Datasets:

---

ftopal

/

huggingface-models

like 0

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. 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]

{"library_name": "transformers", "tags": []}

metythorn/donut-cord

2024-05-03T09:19:00+00:00

TAGS #transformers #safetensors #vision-encoder-decoder #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

peft

<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # phi-1-5-finetuned-cazton_complete This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-1_5", "model-index": [{"name": "phi-1-5-finetuned-cazton_complete", "results": []}]}

alpdk1394/phi-1-5-finetuned-cazton_complete

2024-05-03T09:21:40+00:00

TAGS #peft #tensorboard #safetensors #generated_from_trainer #base_model-microsoft/phi-1_5 #license-mit #region-us

# phi-1-5-finetuned-cazton_complete This model is a fine-tuned version of microsoft/phi-1_5 on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 1 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

transformers

# Uploaded model - **Developed by:** animaRegem - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

animaRegem/llama-3-lora-malayalam

2024-05-03T09:21:42+00:00

TAGS #transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: animaRegem - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

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. 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]

{"library_name": "transformers", "tags": ["unsloth"]}

animaRegem/llama-3-lora-malayalam-tokenizer

2024-05-03T09:22:07+00:00

TAGS #transformers #unsloth #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

token-classification

spacy

A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts. | Feature | Description | | --- | --- | | **Name** | `en_skillner` | | **Version** | `3.7.1` | | **spaCy** | `>=3.7.4,<3.8.0` | | **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` | | **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` | | **Vectors** | 514157 keys, 514157 unique vectors (300 dimensions) | | **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br>[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br>[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br>[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) | | **License** | `MIT` | | **Author** | [nestauk](https://explosion.ai) | ### Label Scheme <details> <summary>View label scheme (3 labels for 1 components)</summary> | Component | Labels | | --- | --- | | **`ner`** | `SKILL`, `EXPERIENCE`, `BENEFIT` | </details> ### Accuracy | Type | Score | | --- | --- | | `ENTS_P` | 46.06 | | `ENTS_R` | 45.74 | | `ENTS_F` | 45.90 |

{"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]}

nestauk/en_skillner

2024-05-03T09:22:16+00:00

TAGS #spacy #token-classification #en #license-mit #model-index #region-us

A Named Entity Recognition (NER) model to extract SKILL, EXPERIENCE and BENEFIT from job adverts. ### Label Scheme View label scheme (3 labels for 1 components) ### Accuracy

text-classification

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_brkfst 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: 0.0786 - Accuracy: 0.9811 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 27 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.5591 | 0.71 | 10 | 0.3018 | 0.8774 | | 0.2997 | 1.43 | 20 | 0.2236 | 0.8679 | | 0.2096 | 2.14 | 30 | 0.1582 | 0.9340 | | 0.2465 | 2.86 | 40 | 0.1677 | 0.9623 | | 0.0823 | 3.57 | 50 | 0.2153 | 0.9528 | | 0.0682 | 4.29 | 60 | 0.2196 | 0.9528 | | 0.1015 | 5.0 | 70 | 0.0825 | 0.9717 | | 0.0364 | 5.71 | 80 | 0.1376 | 0.9623 | | 0.0606 | 6.43 | 90 | 0.1448 | 0.9717 | | 0.03 | 7.14 | 100 | 0.1107 | 0.9811 | | 0.0228 | 7.86 | 110 | 0.0810 | 0.9811 | | 0.003 | 8.57 | 120 | 0.0946 | 0.9811 | | 0.0182 | 9.29 | 130 | 0.0663 | 0.9906 | | 0.0126 | 10.0 | 140 | 0.1986 | 0.9717 | | 0.0006 | 10.71 | 150 | 0.0788 | 0.9811 | | 0.0003 | 11.43 | 160 | 0.0974 | 0.9811 | | 0.0003 | 12.14 | 170 | 0.1012 | 0.9811 | | 0.0005 | 12.86 | 180 | 0.0879 | 0.9811 | | 0.0003 | 13.57 | 190 | 0.0803 | 0.9811 | | 0.0002 | 14.29 | 200 | 0.0794 | 0.9811 | | 0.0002 | 15.0 | 210 | 0.0786 | 0.9811 | ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "roberta-base_brkfst", "results": []}]}

JBhug/roberta-base_brkfst

2024-05-03T09:22:29+00:00

TAGS #transformers #safetensors #roberta #text-classification #generated_from_trainer #base_model-roberta-base #license-mit #autotrain_compatible #endpoints_compatible #region-us

roberta-base\_brkfst ==================== This model is a fine-tuned version of roberta-base on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0786 * Accuracy: 0.9811 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 5e-05 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 27 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 15 ### Training results ### Framework versions * Transformers 4.39.1 * Pytorch 2.2.1+cu121 * Datasets 2.18.0 * Tokenizers 0.15.2

text-to-image

diffusers

AAM XL Anime Mix Anime Screencap Style Model Do you like what I do? Consider supporting me on Patreon 🅿️ or feel free to buy me a coffee ☕. A ❤️, a kind comment or a review is greatly appreciated. Join my Discord Server This is essentially derived from AAM AnyLoRA Anime Mix, but it's based on SDXL. It won't work with loras and embeddings for SD1.5 even if they're trained on the original AAM or AnyLoRA. "Mix" as in mix of anime styles. I suggest you use CFG 5-7 (not higher than 8), 20-30 steps with Euler a. Upscalers suggestions are None (bicubic upscaling in comfyui), any good GAN for anime or Latent (only if you know what you're doing). For Turbo I suggest you use CFG 3-4 and 8 steps Euler a or 15 steps LCM. Unlike with DreamShaper Turbo, I think base AAM XL is better than AAM XL Turbo most of the time. However the latter is MUCH faster. Purpose of this model Make amazing anime style artworks on its own. Train character loras. Use anime styles. Generate anime art and stylized art. It can generate hen/tai on its own, but you might need loras and embeddings for specific stuff. ComfyUI Workflow: https://pastebin.com/BHCEzc6T Finetuned over "DreamShaper Anime", which is a mix of Anime Art Diffusion, Hassaku and DreamShaper XL. Finetuned using the AAM dataset. Fair AI Public License 1.0-SD

{"library_name": "diffusers"}

cookey39/aam_xl

2024-05-03T09:22:38+00:00

TAGS #diffusers #safetensors #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us

AAM XL Anime Mix Anime Screencap Style Model Do you like what I do? Consider supporting me on Patreon 🅿️ or feel free to buy me a coffee . A ️, a kind comment or a review is greatly appreciated. Join my Discord Server This is essentially derived from AAM AnyLoRA Anime Mix, but it's based on SDXL. It won't work with loras and embeddings for SD1.5 even if they're trained on the original AAM or AnyLoRA. "Mix" as in mix of anime styles. I suggest you use CFG 5-7 (not higher than 8), 20-30 steps with Euler a. Upscalers suggestions are None (bicubic upscaling in comfyui), any good GAN for anime or Latent (only if you know what you're doing). For Turbo I suggest you use CFG 3-4 and 8 steps Euler a or 15 steps LCM. Unlike with DreamShaper Turbo, I think base AAM XL is better than AAM XL Turbo most of the time. However the latter is MUCH faster. Purpose of this model Make amazing anime style artworks on its own. Train character loras. Use anime styles. Generate anime art and stylized art. It can generate hen/tai on its own, but you might need loras and embeddings for specific stuff. ComfyUI Workflow: URL Finetuned over "DreamShaper Anime", which is a mix of Anime Art Diffusion, Hassaku and DreamShaper XL. Finetuned using the AAM dataset. Fair AI Public License 1.0-SD

text-classification

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_pork_classifier This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0066 - F1: 0.9667 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.0406 | 1.0 | 760 | 0.0126 | 0.8727 | | 0.0083 | 2.0 | 1520 | 0.0061 | 0.9667 | | 0.0017 | 3.0 | 2280 | 0.0061 | 0.9667 | | 0.0005 | 4.0 | 3040 | 0.0064 | 0.9667 | | 0.0001 | 5.0 | 3800 | 0.0066 | 0.9667 | ### Framework versions - Transformers 4.29.2 - Pytorch 1.13.1+cu116 - Datasets 2.19.0 - Tokenizers 0.13.3

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "model-index": [{"name": "distilbert_pork_classifier", "results": []}]}

andikazf15/distilbert_pork_classifier

2024-05-03T09:23:55+00:00

TAGS #transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

distilbert\_pork\_classifier ============================ This model is a fine-tuned version of distilbert/distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0066 * F1: 0.9667 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: 5 ### Training results ### Framework versions * Transformers 4.29.2 * Pytorch 1.13.1+cu116 * Datasets 2.19.0 * Tokenizers 0.13.3

automatic-speech-recognition

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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{"library_name": "transformers", "tags": []}

shtapm/whisper-large_0502_adapter_encoderall_and_decoder31_200steps

2024-05-03T09:24:37+00:00

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

text-generation

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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{"library_name": "transformers", "tags": []}

golf2248/t63n3up

2024-05-03T09:24:46+00:00

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

sentence-similarity

sentence-transformers

# {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 3850 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss` with parameters: ``` {'loss': 'CoSENTLoss', 'matryoshka_dims': [768, 512, 256, 128, 64], 'matryoshka_weights': [1, 1, 1, 1, 1], 'n_dims_per_step': -1} ``` Parameters of the fit()-Method: ``` { "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 1540, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->

{"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"}

hrusheekeshsawarkar/indic-sentence-bert-nli-matryoshka

2024-05-03T09:25:13+00:00

TAGS #sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #transformers #endpoints_compatible #region-us

# {MODEL_NAME} This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Usage (Sentence-Transformers) Using this model becomes easy when you have sentence-transformers installed: Then you can use the model like this: ## Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ## Evaluation Results For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: URL ## Training The model was trained with the parameters: DataLoader: 'URL.dataloader.DataLoader' of length 3850 with parameters: Loss: 'sentence_transformers.losses.MatryoshkaLoss.MatryoshkaLoss' with parameters: Parameters of the fit()-Method: ## Full Model Architecture ## Citing & Authors

transformers

# LeroyDyer/Mixtral_AI_Chat_1.0-Q4_K_S-GGUF This model was converted to GGUF format from [`LeroyDyer/Mixtral_AI_Chat_1.0`](https://huggingface.co/LeroyDyer/Mixtral_AI_Chat_1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/LeroyDyer/Mixtral_AI_Chat_1.0) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew. ```bash brew install ggerganov/ggerganov/llama.cpp ``` Invoke the llama.cpp server or the CLI. CLI: ```bash llama-cli --hf-repo LeroyDyer/Mixtral_AI_Chat_1.0-Q4_K_S-GGUF --model mixtral_ai_chat_1.0.Q4_K_S.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo LeroyDyer/Mixtral_AI_Chat_1.0-Q4_K_S-GGUF --model mixtral_ai_chat_1.0.Q4_K_S.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m mixtral_ai_chat_1.0.Q4_K_S.gguf -n 128 ```

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl", "llama-cpp", "gguf-my-repo"], "base_model": "LeroyDyer/Mixtral_AI_Chat_2.0"}

LeroyDyer/Mixtral_AI_Chat_1.0-Q4_K_S-GGUF

2024-05-03T09:26:37+00:00

TAGS #transformers #gguf #text-generation-inference #unsloth #mistral #trl #llama-cpp #gguf-my-repo #en #base_model-LeroyDyer/Mixtral_AI_Chat_2.0 #license-apache-2.0 #endpoints_compatible #region-us

# LeroyDyer/Mixtral_AI_Chat_1.0-Q4_K_S-GGUF This model was converted to GGUF format from 'LeroyDyer/Mixtral_AI_Chat_1.0' using URL via the URL's GGUF-my-repo space. Refer to the original model card for more details on the model. ## Use with URL Install URL through brew. Invoke the URL server or the CLI. CLI: Server: Note: You can also use this checkpoint directly through the usage steps listed in the URL repo as well.

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": []}

KevinKibe/whisper-c2translate

2024-05-03T09:26:40+00:00

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

text2text-generation

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_BART_hybrid_V1 This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the arrow 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: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 71 | 2.2072 | ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["arrow"], "base_model": "facebook/bart-large", "model-index": [{"name": "RoBERTa_BART_hybrid_V1", "results": []}]}

MikaSie/RoBERTa_BART_hybrid_V1

2024-05-03T09:27:15+00:00

TAGS #transformers #safetensors #bart #text2text-generation #generated_from_trainer #dataset-arrow #base_model-facebook/bart-large #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

RoBERTa\_BART\_hybrid\_V1 ========================= This model is a fine-tuned version of facebook/bart-large on the arrow 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: 16 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.39.1 * Pytorch 2.2.1+cu121 * Datasets 2.18.0 * Tokenizers 0.15.2

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. --> # RM-helpful_helpful_human_loraR64_20000_gemma2b_lr1e-05_bs2_g4 This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.6261 - Accuracy: 0.6495 ## 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: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.6504 | 1.0 | 2246 | 0.6341 | 0.6455 | | 0.6246 | 2.0 | 4492 | 0.6261 | 0.6495 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "gemma", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/gemma-2b", "model-index": [{"name": "RM-helpful_helpful_human_loraR64_20000_gemma2b_lr1e-05_bs2_g4", "results": []}]}

Holarissun/RM-helpful_helpful_human_loraR64_20000_gemma2b_lr1e-05_bs2_g4

2024-05-03T09:27:19+00:00

TAGS #peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-google/gemma-2b #license-gemma #region-us

RM-helpful\_helpful\_human\_loraR64\_20000\_gemma2b\_lr1e-05\_bs2\_g4 ===================================================================== This model is a fine-tuned version of google/gemma-2b on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.6261 * Accuracy: 0.6495 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: 2 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 8 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2.0 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.1.2 * Datasets 2.18.0 * Tokenizers 0.15.2

text2text-generation

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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(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": []}

Audino/my-awesome-modelv5-bpara

2024-05-03T09:27:39+00:00

TAGS #transformers #safetensors #t5 #text2text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact

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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{"library_name": "transformers", "tags": ["unsloth"]}

Chat-Error/Llama-3-limarp

2024-05-03T09:28:44+00:00

TAGS #transformers #safetensors #unsloth #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

fill-mask

transformers

# DRAGON BERT base domain-specific Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as [`roberta-base`](https://huggingface.co/FacebookAI/roberta-base). ## Model description BERT is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-bert-base-domain-specific") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-bert-base-domain-specific") model = AutoModel.from_pretrained("joeranbosma/dragon-bert-base-domain-specific") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 6e-4 - `train_batch_size`: 16 - `eval_batch_size`: 16 - `seed`: 42 - `gradient_accumulation_steps`: 16 - `total_train_batch_size`: 256 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 10.0 - `max_seq_length`: 512 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-bert-base-domain-specific

2024-05-03T09:29:17+00:00

TAGS #transformers #pytorch #bert #fill-mask #doi-10.57967/hf/2167 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON BERT base domain-specific ================================ Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as 'bert-base-multilingual-cased' from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as 'roberta-base'. Model description ----------------- BERT is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 6e-4 * 'train\_batch\_size': 16 * 'eval\_batch\_size': 16 * 'seed': 42 * 'gradient\_accumulation\_steps': 16 * 'total\_train\_batch\_size': 256 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 10.0 * 'max\_seq\_length': 512 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

transformers

# Uploaded model - **Developed by:** Aryaduta - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2b-bnb-4bit This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-2b-bnb-4bit"}

