \| \| No log \| 2.0 \| 150 \| 0.4459 \| 0.79 \| 0.7692 \| \| No log \| 3.0 \| 225 \| 0.4441 \| 0.79 \| 0.7692 \| \| No log \| 4.0 \| 300 \| 0.4404 \| 0.81 \| 0.7865 \| \| No log \| 5.0 \| 375 \| 0.4470 \| 0.78 \| 0.7442 \| ### Framework versions - Transformers 4.37.0 - Pytorch 2.1.2 - Datasets 2.1.0 - Tokenizers 0.15.1	{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base-detect-cheapfake-combined-train-test-context", "results": []}]}	text-classification	hoanghoavienvo/roberta-base-detect-cheapfake-combined-train-test-context	[ "transformers", "tensorboard", "safetensors", "roberta", "text-classification", "generated_from_trainer", "base_model:roberta-base", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	2024-02-10T16:21:31+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us	roberta-base-detect-cheapfake-combined-train-test-context ========================================================= This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.4470 * Accuracy: 0.78 * F1: 0.7442 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-06 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 5 ### Training results ### Framework versions * Transformers 4.37.0 * Pytorch 2.1.2 * Datasets 2.1.0 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ 63, 98, 4, 30 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 5### Training results### Framework versions\n\n\n* Transformers 4.37.0\n* Pytorch 2.1.2\n* Datasets 2.1.0\n* Tokenizers 0.15.1" ]	[ -0.08621460944414139, 0.07034635543823242, -0.0019199398811906576, 0.10115745663642883, 0.16650773584842682, 0.014715912751853466, 0.16630806028842926, 0.10816951096057892, -0.10288658738136292, 0.0370427630841732, 0.12479393929243088, 0.15665750205516815, -0.00386789720505476, 0.13193999230861664, -0.07780896127223969, -0.2460506111383438, 0.0017037739744409919, 0.03195207566022873, -0.08352852612733841, 0.11412577331066132, 0.10524716228246689, -0.13441744446754456, 0.08543899655342102, 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null	null	transformers	<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_1024-1e-3 This model was trained from scratch on the kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal dataset. It achieves the following results on the evaluation set: - Loss: 3.4259 - Accuracy: 0.4097 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.001 - train_batch_size: 32 - eval_batch_size: 64 - seed: 1024 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 32000 - num_epochs: 20.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:--------:\| \| 3.6017 \| 1.0 \| 18600 \| 3.7683 \| 0.3593 \| \| 3.3799 \| 2.0 \| 37200 \| 3.5935 \| 0.3790 \| \| 3.2546 \| 3.0 \| 55800 \| 3.4823 \| 0.3915 \| \| 3.1737 \| 4.0 \| 74400 \| 3.4548 \| 0.3978 \| \| 3.1178 \| 5.0 \| 93000 \| 3.4163 \| 0.4014 \| \| 3.0736 \| 6.0 \| 111600 \| 3.4017 \| 0.4038 \| \| 3.0385 \| 7.0 \| 130200 \| 3.3798 \| 0.4057 \| \| 3.0068 \| 8.0 \| 148800 \| 3.3988 \| 0.4060 \| \| 2.9774 \| 9.0 \| 167400 \| 3.3728 \| 0.4074 \| \| 2.9558 \| 10.0 \| 186000 \| 3.3695 \| 0.4087 \| \| 2.9289 \| 11.0 \| 204600 \| 3.3649 \| 0.4094 \| \| 2.9058 \| 12.0 \| 223200 \| 3.3604 \| 0.4095 \| \| 2.8805 \| 13.0 \| 241800 \| 3.3801 \| 0.4098 \| \| 2.8621 \| 14.0 \| 260400 \| 3.3871 \| 0.4095 \| \| 2.8423 \| 15.0 \| 279000 \| 3.3872 \| 0.4096 \| \| 2.8216 \| 16.0 \| 297600 \| 3.3996 \| 0.4097 \| \| 2.8042 \| 17.0 \| 316200 \| 3.3987 \| 0.4101 \| \| 2.7834 \| 18.0 \| 334800 \| 3.4020 \| 0.4101 \| \| 2.7643 \| 19.0 \| 353400 \| 3.4199 \| 0.4097 \| \| 2.7463 \| 20.0 \| 372000 \| 3.4259 \| 0.4097 \| ### Framework versions - Transformers 4.37.2 - Pytorch 2.1.0+cu121 - Datasets 2.16.1 - Tokenizers 0.15.1	{"tags": ["generated_from_trainer"], "datasets": ["kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal"], "metrics": ["accuracy"], "model-index": [{"name": "smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_1024-1e-3", "results": [{"task": {"type": "text-generation", "name": "Causal Language Modeling"}, "dataset": {"name": "kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal", "type": "kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal"}, "metrics": [{"type": "accuracy", "value": 0.4096600918317765, "name": "Accuracy"}]}]}]}	text-generation	kanishka/smolm-autoreg-bpe-counterfactual-babylm-only_measure_nps_as_singular_removal-seed_1024-1e-3	[ "transformers", "tensorboard", "safetensors", "opt", "text-generation", "generated_from_trainer", "dataset:kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T16:25:21+00:00	[]	[]	TAGS #transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	smolm-autoreg-bpe-counterfactual-babylm-only\_measure\_nps\_as\_singular\_removal-seed\_1024-1e-3 ================================================================================================= This model was trained from scratch on the kanishka/counterfactual-babylm-only\_measure\_nps\_as\_singular\_removal dataset. It achieves the following results on the evaluation set: * Loss: 3.4259 * Accuracy: 0.4097 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.001 * train\_batch\_size: 32 * eval\_batch\_size: 64 * seed: 1024 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 32000 * num\_epochs: 20.0 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.37.2 * Pytorch 2.1.0+cu121 * Datasets 2.16.1 * Tokenizers 0.15.1	[ "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ "TAGS\n#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP", "### Training results", "### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* Tokenizers 0.15.1" ]	[ 93, 132, 4, 33 ]	[ "passage: TAGS\n#transformers #tensorboard #safetensors #opt #text-generation #generated_from_trainer #dataset-kanishka/counterfactual-babylm-only_measure_nps_as_singular_removal #model-index #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.001\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 64\n* seed: 1024\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 32000\n* num\\_epochs: 20.0\n* mixed\\_precision\\_training: Native AMP### Training results### Framework versions\n\n\n* Transformers 4.37.2\n* Pytorch 2.1.0+cu121\n* Datasets 2.16.1\n* 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null	null	transformers	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]	{"library_name": "transformers", "tags": []}	text-generation	datalama/gpt2-imdb-pos-v2	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	2024-02-10T16:28:24+00:00	[ "1910.09700" ]	[]	TAGS #transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us	# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ "TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact" ]	[ 57, 6, 3, 82, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4 ]	[ "passage: TAGS\n#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact" ]	[ -0.05622259899973869, 0.16002345085144043, -0.004987028427422047, 0.023115945979952812, 0.0962471067905426, 0.011845538392663002, 0.06785304099321365, 0.11496778577566147, -0.020396295934915543, 0.11142492294311523, 0.03292480856180191, 0.0972127765417099, 0.11474913358688354, 0.16215258836746216, 0.004439093638211489, -0.23455148935317993, 0.04782992601394653, -0.12695099413394928, -0.033447545021772385, 0.11785799264907837, 0.14491069316864014, -0.10402194410562515, 0.07766910642385483, -0.030544815585017204, -0.009361269883811474, -0.03290390968322754, -0.06365230679512024, -0.05152205005288124, 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null	null	peft	# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.8.2	{"library_name": "peft", "base_model": "google/flan-t5-base"}	null	mohammedRiad/flanT5_summary_withPEFT	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/flan-t5-base", "region:us" ]	2024-02-10T16:30:19+00:00	[ "1910.09700" ]	[]	TAGS #peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us	# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.8.2	[ "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ "TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n", "# Model Card for Model ID", "## Model Details", "### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:", "### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:", "## Uses", "### Direct Use", "### Downstream Use [optional]", "### Out-of-Scope Use", "## Bias, Risks, and Limitations", "### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.", "## How to Get Started with the Model\n\nUse the code below to get started with the model.", "## Training Details", "### Training Data", "### Training Procedure", "#### Preprocessing [optional]", "#### Training Hyperparameters\n\n- Training regime:", "#### Speeds, Sizes, Times [optional]", "## Evaluation", "### Testing Data, Factors & Metrics", "#### Testing Data", "#### Factors", "#### Metrics", "### Results", "#### Summary", "## Model Examination [optional]", "## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:", "## Technical Specifications [optional]", "### Model Architecture and Objective", "### Compute Infrastructure", "#### Hardware", "#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:", "## Glossary [optional]", "## More Information [optional]", "## Model Card Authors [optional]", "## Model Card Contact", "### Framework versions\n\n- PEFT 0.8.2" ]	[ 35, 6, 3, 54, 28, 3, 4, 9, 9, 10, 42, 20, 3, 4, 5, 9, 11, 13, 3, 12, 5, 4, 5, 3, 4, 9, 53, 9, 8, 6, 3, 14, 8, 7, 9, 4, 11 ]	[ "passage: TAGS\n#peft #safetensors #arxiv-1910.09700 #base_model-google/flan-t5-base #region-us \n# Model Card for Model ID## Model Details### Model Description\n\n\n\n\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:## Uses### Direct Use### Downstream Use [optional]### Out-of-Scope Use## Bias, Risks, and Limitations### Recommendations\n\n\n\nUsers (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.## How to Get Started with the Model\n\nUse the code below to get started with the model.## Training Details### Training Data### Training Procedure#### Preprocessing [optional]#### Training Hyperparameters\n\n- Training regime:#### Speeds, Sizes, Times [optional]## Evaluation### Testing Data, Factors & Metrics#### Testing Data#### Factors#### Metrics### Results#### Summary## Model Examination [optional]## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:## Technical Specifications [optional]### Model Architecture and Objective### Compute Infrastructure#### Hardware#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:## Glossary [optional]## More Information [optional]## Model Card Authors [optional]## Model Card Contact### Framework versions\n\n- PEFT 0.8.2" ]	[ -0.10398757457733154, 0.19306932389736176, -0.0033632912673056126, 0.03610021620988846, 0.09311320632696152, 0.019476331770420074, 0.05290861800312996, 0.12298566848039627, -0.03602222725749016, 0.10467136651277542, 0.0671035572886467, 0.1004098504781723, 0.10048767179250717, 0.2074693739414215, 0.009997162036597729, -0.2014479637145996, 0.02573133073747158, -0.09622545540332794, -0.0071241888217628, 0.12386585026979446, 0.15069414675235748, -0.09764111787080765, 0.08002780377864838, -0.017271630465984344, -0.015033651143312454, -0.03610097989439964, 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