Aryaduta/llm_robot

2024-05-03T09:29:36+00:00

TAGS #transformers #safetensors #text-generation-inference #unsloth #gemma #trl #en #base_model-unsloth/gemma-2b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: Aryaduta - License: apache-2.0 - Finetuned from model : unsloth/gemma-2b-bnb-4bit This gemma model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) zephyr-speakleash-010-pl-3072-32-16-0.01 - GGUF - Model creator: https://huggingface.co/Nondzu/ - Original model: https://huggingface.co/Nondzu/zephyr-speakleash-010-pl-3072-32-16-0.01/ | Name | Quant method | Size | | ---- | ---- | ---- | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q2_K.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q2_K.gguf) | Q2_K | 2.53GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3_XS.gguf) | IQ3_XS | 2.81GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3_S.gguf) | IQ3_S | 2.96GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K_S.gguf) | Q3_K_S | 2.95GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3_M.gguf) | IQ3_M | 3.06GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K.gguf) | Q3_K | 3.28GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K_M.gguf) | Q3_K_M | 3.28GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q3_K_L.gguf) | Q3_K_L | 3.56GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.IQ4_XS.gguf) | IQ4_XS | 3.67GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_0.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_0.gguf) | Q4_0 | 3.83GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.IQ4_NL.gguf) | IQ4_NL | 3.87GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_K_S.gguf) | Q4_K_S | 3.86GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_K.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_K.gguf) | Q4_K | 4.07GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_K_M.gguf) | Q4_K_M | 4.07GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_1.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q4_1.gguf) | Q4_1 | 4.24GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_0.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_0.gguf) | Q5_0 | 4.65GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_K_S.gguf) | Q5_K_S | 4.65GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_K.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_K.gguf) | Q5_K | 4.78GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_K_M.gguf) | Q5_K_M | 4.78GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_1.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q5_1.gguf) | Q5_1 | 5.07GB | | [zephyr-speakleash-010-pl-3072-32-16-0.01.Q6_K.gguf](https://huggingface.co/RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf/blob/main/zephyr-speakleash-010-pl-3072-32-16-0.01.Q6_K.gguf) | Q6_K | 5.53GB | Original model description: --- license: mit --- [speakleash.org](https://speakleash.org) ## Prompt template: ChatML ``` <|im_start|>system {system_message}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ```

{}

RichardErkhov/Nondzu_-_zephyr-speakleash-010-pl-3072-32-16-0.01-gguf

2024-05-03T09:33:18+00:00

TAGS #gguf #region-us

Quantization made by Richard Erkhov. Github Discord Request more models zephyr-speakleash-010-pl-3072-32-16-0.01 - GGUF * Model creator: URL * Original model: URL Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.53GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.81GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.96GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.95GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.06GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.28GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.28GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.56GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.67GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.83GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.87GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.86GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q4\_K.gguf, Quant method: Q4\_K, Size: 4.07GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 4.07GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q4\_1.gguf, Quant method: Q4\_1, Size: 4.24GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.65GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.65GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.78GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.78GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q5\_1.gguf, Quant method: Q5\_1, Size: 5.07GB Name: zephyr-speakleash-010-pl-3072-32-16-0.01.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.53GB Original model description: --------------------------- license: mit ------------ URL Prompt template: ChatML -----------------------

text-generation

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. 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]

{"library_name": "transformers", "tags": []}

golf2248/l2u46k7

2024-05-03T09:33:58+00:00

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

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mamba-130m-hf - bnb 4bits - Model creator: https://huggingface.co/state-spaces/ - Original model: https://huggingface.co/state-spaces/mamba-130m-hf/ Original model description: --- library_name: transformers tags: [] --- # Mamba <!-- Provide a quick summary of what the model is/does. --> This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. # Usage You need to install `transformers` from `main` until `transformers=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used. ## Generation You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") >>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["Hey how are you doing?\n\nI'm so glad you're here."] ``` ## PEFT finetuning example In order to finetune using the `peft` library, we recommend keeping the model in float32! ```python from datasets import load_dataset from trl import SFTTrainer from peft import LoraConfig from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") dataset = load_dataset("Abirate/english_quotes", split="train") training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=4, logging_dir='./logs', logging_steps=10, learning_rate=2e-3 ) lora_config = LoraConfig( r=8, target_modules=["x_proj", "embeddings", "in_proj", "out_proj"], task_type="CAUSAL_LM", bias="none" ) trainer = SFTTrainer( model=model, tokenizer=tokenizer, args=training_args, peft_config=lora_config, train_dataset=dataset, dataset_text_field="quote", ) trainer.train() ```

{}

RichardErkhov/state-spaces_-_mamba-130m-hf-4bits

2024-05-03T09:34:36+00:00

TAGS #transformers #safetensors #mamba #text-generation #autotrain_compatible #endpoints_compatible #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models mamba-130m-hf - bnb 4bits - Model creator: URL - Original model: URL Original model description: --- library_name: transformers tags: [] --- # Mamba This repository contains the 'transfromers' compatible 'mamba-2.8b'. The checkpoints are untouched, but the full 'URL' and tokenizer are pushed to this repo. # Usage You need to install 'transformers' from 'main' until 'transformers=4.39.0' is released. We also recommend you to install both 'causal_conv_1d' and 'mamba-ssm' using: If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised 'cuda' kernels will be used. ## Generation You can use the classic 'generate' API: ## PEFT finetuning example In order to finetune using the 'peft' library, we recommend keeping the model in float32!

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mamba-130m-hf - bnb 8bits - Model creator: https://huggingface.co/state-spaces/ - Original model: https://huggingface.co/state-spaces/mamba-130m-hf/ Original model description: --- library_name: transformers tags: [] --- # Mamba <!-- Provide a quick summary of what the model is/does. --> This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. # Usage You need to install `transformers` from `main` until `transformers=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used. ## Generation You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") >>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["Hey how are you doing?\n\nI'm so glad you're here."] ``` ## PEFT finetuning example In order to finetune using the `peft` library, we recommend keeping the model in float32! ```python from datasets import load_dataset from trl import SFTTrainer from peft import LoraConfig from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") dataset = load_dataset("Abirate/english_quotes", split="train") training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=4, logging_dir='./logs', logging_steps=10, learning_rate=2e-3 ) lora_config = LoraConfig( r=8, target_modules=["x_proj", "embeddings", "in_proj", "out_proj"], task_type="CAUSAL_LM", bias="none" ) trainer = SFTTrainer( model=model, tokenizer=tokenizer, args=training_args, peft_config=lora_config, train_dataset=dataset, dataset_text_field="quote", ) trainer.train() ```

{}

RichardErkhov/state-spaces_-_mamba-130m-hf-8bits

2024-05-03T09:35:04+00:00

TAGS #transformers #safetensors #mamba #text-generation #autotrain_compatible #endpoints_compatible #8-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models mamba-130m-hf - bnb 8bits - Model creator: URL - Original model: URL Original model description: --- library_name: transformers tags: [] --- # Mamba This repository contains the 'transfromers' compatible 'mamba-2.8b'. The checkpoints are untouched, but the full 'URL' and tokenizer are pushed to this repo. # Usage You need to install 'transformers' from 'main' until 'transformers=4.39.0' is released. We also recommend you to install both 'causal_conv_1d' and 'mamba-ssm' using: If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised 'cuda' kernels will be used. ## Generation You can use the classic 'generate' API: ## PEFT finetuning example In order to finetune using the 'peft' library, we recommend keeping the model in float32!

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) mamba-130m-hf - GGUF - Model creator: https://huggingface.co/state-spaces/ - Original model: https://huggingface.co/state-spaces/mamba-130m-hf/ | Name | Quant method | Size | | ---- | ---- | ---- | | [mamba-130m-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q2_K.gguf) | Q2_K | 0.08GB | | [mamba-130m-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.IQ3_XS.gguf) | IQ3_XS | 0.09GB | | [mamba-130m-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.IQ3_S.gguf) | IQ3_S | 0.09GB | | [mamba-130m-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q3_K_S.gguf) | Q3_K_S | 0.09GB | | [mamba-130m-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.IQ3_M.gguf) | IQ3_M | 0.09GB | | [mamba-130m-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q3_K.gguf) | Q3_K | 0.09GB | | [mamba-130m-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q3_K_M.gguf) | Q3_K_M | 0.09GB | | [mamba-130m-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q3_K_L.gguf) | Q3_K_L | 0.09GB | | [mamba-130m-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.IQ4_XS.gguf) | IQ4_XS | 0.09GB | | [mamba-130m-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q4_0.gguf) | Q4_0 | 0.1GB | | [mamba-130m-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.IQ4_NL.gguf) | IQ4_NL | 0.1GB | | [mamba-130m-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q4_K_S.gguf) | Q4_K_S | 0.1GB | | [mamba-130m-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q4_K.gguf) | Q4_K | 0.1GB | | [mamba-130m-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q4_K_M.gguf) | Q4_K_M | 0.1GB | | [mamba-130m-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q4_1.gguf) | Q4_1 | 0.1GB | | [mamba-130m-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q5_0.gguf) | Q5_0 | 0.11GB | | [mamba-130m-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q5_K_S.gguf) | Q5_K_S | 0.11GB | | [mamba-130m-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q5_K.gguf) | Q5_K | 0.11GB | | [mamba-130m-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q5_K_M.gguf) | Q5_K_M | 0.11GB | | [mamba-130m-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q5_1.gguf) | Q5_1 | 0.11GB | | [mamba-130m-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/state-spaces_-_mamba-130m-hf-gguf/blob/main/mamba-130m-hf.Q6_K.gguf) | Q6_K | 0.12GB | Original model description: --- library_name: transformers tags: [] --- # Mamba <!-- Provide a quick summary of what the model is/does. --> This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. # Usage You need to install `transformers` from `main` until `transformers=4.39.0` is released. ```bash pip install git+https://github.com/huggingface/transformers@main ``` We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: ```bash pip install causal-conv1d>=1.2.0 pip install mamba-ssm ``` If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used. ## Generation You can use the classic `generate` API: ```python >>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") >>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") >>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"] >>> out = model.generate(input_ids, max_new_tokens=10) >>> print(tokenizer.batch_decode(out)) ["Hey how are you doing?\n\nI'm so glad you're here."] ``` ## PEFT finetuning example In order to finetune using the `peft` library, we recommend keeping the model in float32! ```python from datasets import load_dataset from trl import SFTTrainer from peft import LoraConfig from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf") model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf") dataset = load_dataset("Abirate/english_quotes", split="train") training_args = TrainingArguments( output_dir="./results", num_train_epochs=3, per_device_train_batch_size=4, logging_dir='./logs', logging_steps=10, learning_rate=2e-3 ) lora_config = LoraConfig( r=8, target_modules=["x_proj", "embeddings", "in_proj", "out_proj"], task_type="CAUSAL_LM", bias="none" ) trainer = SFTTrainer( model=model, tokenizer=tokenizer, args=training_args, peft_config=lora_config, train_dataset=dataset, dataset_text_field="quote", ) trainer.train() ```

{}

RichardErkhov/state-spaces_-_mamba-130m-hf-gguf

2024-05-03T09:35:42+00:00

TAGS #gguf #region-us

Quantization made by Richard Erkhov. Github Discord Request more models mamba-130m-hf - GGUF * Model creator: URL * Original model: URL Name: mamba-130m-hf.Q2\_K.gguf, Quant method: Q2\_K, Size: 0.08GB Name: mamba-130m-hf.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 0.09GB Name: mamba-130m-hf.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 0.09GB Name: mamba-130m-hf.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 0.09GB Name: mamba-130m-hf.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 0.09GB Name: mamba-130m-hf.Q3\_K.gguf, Quant method: Q3\_K, Size: 0.09GB Name: mamba-130m-hf.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 0.09GB Name: mamba-130m-hf.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 0.09GB Name: mamba-130m-hf.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 0.09GB Name: mamba-130m-hf.Q4\_0.gguf, Quant method: Q4\_0, Size: 0.1GB Name: mamba-130m-hf.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 0.1GB Name: mamba-130m-hf.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 0.1GB Name: mamba-130m-hf.Q4\_K.gguf, Quant method: Q4\_K, Size: 0.1GB Name: mamba-130m-hf.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 0.1GB Name: mamba-130m-hf.Q4\_1.gguf, Quant method: Q4\_1, Size: 0.1GB Name: mamba-130m-hf.Q5\_0.gguf, Quant method: Q5\_0, Size: 0.11GB Name: mamba-130m-hf.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 0.11GB Name: mamba-130m-hf.Q5\_K.gguf, Quant method: Q5\_K, Size: 0.11GB Name: mamba-130m-hf.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 0.11GB Name: mamba-130m-hf.Q5\_1.gguf, Quant method: Q5\_1, Size: 0.11GB Name: mamba-130m-hf.Q6\_K.gguf, Quant method: Q6\_K, Size: 0.12GB Original model description: --------------------------- library\_name: transformers tags: [] ------------------------------------ Mamba ===== This repository contains the 'transfromers' compatible 'mamba-2.8b'. The checkpoints are untouched, but the full 'URL' and tokenizer are pushed to this repo. Usage ===== You need to install 'transformers' from 'main' until 'transformers=4.39.0' is released. We also recommend you to install both 'causal\_conv\_1d' and 'mamba-ssm' using: If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised 'cuda' kernels will be used. Generation ---------- You can use the classic 'generate' API: PEFT finetuning example ----------------------- In order to finetune using the 'peft' library, we recommend keeping the model in float32!

text-generation

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. 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]

{"library_name": "transformers", "tags": []}

OwOpeepeepoopoo/herewegoagain10

2024-05-03T09:36:27+00:00

TAGS #transformers #safetensors #stablelm #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

text-generation

transformers

# D_AU-Tiefighter-Holomax-20B-V1 D_AU-Tiefighter-Holomax-20B-V1 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [KoboldAI/LLaMA2-13B-Holomax](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [KoboldAI/LLaMA2-13B-Holomax](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [KoboldAI/LLaMA2-13B-Holomax](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) * [KoboldAI/LLaMA2-13B-Holomax](https://huggingface.co/KoboldAI/LLaMA2-13B-Holomax) * [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) ## 🧩 Configuration ```yaml slices: - sources: - model: KoboldAI/LLaMA2-13B-Tiefighter layer_range: [0, 10] - sources: - model: KoboldAI/LLaMA2-13B-Holomax layer_range: [11,15] - sources: - model: KoboldAI/LLaMA2-13B-Tiefighter layer_range: [16,20] - sources: - model: KoboldAI/LLaMA2-13B-Holomax layer_range: [16,22] - sources: - model: KoboldAI/LLaMA2-13B-Tiefighter layer_range: [21, 30] - sources: - model: KoboldAI/LLaMA2-13B-Holomax layer_range: [31,33] - sources: - model: KoboldAI/LLaMA2-13B-Tiefighter layer_range: [31,35] - sources: - model: KoboldAI/LLaMA2-13B-Holomax layer_range: [36,40] - sources: - model: KoboldAI/LLaMA2-13B-Tiefighter layer_range: [36,40] merge_method: passthrough dtype: bfloat16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "DavidAU/D_AU-Tiefighter-Holomax-20B-V1" 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", "KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Holomax"], "base_model": ["KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Holomax", "KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Holomax", "KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Holomax", "KoboldAI/LLaMA2-13B-Tiefighter", "KoboldAI/LLaMA2-13B-Holomax", "KoboldAI/LLaMA2-13B-Tiefighter"]}

DavidAU/D_AU-Tiefighter-Holomax-20B-V1

2024-05-03T09:36:37+00:00

TAGS #transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #KoboldAI/LLaMA2-13B-Tiefighter #KoboldAI/LLaMA2-13B-Holomax #base_model-KoboldAI/LLaMA2-13B-Tiefighter #base_model-KoboldAI/LLaMA2-13B-Holomax #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# D_AU-Tiefighter-Holomax-20B-V1 D_AU-Tiefighter-Holomax-20B-V1 is a merge of the following models using LazyMergekit: * KoboldAI/LLaMA2-13B-Tiefighter * KoboldAI/LLaMA2-13B-Holomax * KoboldAI/LLaMA2-13B-Tiefighter * KoboldAI/LLaMA2-13B-Holomax * KoboldAI/LLaMA2-13B-Tiefighter * KoboldAI/LLaMA2-13B-Holomax * KoboldAI/LLaMA2-13B-Tiefighter * KoboldAI/LLaMA2-13B-Holomax * KoboldAI/LLaMA2-13B-Tiefighter ## Configuration ## Usage

automatic-speech-recognition

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. --> # vasista_te_small-arthink This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Google Fleurs dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cpu - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["te"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["google/fleurs"], "base_model": "openai/whisper-small", "model-index": [{"name": "vasista_te_small-arthink", "results": []}]}

April01524/ref_vasista_telugu_base

2024-05-03T09:36:42+00:00

TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #hf-asr-leaderboard #generated_from_trainer #te #dataset-google/fleurs #base_model-openai/whisper-small #license-apache-2.0 #endpoints_compatible #region-us

# vasista_te_small-arthink This model is a fine-tuned version of openai/whisper-small on the Google Fleurs dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 4 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - training_steps: 1000 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cpu - Datasets 2.19.0 - Tokenizers 0.19.1

fill-mask

transformers

# DRAGON RoBERTa base mixed-domain Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was first pretrained using general domain data, as specified [here](https://huggingface.co/xlm-roberta-base). The pretrained model was taken from HuggingFace: [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base). Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) was used. ## Model description RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-roberta-base-mixed-domain") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-roberta-base-mixed-domain") model = AutoModel.from_pretrained("joeranbosma/dragon-roberta-base-mixed-domain") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 5e-05 - `train_batch_size`: 4 - `eval_batch_size`: 4 - `seed`: 42 - `gradient_accumulation_steps`: 4 - `total_train_batch_size`: 16 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 3.0 - `max_seq_length`: 512 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-roberta-base-mixed-domain

2024-05-03T09:38:13+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #doi-10.57967/hf/2168 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON RoBERTa base mixed-domain ================================ Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was first pretrained using general domain data, as specified here. The pretrained model was taken from HuggingFace: 'xlm-roberta-base'. Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of 'xlm-roberta-base' was used. Model description ----------------- RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 5e-05 * 'train\_batch\_size': 4 * 'eval\_batch\_size': 4 * 'seed': 42 * 'gradient\_accumulation\_steps': 4 * 'total\_train\_batch\_size': 16 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 3.0 * 'max\_seq\_length': 512 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

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. --> # phi2-16bit This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 128 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.1.2 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "phi2-16bit", "results": []}]}

uzzivirus/phi2-16bit

2024-05-03T09:38:40+00:00

TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-microsoft/phi-2 #license-mit #region-us

# phi2-16bit This model is a fine-tuned version of microsoft/phi-2 on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - training_steps: 128 ### Training results ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.1.2 - Datasets 2.19.0 - Tokenizers 0.19.1

text-generation

transformers

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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": ["trl", "orpo"]}

DuongTrongChi/opp

2024-05-03T09:40:17+00:00

TAGS #transformers #safetensors #qwen2 #text-generation #trl #orpo #conversational #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

text-to-image

diffusers

# 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 🧨 diffusers 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": "diffusers"}

Niggendar/duchaitenPonyXLNo_v20

2024-05-03T09:40:22+00:00

TAGS #diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us

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fill-mask

transformers

# DRAGON BERT base mixed-domain Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was first pretrained using general domain data, as specified [here](https://huggingface.co/bert-base-multilingual-cased). The pretrained model was taken from HuggingFace: [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased). Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of [`bert-base-multilingual-cased`](https://huggingface.co/bert-base-multilingual-cased) was used. ## Model description BERT is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-bert-base-mixed-domain") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-bert-base-mixed-domain") model = AutoModel.from_pretrained("joeranbosma/dragon-bert-base-mixed-domain") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 5e-05 - `train_batch_size`: 4 - `eval_batch_size`: 4 - `seed`: 42 - `gradient_accumulation_steps`: 4 - `total_train_batch_size`: 16 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 3.0 - `max_seq_length`: 512 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-bert-base-mixed-domain

2024-05-03T09:41:14+00:00

TAGS #transformers #pytorch #bert #fill-mask #doi-10.57967/hf/2166 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON BERT base mixed-domain ============================= Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was first pretrained using general domain data, as specified here. The pretrained model was taken from HuggingFace: 'bert-base-multilingual-cased'. Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of 'bert-base-multilingual-cased' was used. Model description ----------------- BERT is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 5e-05 * 'train\_batch\_size': 4 * 'eval\_batch\_size': 4 * 'seed': 42 * 'gradient\_accumulation\_steps': 4 * 'total\_train\_batch\_size': 16 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 3.0 * 'max\_seq\_length': 512 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

fill-mask

transformers

# DRAGON RoBERTa large mixed-domain Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was first pretrained using general domain data, as specified [here](https://huggingface.co/xlm-roberta-large). The pretrained model was taken from HuggingFace: [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large). Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) was used. ## Model description RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-roberta-large-mixed-domain") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-roberta-large-mixed-domain") model = AutoModel.from_pretrained("joeranbosma/dragon-roberta-large-mixed-domain") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 5e-05 - `train_batch_size`: 4 - `eval_batch_size`: 4 - `seed`: 42 - `gradient_accumulation_steps`: 4 - `total_train_batch_size`: 16 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 3.0 - `max_seq_length`: 512 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-roberta-large-mixed-domain

2024-05-03T09:41:21+00:00

TAGS #transformers #pytorch #xlm-roberta #fill-mask #doi-10.57967/hf/2170 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON RoBERTa large mixed-domain ================================= Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was first pretrained using general domain data, as specified here. The pretrained model was taken from HuggingFace: 'xlm-roberta-large'. Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of 'xlm-roberta-large' was used. Model description ----------------- RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 5e-05 * 'train\_batch\_size': 4 * 'eval\_batch\_size': 4 * 'seed': 42 * 'gradient\_accumulation\_steps': 4 * 'total\_train\_batch\_size': 16 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 3.0 * 'max\_seq\_length': 512 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

fill-mask

transformers

# DRAGON Longformer base mixed-domain Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was first pretrained using general domain data, as specified [here](https://huggingface.co/allenai/longformer-base-4096). The pretrained model was taken from HuggingFace: [`allenai/longformer-base-4096`](https://huggingface.co/allenai/longformer-base-4096). Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of [`allenai/longformer-base-4096`](https://huggingface.co/allenai/longformer-base-4096) was used. ## Model description Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-longformer-base-mixed-domain") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-longformer-base-mixed-domain") model = AutoModel.from_pretrained("joeranbosma/dragon-longformer-base-mixed-domain") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 5e-05 - `train_batch_size`: 2 - `eval_batch_size`: 2 - `seed`: 42 - `gradient_accumulation_steps`: 8 - `total_train_batch_size`: 16 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 3.0 - `max_seq_length`: 4096 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-longformer-base-mixed-domain

2024-05-03T09:41:23+00:00

TAGS #transformers #pytorch #longformer #fill-mask #doi-10.57967/hf/2172 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON Longformer base mixed-domain =================================== Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was first pretrained using general domain data, as specified here. The pretrained model was taken from HuggingFace: 'allenai/longformer-base-4096'. Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of 'allenai/longformer-base-4096' was used. Model description ----------------- Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 5e-05 * 'train\_batch\_size': 2 * 'eval\_batch\_size': 2 * 'seed': 42 * 'gradient\_accumulation\_steps': 8 * 'total\_train\_batch\_size': 16 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 3.0 * 'max\_seq\_length': 4096 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

fill-mask

transformers

# DRAGON Longformer large mixed-domain Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was first pretrained using general domain data, as specified [here](https://huggingface.co/allenai/longformer-large-4096). The pretrained model was taken from HuggingFace: [`allenai/longformer-large-4096`](https://huggingface.co/allenai/longformer-large-4096). Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of [`allenai/longformer-large-4096`](https://huggingface.co/allenai/longformer-large-4096) was used. ## Model description Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-longformer-large-mixed-domain") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-longformer-large-mixed-domain") model = AutoModel.from_pretrained("joeranbosma/dragon-longformer-large-mixed-domain") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 5e-05 - `train_batch_size`: 4 - `eval_batch_size`: 4 - `seed`: 42 - `gradient_accumulation_steps`: 4 - `total_train_batch_size`: 16 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 3.0 - `max_seq_length`: 4096 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-longformer-large-mixed-domain

2024-05-03T09:41:26+00:00

TAGS #transformers #pytorch #longformer #fill-mask #doi-10.57967/hf/2174 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON Longformer large mixed-domain ==================================== Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was first pretrained using general domain data, as specified here. The pretrained model was taken from HuggingFace: 'allenai/longformer-large-4096'. Subsequently, the model was pretrained using domain-specific data (i.e., clinical reports). The tokenizer of 'allenai/longformer-large-4096' was used. Model description ----------------- Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 5e-05 * 'train\_batch\_size': 4 * 'eval\_batch\_size': 4 * 'seed': 42 * 'gradient\_accumulation\_steps': 4 * 'total\_train\_batch\_size': 16 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 3.0 * 'max\_seq\_length': 4096 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

fill-mask

transformers

# DRAGON RoBERTa large domain-specific Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as [`xlm-roberta-large`](https://huggingface.co/xlm-roberta-large) from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as [`roberta-base`](https://huggingface.co/FacebookAI/roberta-base). ## Model description RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-roberta-large-domain-specific") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-roberta-large-domain-specific") model = AutoModel.from_pretrained("joeranbosma/dragon-roberta-large-domain-specific") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 1e-4 - `train_batch_size`: 8 - `eval_batch_size`: 8 - `seed`: 42 - `gradient_accumulation_steps`: 32 - `total_train_batch_size`: 256 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 10.0 - `max_seq_length`: 512 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-roberta-large-domain-specific

2024-05-03T09:41:28+00:00

TAGS #transformers #pytorch #roberta #fill-mask #doi-10.57967/hf/2171 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON RoBERTa large domain-specific ==================================== Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as 'xlm-roberta-large' from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as 'roberta-base'. Model description ----------------- RoBERTa is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 1e-4 * 'train\_batch\_size': 8 * 'eval\_batch\_size': 8 * 'seed': 42 * 'gradient\_accumulation\_steps': 32 * 'total\_train\_batch\_size': 256 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 10.0 * 'max\_seq\_length': 512 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

fill-mask

transformers

# DRAGON Longformer base domain-specific Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as [`allenai/longformer-base-4096`](https://huggingface.co/allenai/longformer-base-4096) from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as [`roberta-base`](https://huggingface.co/FacebookAI/roberta-base). ## Model description Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-longformer-base-domain-specific") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-longformer-base-domain-specific") model = AutoModel.from_pretrained("joeranbosma/dragon-longformer-base-domain-specific") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 6e-4 - `train_batch_size`: 16 - `eval_batch_size`: 16 - `seed`: 42 - `gradient_accumulation_steps`: 16 - `total_train_batch_size`: 256 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 10.0 - `max_seq_length`: 4096 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-longformer-base-domain-specific

2024-05-03T09:41:30+00:00

TAGS #transformers #pytorch #longformer #fill-mask #doi-10.57967/hf/2173 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON Longformer base domain-specific ====================================== Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as 'allenai/longformer-base-4096' from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as 'roberta-base'. Model description ----------------- Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 6e-4 * 'train\_batch\_size': 16 * 'eval\_batch\_size': 16 * 'seed': 42 * 'gradient\_accumulation\_steps': 16 * 'total\_train\_batch\_size': 256 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 10.0 * 'max\_seq\_length': 4096 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

fill-mask

transformers

# DRAGON Longformer large domain-specific Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in [this](#pending) paper.&nbsp;The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as [`allenai/longformer-large-4096`](https://huggingface.co/allenai/longformer-large-4096) from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as [`roberta-base`](https://huggingface.co/FacebookAI/roberta-base). ## Model description Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. ## Model variations Multiple architectures were pretrained for the DRAGON challenge. | Model | #params | Language | |------------------------|--------------------------------|-------| | [`joeranbosma/dragon-bert-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-bert-base-mixed-domain) | 109M | Dutch → Dutch | | [`joeranbosma/dragon-roberta-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-base-mixed-domain) | 278M | Multiple → Dutch | | [`joeranbosma/dragon-roberta-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-roberta-large-mixed-domain) | 560M | Multiple → Dutch | | [`joeranbosma/dragon-longformer-base-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-base-mixed-domain) | 149M | English → Dutch | | [`joeranbosma/dragon-longformer-large-mixed-domain`](https://huggingface.co/joeranbosma/dragon-longformer-large-mixed-domain) | 435M | English → Dutch | | [`joeranbosma/dragon-bert-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-bert-base-domain-specific) | 109M | Dutch | | [`joeranbosma/dragon-roberta-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-base-domain-specific) | 278M | Dutch | | [`joeranbosma/dragon-roberta-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-roberta-large-domain-specific) | 560M | Dutch | | [`joeranbosma/dragon-longformer-base-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-base-domain-specific) | 149M | Dutch | | [`joeranbosma/dragon-longformer-large-domain-specific`](https://huggingface.co/joeranbosma/dragon-longformer-large-domain-specific) | 435M | Dutch | ## Intended uses & limitations You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. ## How to use You can use this model directly with a pipeline for masked language modeling: ```python from transformers import pipeline unmasker = pipeline("fill-mask", model="joeranbosma/dragon-longformer-large-domain-specific") unmasker("Dit onderzoek geen aanwijzingen voor significant carcinoom. PIRADS <mask>.") ``` Here is how to use this model to get the features of a given text in PyTorch: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("joeranbosma/dragon-longformer-large-domain-specific") model = AutoModel.from_pretrained("joeranbosma/dragon-longformer-large-domain-specific") text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors="pt") output = model(**encoded_input) ``` ## Limitations and bias Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. ## Training data For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. ## Training procedure ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: - 15% of the tokens are masked. - In 80% of the cases, the masked tokens are replaced by `[MASK]`. - In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. - In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: [`run_mlm.py`](https://github.com/huggingface/transformers/blob/7c6ec195adbfcd22cb6baeee64dd3c24a4b80c74/examples/pytorch/language-modeling/run_mlm.py). ### Pretraining hyperparameters The following hyperparameters were used during pretraining: - `learning_rate`: 1e-4 - `train_batch_size`: 4 - `eval_batch_size`: 4 - `seed`: 42 - `gradient_accumulation_steps`: 64 - `total_train_batch_size`: 256 - `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 - `lr_scheduler_type`: linear - `num_epochs`: 10.0 - `max_seq_length`: 4096 ### Framework versions - Transformers 4.29.0.dev0 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3 ## Evaluation results Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info ```bibtex @article{PENDING} ```

{"license": "cc-by-nc-sa-4.0"}

joeranbosma/dragon-longformer-large-domain-specific

2024-05-03T09:41:32+00:00

TAGS #transformers #pytorch #longformer #fill-mask #doi-10.57967/hf/2175 #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us

DRAGON Longformer large domain-specific ======================================= Pretrained model on Dutch clinical reports using a masked language modeling (MLM) objective. It was introduced in this paper. The model was pretrained using domain-specific data (i.e., clinical reports) from scratch. The architecture is the same as 'allenai/longformer-large-4096' from HuggingFace. The tokenizer was fitted to the dataset of Dutch medical reports, using the same settings for the tokenizer as 'roberta-base'. Model description ----------------- Longformer is a transformers model that was pretrained on a large corpus of Dutch clinical reports in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labeling them in any way with an automatic process to generate inputs and labels from those texts. This way, the model learns an inner representation of the Dutch medical language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled reports, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. Model variations ---------------- Multiple architectures were pretrained for the DRAGON challenge. Model: 'joeranbosma/dragon-bert-base-mixed-domain', #params: 109M, Language: Dutch → Dutch Model: 'joeranbosma/dragon-roberta-base-mixed-domain', #params: 278M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-roberta-large-mixed-domain', #params: 560M, Language: Multiple → Dutch Model: 'joeranbosma/dragon-longformer-base-mixed-domain', #params: 149M, Language: English → Dutch Model: 'joeranbosma/dragon-longformer-large-mixed-domain', #params: 435M, Language: English → Dutch Model: 'joeranbosma/dragon-bert-base-domain-specific', #params: 109M, Language: Dutch Model: 'joeranbosma/dragon-roberta-base-domain-specific', #params: 278M, Language: Dutch Model: 'joeranbosma/dragon-roberta-large-domain-specific', #params: 560M, Language: Dutch Model: 'joeranbosma/dragon-longformer-base-domain-specific', #params: 149M, Language: Dutch Model: 'joeranbosma/dragon-longformer-large-domain-specific', #params: 435M, Language: Dutch Intended uses & limitations --------------------------- You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. Note that this model is primarily aimed at being fine-tuned on tasks that use the whole text (e.g., a clinical report) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation you should look at model like GPT2. How to use ---------- You can use this model directly with a pipeline for masked language modeling: Here is how to use this model to get the features of a given text in PyTorch: Limitations and bias -------------------- Even if the training data used for this model could be characterized as fairly neutral, this model can have biased predictions. This bias will also affect all fine-tuned versions of this model. Training data ------------- For pretraining, 4,333,201 clinical reports (466,351 consecutive patients) were selected from Ziekenhuisgroep Twente from patients with a diagnostic or interventional visit between 13 July 2000 and 25 April 2023. 180,439 duplicate clinical reports (179,808 patients) were excluded, resulting in 4,152,762 included reports (463,692 patients). These reports were split into training (80%, 3,322,209 reports), validation (10%, 415,276 reports), and testing (10%, 415,277 reports). The testing reports were set aside for future analysis and are not used for pretraining. Training procedure ------------------ ### Pretraining The model was pretrained using masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then runs 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 masks the future tokens. It allows the model to learn a bidirectional representation of the sentence. The details of the masking procedure for each sentence are the following: * 15% of the tokens are masked. * In 80% of the cases, the masked tokens are replaced by '[MASK]'. * In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace. * In the 10% remaining cases, the masked tokens are left as is. The HuggingFace implementation was used for pretraining: 'run\_mlm.py'. ### Pretraining hyperparameters The following hyperparameters were used during pretraining: * 'learning\_rate': 1e-4 * 'train\_batch\_size': 4 * 'eval\_batch\_size': 4 * 'seed': 42 * 'gradient\_accumulation\_steps': 64 * 'total\_train\_batch\_size': 256 * 'optimizer': Adam with betas=(0.9,0.999) and epsilon=1e-08 * 'lr\_scheduler\_type': linear * 'num\_epochs': 10.0 * 'max\_seq\_length': 4096 ### Framework versions * Transformers 4.29.0.dev0 * Pytorch 2.0.0+cu117 * Datasets 2.11.0 * Tokenizers 0.13.3 Evaluation results ------------------ Pending evaluation on the DRAGON benchmark. ### BibTeX entry and citation info

text-generation

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": []}

cilantro9246/dta0jvx

2024-05-03T09:42:25+00:00

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

¿Qué es Candid-ex Pastillas? Candid-ex tabletas es una innovadora cápsula para bajar de peso formulada con una mezcla única de ingredientes naturales elegidos meticulosamente por sus potentes propiedades para quemar grasa. Elaborado por expertos en el campo de la nutrición y el bienestar, este suplemento está diseñado para ayudar a las personas en su camino hacia un cuerpo más sano y delgado. Página web oficial:<a href="https://www.nutritionsee.com/candeexgs">www.Candid-ex.com</a> <p><a href="https://www.nutritionsee.com/candeexgs"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/05/Candid-ex-Mexico-1.png" alt="enter image description here"> </a></p> <a href="https://www.nutritionsee.com/candeexgs">¡¡Comprar ahora!! Haga clic en el enlace a continuación para obtener más información y obtener un 50% de descuento ahora... ¡Date prisa!</a> Página web oficial:<a href="https://www.nutritionsee.com/candeexgs">www.Candid-ex.com</a>

{"license": "apache-2.0"}

Candid-exMexico/Candid-ex

2024-05-03T09:43:45+00:00

TAGS #license-apache-2.0 #region-us

¿Qué es Candid-ex Pastillas? Candid-ex tabletas es una innovadora cápsula para bajar de peso formulada con una mezcla única de ingredientes naturales elegidos meticulosamente por sus potentes propiedades para quemar grasa. Elaborado por expertos en el campo de la nutrición y el bienestar, este suplemento está diseñado para ayudar a las personas en su camino hacia un cuerpo más sano y delgado. Página web oficial:<a href="URL <p><a href="URL <img src="URL alt="enter image description here"> </a></p> <a href="URL¡¡Comprar ahora!! Haga clic en el enlace a continuación para obtener más información y obtener un 50% de descuento ahora... ¡Date prisa!</a> Página web oficial:<a href="URL

feature-extraction

sentence-transformers

The model is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for the following use case: This model is designed to support various applications in natural language processing and understanding. ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started: ```python from transformers import AutoModel, AutoTokenizer llm_name = "jina-embeddings-v2-base-en-03052024-im2p-webapp" tokenizer = AutoTokenizer.from_pretrained(llm_name) model = AutoModel.from_pretrained(llm_name, trust_remote_code=True) tokens = tokenizer("Your text here", return_tensors="pt") embedding = model(**tokens) ```

{"language": ["en", "en", "en", "en", "en", "en", "en"], "license": "mit", "tags": ["sentence-transformers", "PyTorch", "Core ML", "ONNX", "allenai/c4", "sentence-similarity", "feature-extraction", "Toys", "Children", "Games", "Educational", "Entertainment"], "datasets": ["fine-tuned/jina-embeddings-v2-base-en-03052024-im2p-webapp"], "pipeline_tag": "feature-extraction"}

fine-tuned/jina-embeddings-v2-base-en-03052024-im2p-webapp

2024-05-03T09:44:33+00:00

TAGS #sentence-transformers #safetensors #bert #PyTorch #Core ML #ONNX #allenai/c4 #sentence-similarity #feature-extraction #Toys #Children #Games #Educational #Entertainment #custom_code #en #dataset-fine-tuned/jina-embeddings-v2-base-en-03052024-im2p-webapp #license-mit #endpoints_compatible #region-us

The model is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for the following use case: This model is designed to support various applications in natural language processing and understanding. ## How to Use This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:

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": []}

aho-tai/test

2024-05-03T09:45:00+00:00

TAGS #transformers #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

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 0.7.1

{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}

pechaut/Mistral-7b-bridge-v0.1

2024-05-03T09:45:07+00:00

TAGS #peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #region-us

# Model Card for Model ID ## Model Details ### Model Description - Developed by: - Funded by [optional]: - Shared by [optional]: - Model type: - Language(s) (NLP): - License: - Finetuned from model [optional]: ### Model Sources [optional] - Repository: - Paper [optional]: - Demo [optional]: ## Uses ### Direct Use ### Downstream Use [optional] ### Out-of-Scope Use ## Bias, Risks, and Limitations ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. ## Training Details ### Training Data ### Training Procedure #### Preprocessing [optional] #### Training Hyperparameters - Training regime: #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data #### Factors #### Metrics ### Results #### Summary ## Model Examination [optional] ## Environmental Impact Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). - Hardware Type: - Hours used: - Cloud Provider: - Compute Region: - Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software [optional] BibTeX: APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact ### Framework versions - PEFT 0.7.1

reinforcement-learning

# **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="cogni-kai/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}]}]}]}

cogni-kai/q-FrozenLake-v1-4x4-noSlippery

2024-05-03T09:45:12+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

text2text-generation

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. --> # bengali_news_article_summarization_mt5 This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.2111 ## 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: 20 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 160 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine_with_restarts - lr_scheduler_warmup_steps: 100 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 0.99 | 83 | 0.8963 | | No log | 2.0 | 167 | 0.3201 | | 9.149 | 2.99 | 250 | 0.2583 | | 9.149 | 3.99 | 334 | 0.2372 | | 0.3009 | 5.0 | 418 | 0.2298 | | 0.3009 | 5.99 | 501 | 0.2244 | | 0.3009 | 7.0 | 585 | 0.2213 | | 0.2524 | 8.0 | 669 | 0.2163 | | 0.2524 | 8.99 | 752 | 0.2136 | | 0.2306 | 10.0 | 836 | 0.2126 | | 0.2306 | 10.99 | 919 | 0.2117 | | 0.2176 | 11.99 | 1003 | 0.2120 | | 0.2176 | 13.0 | 1087 | 0.2116 | | 0.2176 | 13.99 | 1170 | 0.2111 | | 0.2119 | 14.89 | 1245 | 0.2111 | ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/mt5-small", "model-index": [{"name": "bengali_news_article_summarization_mt5", "results": []}]}

fahad1770/bengali_news_article_summarization_mt5

2024-05-03T09:45:23+00:00

TAGS #transformers #safetensors #mt5 #text2text-generation #generated_from_trainer #base_model-google/mt5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

bengali\_news\_article\_summarization\_mt5 ========================================== This model is a fine-tuned version of google/mt5-small on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.2111 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: 20 * eval\_batch\_size: 16 * seed: 42 * gradient\_accumulation\_steps: 8 * total\_train\_batch\_size: 160 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine\_with\_restarts * lr\_scheduler\_warmup\_steps: 100 * num\_epochs: 15 ### Training results ### Framework versions * Transformers 4.39.3 * Pytorch 2.1.2 * Datasets 2.18.0 * Tokenizers 0.15.2

text-generation

transformers

Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of [meta-llama/Meta-Llama-3-70B-Instruct ](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct). For this quantization, we used 1 codebook of 16 bits. Results (in progress): | Model | Quantization | Model size, Gb | |------|------|------| |meta-llama/Meta-Llama-3-70B-Instruct | - | 141.2 | | | 1x16 | 21.9 |

{"library_name": "transformers", "tags": ["llama", "facebook", "meta", "llama-3", "conversational", "text-generation-inference"]}

ISTA-DASLab/Meta-Llama-3-70B-Instruct-AQLM-2Bit-1x16

2024-05-03T09:45:59+00:00

TAGS #transformers #safetensors #llama #text-generation #facebook #meta #llama-3 #conversational #text-generation-inference #arxiv-2401.06118 #autotrain_compatible #endpoints_compatible #region-us

Official AQLM quantization of meta-llama/Meta-Llama-3-70B-Instruct . For this quantization, we used 1 codebook of 16 bits. Results (in progress): Model: meta-llama/Meta-Llama-3-70B-Instruct, Quantization: -, Model size, Gb: 141.2 Model: , Quantization: 1x16, Model size, Gb: 21.9

other

transformers

# Chronos-T5 (Tiny) Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper [Chronos: Learning the Language of Time Series](https://arxiv.org/abs/2403.07815). <p align="center"> <img src="figures/main-figure.png" width="100%"> <br /> <span> Fig. 1: High-level depiction of Chronos. (<b>Left</b>) The input time series is scaled and quantized to obtain a sequence of tokens. (<b>Center</b>) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (<b>Right</b>) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. </span> </p> --- ## Architecture The models in this repository are based on the [T5 architecture](https://arxiv.org/abs/1910.10683). The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. | Model | Parameters | Based on | | ---------------------------------------------------------------------- | ---------- | ---------------------------------------------------------------------- | | [**chronos-t5-tiny**](https://huggingface.co/amazon/chronos-t5-tiny) | 8M | [t5-efficient-tiny](https://huggingface.co/google/t5-efficient-tiny) | | [**chronos-t5-mini**](https://huggingface.co/amazon/chronos-t5-mini) | 20M | [t5-efficient-mini](https://huggingface.co/google/t5-efficient-mini) | | [**chronos-t5-small**](https://huggingface.co/amazon/chronos-t5-small) | 46M | [t5-efficient-small](https://huggingface.co/google/t5-efficient-small) | | [**chronos-t5-base**](https://huggingface.co/amazon/chronos-t5-base) | 200M | [t5-efficient-base](https://huggingface.co/google/t5-efficient-base) | | [**chronos-t5-large**](https://huggingface.co/amazon/chronos-t5-large) | 710M | [t5-efficient-large](https://huggingface.co/google/t5-efficient-large) | ## Usage To perform inference with Chronos models, install the package in the GitHub [companion repo](https://github.com/amazon-science/chronos-forecasting) by running: ``` pip install git+https://github.com/amazon-science/chronos-forecasting.git ``` A minimal example showing how to perform inference using Chronos models: ```python import matplotlib.pyplot as plt import numpy as np import pandas as pd import torch from chronos import ChronosPipeline pipeline = ChronosPipeline.from_pretrained( "amazon/chronos-t5-tiny", device_map="cuda", torch_dtype=torch.bfloat16, ) df = pd.read_csv("https://raw.githubusercontent.com/AileenNielsen/TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv") # context must be either a 1D tensor, a list of 1D tensors, # or a left-padded 2D tensor with batch as the first dimension context = torch.tensor(df["#Passengers"]) prediction_length = 12 forecast = pipeline.predict(context, prediction_length) # shape [num_series, num_samples, prediction_length] # visualize the forecast forecast_index = range(len(df), len(df) + prediction_length) low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0) plt.figure(figsize=(8, 4)) plt.plot(df["#Passengers"], color="royalblue", label="historical data") plt.plot(forecast_index, median, color="tomato", label="median forecast") plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval") plt.legend() plt.grid() plt.show() ``` ## Citation If you find Chronos models useful for your research, please consider citing the associated [paper](https://arxiv.org/abs/2403.07815): ``` @article{ansari2024chronos, author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan, and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, title = {Chronos: Learning the Language of Time Series}, journal = {arXiv preprint arXiv:2403.07815}, year = {2024} } ``` ## Security See [CONTRIBUTING](CONTRIBUTING.md#security-issue-notifications) for more information. ## License This project is licensed under the Apache-2.0 License.

{"license": "apache-2.0", "tags": ["time series", "forecasting", "pretrained models", "foundation models", "time series foundation models", "time-series"], "pipeline_tag": "other"}

shchuro/chronos-t5-tiny-deploy

2024-05-03T09:47:16+00:00

TAGS #transformers #safetensors #t5 #text2text-generation #time series #forecasting #pretrained models #foundation models #time series foundation models #time-series #other #arxiv-2403.07815 #arxiv-1910.10683 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Chronos-T5 (Tiny) ================= Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes. For details on Chronos models, training data and procedures, and experimental results, please refer to the paper Chronos: Learning the Language of Time Series.  Fig. 1: High-level depiction of Chronos. (**Left**) The input time series is scaled and quantized to obtain a sequence of tokens. (**Center**) The tokens are fed into a language model which may either be an encoder-decoder or a decoder-only model. The model is trained using the cross-entropy loss. (**Right**) During inference, we autoregressively sample tokens from the model and map them back to numerical values. Multiple trajectories are sampled to obtain a predictive distribution. --- Architecture ------------ The models in this repository are based on the T5 architecture. The only difference is in the vocabulary size: Chronos-T5 models use 4096 different tokens, compared to 32128 of the original T5 models, resulting in fewer parameters. Model: chronos-t5-tiny, Parameters: 8M, Based on: t5-efficient-tiny Model: chronos-t5-mini, Parameters: 20M, Based on: t5-efficient-mini Model: chronos-t5-small, Parameters: 46M, Based on: t5-efficient-small Model: chronos-t5-base, Parameters: 200M, Based on: t5-efficient-base Model: chronos-t5-large, Parameters: 710M, Based on: t5-efficient-large Usage ----- To perform inference with Chronos models, install the package in the GitHub companion repo by running: A minimal example showing how to perform inference using Chronos models: If you find Chronos models useful for your research, please consider citing the associated paper: Security -------- See CONTRIBUTING for more information. License ------- This project is licensed under the Apache-2.0 License.

text-generation

transformers

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{"library_name": "transformers", "tags": []}

avemio-digital/llama3_entity_extraction_category_adapter_merge

2024-05-03T09:47:42+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

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text-generation

transformers

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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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{"library_name": "transformers", "tags": []}

andreidima/Mistral-7B-v0.1-Romanian-qlora

2024-05-03T09:48:17+00:00

TAGS #transformers #safetensors #mistral #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

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text-generation

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. 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{"library_name": "transformers", "tags": []}

golf2248/pwyek3f

2024-05-03T09:48:20+00:00

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

reinforcement-learning

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": "254.69 +/- 19.00", "name": "mean_reward", "verified": false}]}]}]}

Whiskas0663/ppo-LunarLander-v2

2024-05-03T09:48:40+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

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. --> # mal_tam_instruct_trans This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) 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: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 120 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "model-index": [{"name": "mal_tam_instruct_trans", "results": []}]}

ArunIcfoss/mal_tam_instruct_trans

2024-05-03T09:49:51+00:00

TAGS #peft #tensorboard #safetensors #trl #sft #unsloth #generated_from_trainer #base_model-unsloth/llama-3-8b-bnb-4bit #license-llama2 #region-us

# mal_tam_instruct_trans This model is a fine-tuned version of unsloth/llama-3-8b-bnb-4bit 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: 2 - eval_batch_size: 8 - seed: 3407 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 5 - training_steps: 120 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.2+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

text-generation

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. --> # 0.0001_withdpo_3iters_bs256_551lr_iter_3 This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_3iters_bs256_551lr_iter_2](https://huggingface.co/ShenaoZ/0.0001_withdpo_3iters_bs256_551lr_iter_2) on the updated and the original datasets. ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_3iters_bs256_551lr_iter_2", "model-index": [{"name": "0.0001_withdpo_3iters_bs256_551lr_iter_3", "results": []}]}

ShenaoZ/0.0001_withdpo_3iters_bs256_551lr_iter_3

2024-05-03T09:50:03+00:00

TAGS #transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #trl #dpo #conversational #dataset-updated #dataset-original #base_model-ShenaoZ/0.0001_withdpo_3iters_bs256_551lr_iter_2 #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# 0.0001_withdpo_3iters_bs256_551lr_iter_3 This model is a fine-tuned version of ShenaoZ/0.0001_withdpo_3iters_bs256_551lr_iter_2 on the updated and the original datasets. ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

automatic-speech-recognition

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-fine-tuned-large-v3-company-earnings-call-v0-aws This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0602 - Wer: 3.8548 ## 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: 16 - seed: 42 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 10 - training_steps: 80 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | No log | 0.6897 | 20 | 0.0781 | 4.5978 | | 0.1055 | 1.3793 | 40 | 0.0646 | 3.6611 | | 0.0452 | 2.0690 | 60 | 0.0602 | 3.5341 | | 0.024 | 2.7586 | 80 | 0.0602 | 3.8548 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-large-v3", "model-index": [{"name": "Whisper-fine-tuned-large-v3-company-earnings-call-v0-aws", "results": []}]}

MasatoShima1618/Whisper-fine-tuned-large-v3-company-earnings-call-v0-aws

2024-05-03T09:51:06+00:00

TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #base_model-openai/whisper-large-v3 #license-apache-2.0 #endpoints_compatible #region-us

Whisper-fine-tuned-large-v3-company-earnings-call-v0-aws ======================================================== This model is a fine-tuned version of openai/whisper-large-v3 on the None dataset. It achieves the following results on the evaluation set: * Loss: 0.0602 * Wer: 3.8548 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: 16 * seed: 42 * distributed\_type: multi-GPU * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 10 * training\_steps: 80 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

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-b0-scene-parse-150 This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the scene_parse_150 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "tags": ["generated_from_trainer"], "datasets": ["scene_parse_150"], "base_model": "nvidia/mit-b0", "model-index": [{"name": "segformer-b0-scene-parse-150", "results": []}]}

MVRonkin/segformer-b0-scene-parse-150

2024-05-03T09:52:56+00:00

TAGS #transformers #tensorboard #safetensors #segformer #generated_from_trainer #dataset-scene_parse_150 #base_model-nvidia/mit-b0 #license-other #endpoints_compatible #region-us

# segformer-b0-scene-parse-150 This model is a fine-tuned version of nvidia/mit-b0 on the scene_parse_150 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: 6e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

image-classification

transformers

# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metrics loss: 0.22053596377372742 f1_macro: 0.881587062204185 f1_micro: 0.9269375213383407 f1_weighted: 0.9267500134300362 precision_macro: 0.9520135455160805 precision_micro: 0.9269375213383407 precision_weighted: 0.932072731880276 recall_macro: 0.8425714533291321 recall_micro: 0.9269375213383407 recall_weighted: 0.9269375213383407 accuracy: 0.9269375213383407

{"tags": ["autotrain", "image-classification"], "datasets": ["autotrain-beit-base-patch16-224/autotrain-data"], "widget": [{"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg", "example_title": "Tiger"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg", "example_title": "Teapot"}, {"src": "https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg", "example_title": "Palace"}]}

Kushagra07/autotrain-beit-base-patch16-224

2024-05-03T09:52:57+00:00

TAGS #transformers #tensorboard #safetensors #beit #image-classification #autotrain #dataset-autotrain-beit-base-patch16-224/autotrain-data #autotrain_compatible #endpoints_compatible #region-us

# Model Trained Using AutoTrain - Problem type: Image Classification ## Validation Metrics loss: 0.22053596377372742 f1_macro: 0.881587062204185 f1_micro: 0.9269375213383407 f1_weighted: 0.9267500134300362 precision_macro: 0.9520135455160805 precision_micro: 0.9269375213383407 precision_weighted: 0.932072731880276 recall_macro: 0.8425714533291321 recall_micro: 0.9269375213383407 recall_weighted: 0.9269375213383407 accuracy: 0.9269375213383407

reinforcement-learning

# **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="cogni-kai/q-FrozenLake-weird-values", 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-weird-values", "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}]}]}]}

cogni-kai/q-FrozenLake-weird-values

2024-05-03T09:53:31+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

text-classification

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. --> # robust_llm_pythia-31m_niki-041a_imdb_random-token-1280_10-rounds_seed-2 This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_niki-041a_imdb_random-token-1280_10-rounds_seed-2", "results": []}]}

AlignmentResearch/robust_llm_pythia-31m_niki-041a_imdb_random-token-1280_10-rounds_seed-2

2024-05-03T09:54:06+00:00

TAGS #transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-31m #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# robust_llm_pythia-31m_niki-041a_imdb_random-token-1280_10-rounds_seed-2 This model is a fine-tuned version of EleutherAI/pythia-31m on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.39.3 - Pytorch 2.2.1 - Datasets 2.18.0 - Tokenizers 0.15.2

text-generation

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. 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]

{"library_name": "transformers", "tags": []}

JetBrains-Research/traj0.1-llama3

2024-05-03T09:54:54+00:00

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

text-classification

setfit

# SetFit with deepset/gbert-large-sts This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [deepset/gbert-large-sts](https://huggingface.co/deepset/gbert-large-sts) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - **Model Type:** SetFit - **Sentence Transformer body:** [deepset/gbert-large-sts](https://huggingface.co/deepset/gbert-large-sts) - **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance - **Maximum Sequence Length:** 512 tokens - **Number of Classes:** 2 classes <!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> <!-- - **Language:** Unknown --> <!-- - **License:** Unknown --> ### Model Sources - **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) - **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) - **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) ## Uses ### Direct Use for Inference First install the SetFit library: ```bash pip install setfit ``` Then you can load this model and run inference. ```python from setfit import SetFitModel # Download from the 🤗 Hub model = SetFitModel.from_pretrained("tstadel/answer-classification-setfit-v2-binary-german") # Run inference preds = model("I loved the spiderman movie!") ``` <!-- ### Downstream Use *List how someone could finetune this model on their own dataset.* --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* --> <!-- ## Bias, Risks and Limitations *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* --> <!-- ### Recommendations *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* --> ## Training Details ### Framework Versions - Python: 3.10.9 - SetFit: 1.0.3 - Sentence Transformers: 2.7.0 - Transformers: 4.37.2 - PyTorch: 1.13.1+cu117 - Datasets: 2.19.0 - Tokenizers: 0.15.2 ## Citation ### BibTeX ```bibtex @article{https://doi.org/10.48550/arxiv.2209.11055, doi = {10.48550/ARXIV.2209.11055}, url = {https://arxiv.org/abs/2209.11055}, author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren}, keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Efficient Few-Shot Learning Without Prompts}, publisher = {arXiv}, year = {2022}, copyright = {Creative Commons Attribution 4.0 International} } ``` <!-- ## Glossary *Clearly define terms in order to be accessible across audiences.* --> <!-- ## Model Card Authors *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* --> <!-- ## Model Card Contact *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.* -->

{"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "base_model": "deepset/gbert-large-sts", "widget": [], "pipeline_tag": "text-classification", "inference": true}

tstadel/answer-classification-setfit-v2-binary-german

2024-05-03T09:55:12+00:00

TAGS #setfit #safetensors #bert #sentence-transformers #text-classification #generated_from_setfit_trainer #arxiv-2209.11055 #base_model-deepset/gbert-large-sts #region-us

# SetFit with deepset/gbert-large-sts This is a SetFit model that can be used for Text Classification. This SetFit model uses deepset/gbert-large-sts as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification. The model has been trained using an efficient few-shot learning technique that involves: 1. Fine-tuning a Sentence Transformer with contrastive learning. 2. Training a classification head with features from the fine-tuned Sentence Transformer. ## Model Details ### Model Description - Model Type: SetFit - Sentence Transformer body: deepset/gbert-large-sts - Classification head: a LogisticRegression instance - Maximum Sequence Length: 512 tokens - Number of Classes: 2 classes ### Model Sources - Repository: SetFit on GitHub - Paper: Efficient Few-Shot Learning Without Prompts - Blogpost: SetFit: Efficient Few-Shot Learning Without Prompts ## Uses ### Direct Use for Inference First install the SetFit library: Then you can load this model and run inference. ## Training Details ### Framework Versions - Python: 3.10.9 - SetFit: 1.0.3 - Sentence Transformers: 2.7.0 - Transformers: 4.37.2 - PyTorch: 1.13.1+cu117 - Datasets: 2.19.0 - Tokenizers: 0.15.2 ### BibTeX

reinforcement-learning

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": "231.81 +/- 37.04", "name": "mean_reward", "verified": false}]}]}]}

Sweety07/ppo-LunarLander-v2

2024-05-03T09:55:38+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

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. --> # nash_dpo_merge_iter_real_plus_3 This model is a fine-tuned version of [YYYYYYibo/nash_dpo_merge_iter_3](https://huggingface.co/YYYYYYibo/nash_dpo_merge_iter_3) on the updated and the original datasets. ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

{"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo"], "datasets": ["updated", "original"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "nash_dpo_merge_iter_real_plus_3", "results": []}]}

YYYYYYibo/nash_dpo_merge_iter_real_plus_3

2024-05-03T09:55:39+00:00

TAGS #peft #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #dpo #dataset-updated #dataset-original #base_model-alignment-handbook/zephyr-7b-sft-full #license-apache-2.0 #region-us

# nash_dpo_merge_iter_real_plus_3 This model is a fine-tuned version of YYYYYYibo/nash_dpo_merge_iter_3 on the updated and the original datasets. ## 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: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 16 - total_train_batch_size: 128 - total_eval_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2

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. 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]

{"library_name": "transformers", "tags": []}

fmshahata/phi-moe-alt_

2024-05-03T09:57:27+00:00

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

text-generation

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. 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]

{"library_name": "transformers", "tags": []}

golf2248/2o6rqgb

2024-05-03T09:57:43+00:00

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

text-generation

transformers

# Uploaded model - **Developed by:** aminlouhichi - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

aminlouhichi/LLAMA3_SQL

2024-05-03T09:58:38+00:00

TAGS #transformers #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Uploaded model - Developed by: aminlouhichi - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

transformers

# Uploaded model - **Developed by:** dmorrigan - **License:** apache-2.0 - **Finetuned from model :** meta-llama/Meta-Llama-3-8B This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "meta-llama/Meta-Llama-3-8B"}

dmorrigan/HebrewLyricsLoRA-FULL-23K-8Epoch

2024-05-03T09:59:48+00:00

TAGS #transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-meta-llama/Meta-Llama-3-8B #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: dmorrigan - License: apache-2.0 - Finetuned from model : meta-llama/Meta-Llama-3-8B This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

<!-- header start --> <!-- 200823 --> <div style="width: auto; margin-left: auto; margin-right: auto"> <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer"> <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;"> </a> </div> <!-- header end --> [](https://twitter.com/PrunaAI) [](https://github.com/PrunaAI) [](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following) [](https://discord.com/invite/vb6SmA3hxu) ## This repo contains GGUF versions of the nvidia/Llama3-ChatQA-1.5-8B model. # Simply make AI models cheaper, smaller, faster, and greener! - Give a thumbs up if you like this model! - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) - Join Pruna AI community on Discord [here](https://discord.com/invite/vb6SmA3hxu) to share feedback/suggestions or get help. **Frequently Asked Questions** - ***How does the compression work?*** The model is compressed with GGUF. - ***How does the model quality change?*** The quality of the model output might vary compared to the base model. - ***What is the model format?*** We use GGUF format. - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data. - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). # Downloading and running the models You can download the individual files from the Files & versions section. Here is a list of the different versions we provide. For more info checkout [this chart](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) and [this guide](https://www.reddit.com/r/LocalLLaMA/comments/1ba55rj/overview_of_gguf_quantization_methods/): | Quant type | Description | |------------|--------------------------------------------------------------------------------------------| | Q5_K_M | High quality, recommended. | | Q5_K_S | High quality, recommended. | | Q4_K_M | Good quality, uses about 4.83 bits per weight, recommended. | | Q4_K_S | Slightly lower quality with more space savings, recommended. | | IQ4_NL | Decent quality, slightly smaller than Q4_K_S with similar performance, recommended. | | IQ4_XS | Decent quality, smaller than Q4_K_S with similar performance, recommended. | | Q3_K_L | Lower quality but usable, good for low RAM availability. | | Q3_K_M | Even lower quality. | | IQ3_M | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | IQ3_S | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. | | Q3_K_S | Low quality, not recommended. | | IQ3_XS | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | Q2_K | Very low quality but surprisingly usable. | ## How to download GGUF files ? **Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev - **Option A** - Downloading in `text-generation-webui`: - **Step 1**: Under Download Model, you can enter the model repo: PrunaAI/Llama3-ChatQA-1.5-8B-GGUF-smashed and below it, a specific filename to download, such as: phi-2.IQ3_M.gguf. - **Step 2**: Then click Download. - **Option B** - Downloading on the command line (including multiple files at once): - **Step 1**: We recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` - **Step 2**: Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download PrunaAI/Llama3-ChatQA-1.5-8B-GGUF-smashed Llama3-ChatQA-1.5-8B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> Alternatively, you can also download multiple files at once with a pattern: ```shell huggingface-cli download PrunaAI/Llama3-ChatQA-1.5-8B-GGUF-smashed --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install hf_transfer ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download PrunaAI/Llama3-ChatQA-1.5-8B-GGUF-smashed Llama3-ChatQA-1.5-8B.IQ3_M.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## How to run model in GGUF format? - **Option A** - Introductory example with `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Llama3-ChatQA-1.5-8B.IQ3_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt\} [/INST]" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) - **Option B** - Running in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20-%20Model%20Tab.md#llamacpp). - **Option C** - Running from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Llama3-ChatQA-1.5-8B.IQ3_M.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<s>[INST] {prompt} [/INST]", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Llama3-ChatQA-1.5-8B.IQ3_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` - **Option D** - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Configurations The configuration info are in `smash_config.json`. ## Credits & License The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi. ## Want to compress other models? - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact). - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).

{"tags": ["pruna-ai"], "metrics": ["memory_disk", "memory_inference", "inference_latency", "inference_throughput", "inference_CO2_emissions", "inference_energy_consumption"], "thumbnail": "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"}

PrunaAI/Llama3-ChatQA-1.5-8B-GGUF-smashed

2024-05-03T10:00:53+00:00

TAGS #gguf #pruna-ai #region-us

[](URL target=) : * Step 1: We recommend using the 'huggingface-hub' Python library: * Step 2: Then you can download any individual model file to the current directory, at high speed, with a command like this: More advanced huggingface-cli download usage (click to read) Alternatively, you can also download multiple files at once with a pattern: For more documentation on downloading with 'huggingface-cli', please see: HF -> Hub Python Library -> Download files -> Download from the CLI. To accelerate downloads on fast connections (1Gbit/s or higher), install 'hf\_transfer': And set environment variable 'HF\_HUB\_ENABLE\_HF\_TRANSFER' to '1': Windows Command Line users: You can set the environment variable by running 'set HF\_HUB\_ENABLE\_HF\_TRANSFER=1' before the download command. How to run model in GGUF format? -------------------------------- * Option A - Introductory example with 'URL' command Make sure you are using 'URL' from commit d0cee0d or later. Change '-ngl 32' to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change '-c 32768' to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by URL automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the '-p ' argument with '-i -ins' For other parameters and how to use them, please refer to the URL documentation * Option B - Running in 'text-generation-webui' Further instructions can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model URL. * Option C - Running from Python code You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ``` ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: llama-cpp-python docs. #### First install the package Run one of the following commands, according to your system: #### Simple llama-cpp-python example code ``` * Option D - Running with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * LangChain + llama-cpp-python * LangChain + ctransformers Configurations -------------- The configuration info are in 'smash\_config.json'. Credits & License ----------------- The license of the smashed model follows the license of the original model. Please check the license of the original model before using this model which provided the base model. The license of the 'pruna-engine' is here on Pypi. Want to compress other models? ------------------------------ * Contact us and tell us which model to compress next here. * Request access to easily compress your own AI models here.

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. --> # OpenHermes_on_charttotext This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) 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: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.2 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "teknium/OpenHermes-2.5-Mistral-7B", "model-index": [{"name": "OpenHermes_on_charttotext", "results": []}]}

moetezsa/OpenHermes_on_charttotext

2024-05-03T10:07:26+00:00

TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-teknium/OpenHermes-2.5-Mistral-7B #license-apache-2.0 #region-us

# OpenHermes_on_charttotext This model is a fine-tuned version of teknium/OpenHermes-2.5-Mistral-7B 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: 1e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.1.2 - Datasets 2.19.0 - Tokenizers 0.19.1

text-classification

transformers

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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": []}

Lakshit11/BERT-15-categories-retrained-iter2

2024-05-03T10:08:30+00:00

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

text-generation

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. 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{"library_name": "transformers", "tags": []}

46an/llama-2-7b-waziai

2024-05-03T10:08:30+00:00

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

transformers

# Uploaded model - **Developed by:** Falah - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

Falah/lora_model_mental_health_llama3

2024-05-03T10:09:47+00:00

TAGS #transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: Falah - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-bnb-4bit This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

automatic-speech-recognition

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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(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": []}

shtapm/whisper-large_0502_decoder31_200steps

2024-05-03T10:10:03+00:00

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

text-generation

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": []}

cilantro9246/ngqdlvx

2024-05-03T10:10:20+00:00

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

automatic-speech-recognition

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. 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]

{"library_name": "transformers", "tags": []}

Mihaj/w2v-bert-karelian-CodeSwitching-with-all-aug

2024-05-03T10:10:23+00:00

TAGS #transformers #tensorboard #safetensors #wav2vec2-bert #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

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. --> # loha_fine_tuned_copa This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7712 - Accuracy: 0.52 - F1: 0.5208 ## 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: 0.003 - 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 - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7031 | 1.0 | 50 | 0.6881 | 0.54 | 0.5411 | | 0.692 | 2.0 | 100 | 0.6918 | 0.46 | 0.4613 | | 0.6983 | 3.0 | 150 | 0.6993 | 0.48 | 0.4800 | | 0.7013 | 4.0 | 200 | 0.6969 | 0.48 | 0.4771 | | 0.6993 | 5.0 | 250 | 0.6922 | 0.53 | 0.5312 | | 0.7012 | 6.0 | 300 | 0.6921 | 0.51 | 0.5110 | | 0.6636 | 7.0 | 350 | 0.7049 | 0.53 | 0.5310 | | 0.5915 | 8.0 | 400 | 0.7712 | 0.52 | 0.5208 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.1.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "google-bert/bert-base-uncased", "model-index": [{"name": "loha_fine_tuned_copa", "results": []}]}

anzeo/loha_fine_tuned_copa

2024-05-03T10:11:20+00:00

TAGS #peft #tensorboard #safetensors #generated_from_trainer #base_model-google-bert/bert-base-uncased #license-apache-2.0 #region-us

loha\_fine\_tuned\_copa ======================= This model is a fine-tuned version of google-bert/bert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.7712 * Accuracy: 0.52 * F1: 0.5208 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: 0.003 * 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 * training\_steps: 400 ### Training results ### Framework versions * PEFT 0.10.1.dev0 * Transformers 4.40.1 * Pytorch 2.1.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

reinforcement-learning

stable-baselines3

# **A2C** Agent playing **PandaPickAndPlace-v3** This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

{"library_name": "stable-baselines3", "tags": ["PandaPickAndPlace-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "PandaPickAndPlace-v3", "type": "PandaPickAndPlace-v3"}, "metrics": [{"type": "mean_reward", "value": "-45.00 +/- 15.00", "name": "mean_reward", "verified": false}]}]}]}

lzacchini/a2c-PandaPickAndPlace-v3

2024-05-03T10:11:26+00:00

TAGS #stable-baselines3 #PandaPickAndPlace-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us

# A2C Agent playing PandaPickAndPlace-v3 This is a trained model of a A2C agent playing PandaPickAndPlace-v3 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code

text-generation

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. 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]

{"library_name": "transformers", "tags": []}

golf2248/t9y7z2e

2024-05-03T10:11:43+00:00

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

transformers

This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

{"tags": ["pytorch_model_hub_mixin", "model_hub_mixin"]}

BothBosu/cnn-scam-classifier-v1

2024-05-03T10:11:58+00:00

TAGS #transformers #safetensors #pytorch_model_hub_mixin #model_hub_mixin #endpoints_compatible #region-us

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: - Docs:

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) c4ai-command-r-v01 - bnb 4bits - Model creator: https://huggingface.co/CohereForAI/ - Original model: https://huggingface.co/CohereForAI/c4ai-command-r-v01/ Original model description: --- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 --- # Model Card for C4AI Command-R 🚨 **This model is non-quantized version of C4AI Command-R. You can find the quantized version of C4AI Command-R using bitsandbytes [here](https://huggingface.co/CohereForAI/c4ai-command-r-v01-4bit)**. ## Model Summary C4AI Command-R is a research release of a 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities. Developed by: Cohere and [Cohere For AI](https://cohere.for.ai) - Point of Contact: Cohere For AI: [cohere.for.ai](https://cohere.for.ai/) - License: [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license), requires also adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy) - Model: c4ai-command-r-v01 - Model Size: 35 billion parameters - Context length: 128K **Try C4AI Command R** If you want to try Command R before downloading the weights, the model is hosted in a hugging face space [here](https://huggingface.co/spaces/CohereForAI/c4ai-command-r-v01). **Usage** Please use `transformers` version 4.39.1 or higher ```python # pip install 'transformers>=4.39.1' from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "CohereForAI/c4ai-command-r-v01" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id) # Format message with the command-r chat template messages = [{"role": "user", "content": "Hello, how are you?"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` **Quantized model through bitsandbytes, 8-bit precision** ```python # pip install 'transformers>=4.39.1' bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig bnb_config = BitsAndBytesConfig(load_in_8bit=True) model_id = "CohereForAI/c4ai-command-r-v01" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config) # Format message with the command-r chat template messages = [{"role": "user", "content": "Hello, how are you?"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") ## <BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello, how are you?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> gen_tokens = model.generate( input_ids, max_new_tokens=100, do_sample=True, temperature=0.3, ) gen_text = tokenizer.decode(gen_tokens[0]) print(gen_text) ``` **Quantized model through bitsandbytes, 4-bit precision** You can find a quantized version of this model to 4-bit precision [here](https://huggingface.co/CohereForAI/c4ai-command-r-v01-4bit). ## Model Details **Input**: Models input text only. **Output**: Models generate text only. **Model Architecture**: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. **Languages covered**: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic. Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian. **Context length**: Command-R supports a context length of 128K. ### Tool use capabilities: Command-R has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation. Command-R’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command-R may use one of its supplied tools more than once. The model has been trained to recognise a special `directly_answer` tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the `directly_answer` tool, but it can be removed or renamed if required. Comprehensive documentation for working with command-R's tool use prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). The code snippet below shows a minimal working example on how to render a prompt. <details> <summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary> ```python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-v01" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # Define tools available for the model to use: tools = [ { "name": "internet_search", "description": "Returns a list of relevant document snippets for a textual query retrieved from the internet", "parameter_definitions": { "query": { "description": "Query to search the internet with", "type": 'str', "required": True } } }, { 'name': "directly_answer", "description": "Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history", 'parameter_definitions': {} } ] # render the tool use prompt as a string: tool_use_prompt = tokenizer.apply_tool_use_template( conversation, tools=tools, tokenize=False, add_generation_prompt=True, ) print(tool_use_prompt) ``` </details> <details> <summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary> ```` <BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling. ## Available Tools Here is a list of tools that you have available to you: ```python def internet_search(query: str) -> List[Dict]: """Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query (str): Query to search the internet with """ pass ``` ```python def directly_answer() -> List[Dict]: """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass ```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Write 'Action:' followed by a json-formatted list of actions that you want to perform in order to produce a good response to the user's last input. You can use any of the supplied tools any number of times, but you should aim to execute the minimum number of necessary actions for the input. You should use the `directly-answer` tool if calling the other tools is unnecessary. The list of actions you want to call should be formatted as a list of json objects, for example: ```json [ { "tool_name": title of the tool in the specification, "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters } ]```<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ```` </details> <details> <summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary> ```` Action: ```json [ { "tool_name": "internet_search", "parameters": { "query": "biggest penguin in the world" } } ] ``` ```` </details> ### Grounded Generation and RAG Capabilities: Command-R has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG).This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation. Command-R’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured. By default, Command-R will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as `accurate` grounded generation. The model is trained with a number of other answering modes, which can be selected by prompt changes . A `fast` citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens. Comprehensive documentation for working with command-R's grounded generation prompt template can be found [here](https://docs.cohere.com/docs/prompting-command-r). The code snippet below shows a minimal working example on how to render a prompt. <details> <summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary> ````python from transformers import AutoTokenizer model_id = "CohereForAI/c4ai-command-r-v01" tokenizer = AutoTokenizer.from_pretrained(model_id) # define conversation input: conversation = [ {"role": "user", "content": "Whats the biggest penguin in the world?"} ] # define documents to ground on: documents = [ { "title": "Tall penguins", "text": "Emperor penguins are the tallest growing up to 122 cm in height." }, { "title": "Penguin habitats", "text": "Emperor penguins only live in Antarctica."} ] # render the tool use prompt as a string: grounded_generation_prompt = tokenizer.apply_grounded_generation_template( conversation, documents=documents, citation_mode="accurate", # or "fast" tokenize=False, add_generation_prompt=True, ) print(grounded_generation_prompt) ```` </details> <details> <summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary> ````<BOS_TOKEN><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|># Safety Preamble The instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral. # System Preamble ## Basic Rules You are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions. # User Preamble ## Task and Context You help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging. ## Style Guide Unless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Whats the biggest penguin in the world?<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|><results> Document: 0 title: Tall penguins text: Emperor penguins are the tallest growing up to 122 cm in height. Document: 1 title: Penguin habitats text: Emperor penguins only live in Antarctica. </results><|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>Carefully perform the following instructions, in order, starting each with a new line. Firstly, Decide which of the retrieved documents are relevant to the user's last input by writing 'Relevant Documents:' followed by comma-separated list of document numbers. If none are relevant, you should instead write 'None'. Secondly, Decide which of the retrieved documents contain facts that should be cited in a good answer to the user's last input by writing 'Cited Documents:' followed a comma-separated list of document numbers. If you dont want to cite any of them, you should instead write 'None'. Thirdly, Write 'Answer:' followed by a response to the user's last input in high quality natural english. Use the retrieved documents to help you. Do not insert any citations or grounding markup. Finally, Write 'Grounded answer:' followed by a response to the user's last input in high quality natural english. Use the symbols <co: doc> and </co: doc> to indicate when a fact comes from a document in the search result, e.g <co: 0>my fact</co: 0> for a fact from document 0.<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|> ```` </details> <details> <summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary> ```` Relevant Documents: 0,1 Cited Documents: 0,1 Answer: The Emperor Penguin is the tallest or biggest penguin in the world. It is a bird that lives only in Antarctica and grows to a height of around 122 centimetres. Grounded answer: The <co: 0>Emperor Penguin</co: 0> is the <co: 0>tallest</co: 0> or biggest penguin in the world. It is a bird that <co: 1>lives only in Antarctica</co: 1> and <co: 0>grows to a height of around 122 centimetres.</co: 0> ```` </details> ### Code Capabilities: Command-R has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions. ### Model Card Contact For errors or additional questions about details in this model card, contact [info@for.ai](mailto:info@for.ai). ### Terms of Use: We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a [CC-BY-NC](https://cohere.com/c4ai-cc-by-nc-license) License with an acceptable use addendum, and also requires adhering to [C4AI's Acceptable Use Policy](https://docs.cohere.com/docs/c4ai-acceptable-use-policy). ### Try Chat: You can try Command-R chat in the playground [here](https://dashboard.cohere.com/playground/chat).

{}

RichardErkhov/CohereForAI_-_c4ai-command-r-v01-4bits

2024-05-03T10:12:48+00:00

TAGS #transformers #safetensors #cohere #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models c4ai-command-r-v01 - bnb 4bits - Model creator: URL - Original model: URL Original model description: --- library_name: transformers language: - en - fr - de - es - it - pt - ja - ko - zh - ar license: cc-by-nc-4.0 --- # Model Card for C4AI Command-R This model is non-quantized version of C4AI Command-R. You can find the quantized version of C4AI Command-R using bitsandbytes here. ## Model Summary C4AI Command-R is a research release of a 35 billion parameter highly performant generative model. Command-R is a large language model with open weights optimized for a variety of use cases including reasoning, summarization, and question answering. Command-R has the capability for multilingual generation evaluated in 10 languages and highly performant RAG capabilities. Developed by: Cohere and Cohere For AI - Point of Contact: Cohere For AI: URL - License: CC-BY-NC, requires also adhering to C4AI's Acceptable Use Policy - Model: c4ai-command-r-v01 - Model Size: 35 billion parameters - Context length: 128K Try C4AI Command R If you want to try Command R before downloading the weights, the model is hosted in a hugging face space here. Usage Please use 'transformers' version 4.39.1 or higher Quantized model through bitsandbytes, 8-bit precision Quantized model through bitsandbytes, 4-bit precision You can find a quantized version of this model to 4-bit precision here. ## Model Details Input: Models input text only. Output: Models generate text only. Model Architecture: This is an auto-regressive language model that uses an optimized transformer architecture. After pretraining, this model uses supervised fine-tuning (SFT) and preference training to align model behavior to human preferences for helpfulness and safety. Languages covered: The model is optimized to perform well in the following languages: English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Simplified Chinese, and Arabic. Pre-training data additionally included the following 13 languages: Russian, Polish, Turkish, Vietnamese, Dutch, Czech, Indonesian, Ukrainian, Romanian, Greek, Hindi, Hebrew, Persian. Context length: Command-R supports a context length of 128K. ### Tool use capabilities: Command-R has been specifically trained with conversational tool use capabilities. These have been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template will likely reduce performance, but we encourage experimentation. Command-R’s tool use functionality takes a conversation as input (with an optional user-system preamble), along with a list of available tools. The model will then generate a json-formatted list of actions to execute on a subset of those tools. Command-R may use one of its supplied tools more than once. The model has been trained to recognise a special 'directly_answer' tool, which it uses to indicate that it doesn’t want to use any of its other tools. The ability to abstain from calling a specific tool can be useful in a range of situations, such as greeting a user, or asking clarifying questions. We recommend including the 'directly_answer' tool, but it can be removed or renamed if required. Comprehensive documentation for working with command-R's tool use prompt template can be found here. The code snippet below shows a minimal working example on how to render a prompt. <details> <summary><b>Usage: Rendering Tool Use Prompts [CLICK TO EXPAND]</b> </summary> </details> <details> <summary><b>Example Rendered Tool Use Prompt [CLICK TO EXPAND]</b></summary> python def internet_search(query: str) -> List[Dict]: """Returns a list of relevant document snippets for a textual query retrieved from the internet Args: query (str): Query to search the internet with """ pass python def directly_answer() -> List[Dict]: """Calls a standard (un-augmented) AI chatbot to generate a response given the conversation history """ pass json [ { "tool_name": title of the tool in the specification, "parameters": a dict of parameters to input into the tool as they are defined in the specs, or {} if it takes no parameters } ]' </details> <details> <summary><b>Example Rendered Tool Use Completion [CLICK TO EXPAND]</b></summary> json [ { "tool_name": "internet_search", "parameters": { "query": "biggest penguin in the world" } } ] ' </details> ### Grounded Generation and RAG Capabilities: Command-R has been specifically trained with grounded generation capabilities. This means that it can generate responses based on a list of supplied document snippets, and it will include grounding spans (citations) in its response indicating the source of the information. This can be used to enable behaviors such as grounded summarization and the final step of Retrieval Augmented Generation (RAG).This behavior has been trained into the model via a mixture of supervised fine-tuning and preference fine-tuning, using a specific prompt template. Deviating from this prompt template may reduce performance, but we encourage experimentation. Command-R’s grounded generation behavior takes a conversation as input (with an optional user-supplied system preamble, indicating task, context and desired output style), along with a list of retrieved document snippets. The document snippets should be chunks, rather than long documents, typically around 100-400 words per chunk. Document snippets consist of key-value pairs. The keys should be short descriptive strings, the values can be text or semi-structured. By default, Command-R will generate grounded responses by first predicting which documents are relevant, then predicting which ones it will cite, then generating an answer. Finally, it will then insert grounding spans into the answer. See below for an example. This is referred to as 'accurate' grounded generation. The model is trained with a number of other answering modes, which can be selected by prompt changes . A 'fast' citation mode is supported in the tokenizer, which will directly generate an answer with grounding spans in it, without first writing the answer out in full. This sacrifices some grounding accuracy in favor of generating fewer tokens. Comprehensive documentation for working with command-R's grounded generation prompt template can be found here. The code snippet below shows a minimal working example on how to render a prompt. <details> <summary> <b>Usage: Rendering Grounded Generation prompts [CLICK TO EXPAND]</b> </summary> ' </details> <details> <summary><b>Example Rendered Grounded Generation Prompt [CLICK TO EXPAND]</b></summary> ' </details> <details> <summary><b>Example Rendered Grounded Generation Completion [CLICK TO EXPAND]</b></summary> ' </details> ### Code Capabilities: Command-R has been optimized to interact with your code, by requesting code snippets, code explanations, or code rewrites. It might not perform well out-of-the-box for pure code completion. For better performance, we also recommend using a low temperature (and even greedy decoding) for code-generation related instructions. ### Model Card Contact For errors or additional questions about details in this model card, contact info@URL. ### Terms of Use: We hope that the release of this model will make community-based research efforts more accessible, by releasing the weights of a highly performant 35 billion parameter model to researchers all over the world. This model is governed by a CC-BY-NC License with an acceptable use addendum, and also requires adhering to C4AI's Acceptable Use Policy. ### Try Chat: You can try Command-R chat in the playground here.

text2text-generation

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. --> # mt5.gendered This model is a fine-tuned version of [samzirbo/mT5.en-es.pretrained](https://huggingface.co/samzirbo/mT5.en-es.pretrained) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.5115 - Bleu: 38.4985 - Meteor: 0.6605 - Chrf++: 60.6076 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - training_steps: 30000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Chrf++ | |:-------------:|:------:|:-----:|:---------------:|:-------:|:------:|:-------:| | 4.0658 | 0.3215 | 3000 | 2.1146 | 29.6159 | 0.587 | 53.1876 | | 2.3311 | 0.6431 | 6000 | 1.8553 | 33.4209 | 0.6198 | 56.5789 | | 2.0997 | 0.9646 | 9000 | 1.7308 | 35.2211 | 0.6359 | 58.0995 | | 1.9354 | 1.2862 | 12000 | 1.6645 | 36.311 | 0.6437 | 58.9512 | | 1.8583 | 1.6077 | 15000 | 1.6007 | 37.0671 | 0.6512 | 59.446 | | 1.8043 | 1.9293 | 18000 | 1.5639 | 37.7823 | 0.6553 | 60.0906 | | 1.728 | 2.2508 | 21000 | 1.5318 | 37.9166 | 0.6565 | 60.1504 | | 1.6989 | 2.5723 | 24000 | 1.5188 | 38.4261 | 0.6603 | 60.5626 | | 1.6854 | 2.8939 | 27000 | 1.5115 | 38.5249 | 0.6607 | 60.6204 | | 1.6696 | 3.2154 | 30000 | 1.5115 | 38.4985 | 0.6605 | 60.6076 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "samzirbo/mT5.en-es.pretrained", "model-index": [{"name": "mt5.gendered", "results": []}]}

samzirbo/mt5.gendered

2024-05-03T10:14:23+00:00

TAGS #transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #base_model-samzirbo/mT5.en-es.pretrained #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

mt5.gendered ============ This model is a fine-tuned version of samzirbo/URL-es.pretrained on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.5115 * Bleu: 38.4985 * Meteor: 0.6605 * Chrf++: 60.6076 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: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 1000 * training\_steps: 30000 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

text-generation

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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(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": []}

yleo/OrpoLlama-3-8B

2024-05-03T10:14:30+00:00

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

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. --> # Mistral-7B-v0.1_mbe_positive This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the mbe dataset. It achieves the following results on the evaluation set: - Loss: 1.0233 - Accuracy: 0.6809 ## 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: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.3217 | 0.07 | 10 | 0.7263 | 0.4901 | | 0.56 | 0.13 | 20 | 0.6898 | 0.5526 | | 0.5281 | 0.2 | 30 | 0.6465 | 0.5888 | | 0.994 | 0.27 | 40 | 0.7351 | 0.5987 | | 0.4785 | 0.33 | 50 | 0.6004 | 0.6118 | | 0.4732 | 0.4 | 60 | 0.5783 | 0.6349 | | 0.4466 | 0.47 | 70 | 0.5714 | 0.6414 | | 0.8737 | 0.53 | 80 | 0.5673 | 0.6184 | | 0.4471 | 0.6 | 90 | 0.5631 | 0.6283 | | 0.46 | 0.67 | 100 | 0.5504 | 0.6349 | | 0.3294 | 0.73 | 110 | 0.6010 | 0.625 | | 0.6526 | 0.8 | 120 | 0.5731 | 0.6283 | | 0.3712 | 0.87 | 130 | 0.5379 | 0.6447 | | 0.3341 | 0.93 | 140 | 0.5409 | 0.6283 | | 0.552 | 1.0 | 150 | 0.5311 | 0.6382 | | 0.4681 | 1.07 | 160 | 0.5371 | 0.6414 | | 0.3119 | 1.14 | 170 | 0.6172 | 0.6283 | | 0.3082 | 1.2 | 180 | 0.5361 | 0.6513 | | 0.5217 | 1.27 | 190 | 0.5468 | 0.625 | | 0.3888 | 1.34 | 200 | 0.5891 | 0.6316 | | 0.2841 | 1.4 | 210 | 0.5429 | 0.6283 | | 0.2728 | 1.47 | 220 | 0.5247 | 0.6382 | | 0.5563 | 1.54 | 230 | 0.5004 | 0.6513 | | 0.2862 | 1.6 | 240 | 0.4741 | 0.6546 | | 0.2289 | 1.67 | 250 | 0.5441 | 0.6513 | | 0.2481 | 1.74 | 260 | 0.5171 | 0.6513 | | 0.329 | 1.8 | 270 | 0.5371 | 0.6546 | | 0.1741 | 1.87 | 280 | 0.5412 | 0.6678 | | 0.2888 | 1.94 | 290 | 0.5131 | 0.6711 | | 0.4157 | 2.0 | 300 | 0.4555 | 0.6447 | | 0.1982 | 2.07 | 310 | 0.5670 | 0.6612 | | 0.106 | 2.14 | 320 | 0.7943 | 0.6678 | | 0.1718 | 2.2 | 330 | 0.7496 | 0.6645 | | 0.214 | 2.27 | 340 | 0.6264 | 0.6842 | | 0.1571 | 2.34 | 350 | 0.6139 | 0.6316 | | 0.1432 | 2.4 | 360 | 0.6199 | 0.6842 | | 0.1038 | 2.47 | 370 | 0.6368 | 0.6974 | | 0.1728 | 2.54 | 380 | 0.7889 | 0.6678 | | 0.14 | 2.6 | 390 | 0.7952 | 0.6546 | | 0.1522 | 2.67 | 400 | 0.7745 | 0.6579 | | 0.1345 | 2.74 | 410 | 0.7231 | 0.6513 | | 0.1587 | 2.8 | 420 | 0.7154 | 0.6480 | | 0.1391 | 2.87 | 430 | 0.6923 | 0.6513 | | 0.129 | 2.94 | 440 | 0.6484 | 0.6711 | | 0.2092 | 3.01 | 450 | 0.5822 | 0.6743 | | 0.015 | 3.07 | 460 | 1.1217 | 0.6579 | | 0.051 | 3.14 | 470 | 1.5790 | 0.6480 | | 0.0999 | 3.21 | 480 | 1.5168 | 0.6678 | | 0.1776 | 3.27 | 490 | 1.2342 | 0.6875 | | 0.0612 | 3.34 | 500 | 1.0371 | 0.6974 | | 0.0858 | 3.41 | 510 | 1.0277 | 0.6776 | | 0.0316 | 3.47 | 520 | 1.0387 | 0.6809 | | 0.1899 | 3.54 | 530 | 0.8185 | 0.6908 | | 0.1517 | 3.61 | 540 | 0.7054 | 0.6842 | | 0.0324 | 3.67 | 550 | 0.8505 | 0.6842 | | 0.0646 | 3.74 | 560 | 1.0057 | 0.6612 | | 0.1038 | 3.81 | 570 | 1.0027 | 0.6645 | | 0.0844 | 3.87 | 580 | 0.9926 | 0.6513 | | 0.0986 | 3.94 | 590 | 0.9246 | 0.6579 | | 0.0627 | 4.01 | 600 | 0.8539 | 0.6546 | | 0.0513 | 4.07 | 610 | 0.9247 | 0.6513 | | 0.0484 | 4.14 | 620 | 1.1128 | 0.6546 | | 0.0244 | 4.21 | 630 | 1.2702 | 0.6480 | | 0.0672 | 4.27 | 640 | 1.7169 | 0.6414 | | 0.0824 | 4.34 | 650 | 1.6627 | 0.6414 | | 0.0068 | 4.41 | 660 | 1.3425 | 0.6349 | | 0.044 | 4.47 | 670 | 1.2208 | 0.6612 | | 0.0378 | 4.54 | 680 | 1.2891 | 0.6447 | | 0.0411 | 4.61 | 690 | 1.3528 | 0.6612 | | 0.0215 | 4.67 | 700 | 1.2606 | 0.6678 | | 0.0438 | 4.74 | 710 | 1.2515 | 0.6546 | | 0.0936 | 4.81 | 720 | 1.0858 | 0.6645 | | 0.0305 | 4.87 | 730 | 0.9839 | 0.6579 | | 0.0282 | 4.94 | 740 | 1.0233 | 0.6809 | ### Framework versions - PEFT 0.7.1 - Transformers 4.37.2 - Pytorch 2.1.2+cu121 - Datasets 2.17.1 - Tokenizers 0.15.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "datasets": ["mbe"], "metrics": ["accuracy"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "Mistral-7B-v0.1_mbe_positive", "results": []}]}

retrieval-bar/Mistral-7B-v0.1_mbe_positive

2024-05-03T10:15:19+00:00

TAGS #peft #safetensors #generated_from_trainer #dataset-mbe #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us

Mistral-7B-v0.1\_mbe\_positive ============================== This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the mbe dataset. It achieves the following results on the evaluation set: * Loss: 1.0233 * Accuracy: 0.6809 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: 4 * eval\_batch\_size: 4 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 16 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * lr\_scheduler\_warmup\_ratio: 0.03 * num\_epochs: 5.0 ### Training results ### Framework versions * PEFT 0.7.1 * Transformers 4.37.2 * Pytorch 2.1.2+cu121 * Datasets 2.17.1 * Tokenizers 0.15.1

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. --> # RM-helpful_helpful_gpt4_loraR64_20000_gemma2b_lr1e-05_bs2_g4 This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0007 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.0015 | 1.0 | 2245 | 0.0011 | 1.0 | | 0.0012 | 2.0 | 4490 | 0.0007 | 1.0 | ### Framework versions - PEFT 0.9.0 - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "gemma", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/gemma-2b", "model-index": [{"name": "RM-helpful_helpful_gpt4_loraR64_20000_gemma2b_lr1e-05_bs2_g4", "results": []}]}

Holarissun/RM-helpful_helpful_gpt4_loraR64_20000_gemma2b_lr1e-05_bs2_g4

2024-05-03T10:17:15+00:00

TAGS #peft #safetensors #trl #reward-trainer #generated_from_trainer #base_model-google/gemma-2b #license-gemma #region-us

RM-helpful\_helpful\_gpt4\_loraR64\_20000\_gemma2b\_lr1e-05\_bs2\_g4 ==================================================================== This model is a fine-tuned version of google/gemma-2b on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.0007 * Accuracy: 1.0 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 2 * eval\_batch\_size: 8 * seed: 42 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 8 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * num\_epochs: 2.0 ### Training results ### Framework versions * PEFT 0.9.0 * Transformers 4.38.2 * Pytorch 2.1.2 * Datasets 2.18.0 * Tokenizers 0.15.2

reinforcement-learning

# **Reinforce** Agent playing **CartPole-v1** This is a trained model of a **Reinforce** agent playing **CartPole-v1** . 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": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-hello_pg", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}

jetApril/Reinforce-hello_pg

2024-05-03T10:18:21+00:00

TAGS #CartPole-v1 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us

# Reinforce Agent playing CartPole-v1 This is a trained model of a Reinforce agent playing CartPole-v1 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL

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 0.10.0

{"library_name": "peft", "base_model": "mistralai/Mistral-7B-Instruct-v0.2"}

Nour0707/mistral_7b_222

2024-05-03T10:19:57+00:00

TAGS #peft #safetensors #arxiv-1910.09700 #base_model-mistralai/Mistral-7B-Instruct-v0.2 #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 0.10.0

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. 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]

{"library_name": "transformers", "tags": []}

vc64/Mistral7b_combinedQA

2024-05-03T10:21:25+00:00

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

text-generation

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": []}

golf2248/7w27eet

2024-05-03T10:21:35+00:00

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

text2text-generation

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. --> # mt5.gendered_balanced This model is a fine-tuned version of [samzirbo/mT5.en-es.pretrained](https://huggingface.co/samzirbo/mT5.en-es.pretrained) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4866 - Bleu: 39.6812 - Meteor: 0.6692 - Chrf++: 61.3473 ## 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: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 1000 - training_steps: 30000 ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Chrf++ | |:-------------:|:------:|:-----:|:---------------:|:-------:|:------:|:-------:| | 4.1158 | 0.5398 | 3000 | 2.0732 | 30.5675 | 0.5924 | 53.893 | | 2.3023 | 1.0795 | 6000 | 1.8272 | 34.4378 | 0.6271 | 57.2898 | | 2.0519 | 1.6193 | 9000 | 1.6942 | 36.285 | 0.6425 | 58.7385 | | 1.9164 | 2.1591 | 12000 | 1.6272 | 37.2462 | 0.6501 | 59.5268 | | 1.8125 | 2.6988 | 15000 | 1.5733 | 38.0984 | 0.658 | 60.2315 | | 1.745 | 3.2386 | 18000 | 1.5362 | 38.7569 | 0.6624 | 60.7258 | | 1.6918 | 3.7783 | 21000 | 1.5089 | 39.1779 | 0.6656 | 60.9923 | | 1.6465 | 4.3181 | 24000 | 1.4947 | 39.5129 | 0.6681 | 61.2241 | | 1.6289 | 4.8579 | 27000 | 1.4876 | 39.6134 | 0.669 | 61.2987 | | 1.6187 | 5.3976 | 30000 | 1.4866 | 39.6812 | 0.6692 | 61.3473 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"tags": ["generated_from_trainer"], "metrics": ["bleu"], "base_model": "samzirbo/mT5.en-es.pretrained", "model-index": [{"name": "mt5.gendered_balanced", "results": []}]}

samzirbo/mt5.gendered_balanced

2024-05-03T10:22:13+00:00

TAGS #transformers #pytorch #mt5 #text2text-generation #generated_from_trainer #base_model-samzirbo/mT5.en-es.pretrained #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

mt5.gendered\_balanced ====================== This model is a fine-tuned version of samzirbo/URL-es.pretrained on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.4866 * Bleu: 39.6812 * Meteor: 0.6692 * Chrf++: 61.3473 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: 64 * eval\_batch\_size: 64 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: cosine * lr\_scheduler\_warmup\_steps: 1000 * training\_steps: 30000 ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.2.1+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

text-generation

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. 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]

{"library_name": "transformers", "tags": []}

Kimty/sql_coder_text_v3

2024-05-03T10:22:23+00:00

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

text-generation

transformers

Self trained GPT-2 large. Around 770M parameters. The tokenizer is the one from https://huggingface.co/openai-community/gpt2. It is being trained on around 400B tokens and this is step 57k. The evaluation is being conducted now. ## License This model is available under the Apache 2.0 License. Well, also MIT License. So both should be followed. ## Discord Server Join our Discord server [here](https://discord.gg/xhcBDEM3). ## Feeling Generous? 😊 Eager to buy me a cup of 2$ coffe or iced tea?🍵☕ Sure, here is the link: [https://ko-fi.com/drnicefellow](https://ko-fi.com/drnicefellow). Please add a note on which one you want me to drink?

{"license": "apache-2.0"}

DrNicefellow/GPT-2-Large-57k-steps

2024-05-03T10:24:23+00:00

TAGS #transformers #pytorch #gpt2 #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

Self trained GPT-2 large. Around 770M parameters. The tokenizer is the one from URL It is being trained on around 400B tokens and this is step 57k. The evaluation is being conducted now. ## License This model is available under the Apache 2.0 License. Well, also MIT License. So both should be followed. ## Discord Server Join our Discord server here. ## Feeling Generous? Eager to buy me a cup of 2$ coffe or iced tea? Sure, here is the link: URL Please add a note on which one you want me to drink?

Imatrix compressions of FP Merge of "D_AU-Mistral-7B-Instruct-v0.2-Bagel-DarkSapling-DPO-7B-v2.0". "Imatrix Plus" is an upgraded form of Imatrix which using full precision for specific parts of the compression. As a result all compressions will be slightly larger in size than standard 7B compressions. This method results in a higher quality model, especially at lower compressions. This method is applied across all compressions from IQ1 to Q8. Even IQ1_S - the most compressed verison - works well, however IQ4/Q4 are suggested as minimums for quality. Highest quality will be Q6/Q8. Q8 Imatrix Plus quality will exceed standard Q8 and Regular Imatrix Q8. This merge was an experiment to test already established Roleplay, Fiction and Story generation of "DarkSapling" with a some of "Bagel"'s qualities with a Mistral Instruct Base. For Imatrix plus this was a test of high precision in specific areas of the model leading to a slightly larger compressed file. In addition the Imatrix process itself used a larger "calibration" file than standard was used to further enhance quality. The process added appoximately 250 MB to each compressed file. An additional enhancement added another 250 mb to each compressed file. A blank or standard Alpaca Template for text generation will work. Context length: 32768. Please see the orginal model card for specific details of use, additional credits and tips under "Models Merged" below. # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [TeeZee/DarkSapling-7B-v2.0](https://huggingface.co/TeeZee/DarkSapling-7B-v2.0) * [MaziyarPanahi/bagel-dpo-7b-v0.1-Mistral-7B-Instruct-v0.2-slerp](https://huggingface.co/MaziyarPanahi/bagel-dpo-7b-v0.1-Mistral-7B-Instruct-v0.2-slerp) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: MaziyarPanahi/bagel-dpo-7b-v0.1-Mistral-7B-Instruct-v0.2-slerp layer_range: [0, 32] - model: TeeZee/DarkSapling-7B-v2.0 layer_range: [0, 32] merge_method: slerp base_model: MaziyarPanahi/bagel-dpo-7b-v0.1-Mistral-7B-Instruct-v0.2-slerp parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```

{"license": "mit"}

DavidAU/D_AU-Mistral-7B-Instruct-v0.2-Bagel-DarkSapling-DPO-7B-v2.0-imat-plus-GGUF

2024-05-03T10:26:04+00:00

TAGS #gguf #license-mit #region-us

Imatrix compressions of FP Merge of "D_AU-Mistral-7B-Instruct-v0.2-Bagel-DarkSapling-DPO-7B-v2.0". "Imatrix Plus" is an upgraded form of Imatrix which using full precision for specific parts of the compression. As a result all compressions will be slightly larger in size than standard 7B compressions. This method results in a higher quality model, especially at lower compressions. This method is applied across all compressions from IQ1 to Q8. Even IQ1_S - the most compressed verison - works well, however IQ4/Q4 are suggested as minimums for quality. Highest quality will be Q6/Q8. Q8 Imatrix Plus quality will exceed standard Q8 and Regular Imatrix Q8. This merge was an experiment to test already established Roleplay, Fiction and Story generation of "DarkSapling" with a some of "Bagel"'s qualities with a Mistral Instruct Base. For Imatrix plus this was a test of high precision in specific areas of the model leading to a slightly larger compressed file. In addition the Imatrix process itself used a larger "calibration" file than standard was used to further enhance quality. The process added appoximately 250 MB to each compressed file. An additional enhancement added another 250 mb to each compressed file. A blank or standard Alpaca Template for text generation will work. Context length: 32768. Please see the orginal model card for specific details of use, additional credits and tips under "Models Merged" below. # merge This is a merge of pre-trained language models created using mergekit. ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * TeeZee/DarkSapling-7B-v2.0 * MaziyarPanahi/bagel-dpo-7b-v0.1-Mistral-7B-Instruct-v0.2-slerp ### Configuration The following YAML configuration was used to produce this model:

text-to-image

diffusers

# 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 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

{"library_name": "diffusers"}

Niggendar/pianomix_v11VAE

2024-05-03T10:28:44+00:00

TAGS #diffusers #safetensors #arxiv-1910.09700 #endpoints_compatible #diffusers-StableDiffusionXLPipeline #region-us

# Model Card for Model ID ## Model Details ### Model Description This is the model card of a diffusers 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

transformers

This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

{"tags": ["pytorch_model_hub_mixin", "model_hub_mixin"]}

BothBosu/bilstm-scam-classifier-v1

2024-05-03T10:28:58+00:00

TAGS #transformers #safetensors #pytorch_model_hub_mixin #model_hub_mixin #endpoints_compatible #region-us

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: - Docs:

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) Mistral-7B-v0.2 - bnb 4bits - Model creator: https://huggingface.co/mistral-community/ - Original model: https://huggingface.co/mistral-community/Mistral-7B-v0.2/ Original model description: --- license: apache-2.0 --- Conversion process: 1. Download original weights from https://models.mistralcdn.com/mistral-7b-v0-2/mistral-7B-v0.2.tar 2. Convert with https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/convert_mistral_weights_to_hf.py 3. You may need to copy the tokenizer.model from Mistral-7B-Instruct-v0.2 repo.

{}

RichardErkhov/mistral-community_-_Mistral-7B-v0.2-4bits

2024-05-03T10:30:13+00:00

TAGS #transformers #safetensors #mistral #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models Mistral-7B-v0.2 - bnb 4bits - Model creator: URL - Original model: URL Original model description: --- license: apache-2.0 --- Conversion process: 1. Download original weights from URL 2. Convert with URL 3. You may need to copy the URL from Mistral-7B-Instruct-v0.2 repo.

text-generation

transformers

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) llama-3-8b-Instruct-bnb-4bit - bnb 4bits - Model creator: https://huggingface.co/unsloth/ - Original model: https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit/ Original model description: --- language: - en license: apache-2.0 library_name: transformers tags: - unsloth - transformers - llama - llama-3 --- # Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth! Directly quantized 4bit model with `bitsandbytes`. We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth) [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) ## ✨ Finetune for Free All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. | Unsloth supports | Free Notebooks | Performance | Memory use | |-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------| | **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less | | **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less | | **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less | | **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less | | **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less | | **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less | | **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less | | **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less | - This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates. - This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr. - \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

{}

RichardErkhov/unsloth_-_llama-3-8b-Instruct-bnb-4bit-4bits

2024-05-03T10:30:33+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models llama-3-8b-Instruct-bnb-4bit - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- language: * en license: apache-2.0 library\_name: transformers tags: * unsloth * transformers * llama * llama-3 --- Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth! ============================================================================ Directly quantized 4bit model with 'bitsandbytes'. We have a Google Colab Tesla T4 notebook for Llama-3 8b here: URL <img src="URL width="200"/> <img src="URL width="200"/> <img src="URL width="200"/> Finetune for Free ----------------- All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. * This conversational notebook is useful for ShareGPT ChatML / Vicuna templates. * This text completion notebook is for raw text. This DPO notebook replicates Zephyr. * \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

transformers

This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

{"tags": ["pytorch_model_hub_mixin", "model_hub_mixin"]}

BothBosu/gru-scam-classifier-v1

2024-05-03T10:31:39+00:00

TAGS #transformers #safetensors #pytorch_model_hub_mixin #model_hub_mixin #endpoints_compatible #region-us

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: - Docs:

feature-extraction

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. 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{"library_name": "transformers", "tags": []}

KoonJamesZ/sentence-transformers-nina-thai

2024-05-03T10:33:19+00:00

TAGS #transformers #safetensors #bert #feature-extraction #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

text-generation

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. 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{"library_name": "transformers", "tags": []}

syannn/llama-2-7b-spammer

2024-05-03T10:33:21+00:00

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

transformers

This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: [More Information Needed] - Docs: [More Information Needed]

{"tags": ["pytorch_model_hub_mixin", "model_hub_mixin"]}

BothBosu/bigru-scam-classifier-v1

2024-05-03T10:33:54+00:00

TAGS #transformers #safetensors #pytorch_model_hub_mixin #model_hub_mixin #endpoints_compatible #region-us

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Library: - Docs:

text-generation

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. 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{"library_name": "transformers", "tags": []}

golf2248/rxnk5b4

2024-05-03T10:35:37+00:00

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