Datasets:

ftopal

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huggingface-models

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transformers

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

{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"}

dbands/Phi-3-mini-4k-code-instructions-122k-alpaca-style-lora

2024-05-03T12:14:27+00:00

TAGS #transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/Phi-3-mini-4k-instruct-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: dbands - License: apache-2.0 - Finetuned from model : unsloth/Phi-3-mini-4k-instruct-bnb-4bit This mistral 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. 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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": []}

nguyenhuy/bert-finetuned-ner

2024-05-03T12:15:50+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

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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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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

golf2248/qauxn1u

2024-05-03T12:16:23+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

Local-Novel-LLM-project様の [Assistance](https://huggingface.co/Local-Novel-LLM-project/Assistance) をGGUF形式に変換したものです。 K量子化モデルについてもiMatrix適用してあります。 iMatrixテキストはTFMC様の[c4_en_ja_imatrix.txt](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用しています。

{"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"}

MCZK/Assistance-7B-GGUF

2024-05-03T12:16:24+00:00

TAGS #transformers #gguf #finetuned #text-generation #en #ja #license-apache-2.0 #endpoints_compatible #region-us

Local-Novel-LLM-project様の Assistance をGGUF形式に変換したものです。 K量子化モデルについてもiMatrix適用してあります。 iMatrixテキストはTFMC様のc4_en_ja_imatrix.txtを使用しています。

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{}

Kaka786/MinitoolPartitionWizard

2024-05-03T12:18:19+00:00

TAGS #region-us

Are you tired of struggling with disk partitioning issues? Say hello to your new best friend in the digital world: Minitool Partition Wizard Server Edition. This powerful software is designed to make disk management a breeze, offering a wide range of features to meet your every need. Download Minitool Partition Wizard Cracked is a comprehensive disk partition management tool that allows users to perform various operations on their hard drives effortlessly. Whether you need to resize partitions, recover lost data, or clone disks, this software has got you covered. With Minitool Partition Wizard Cracked Download, you can easily create, delete, format, move, and resize partitions without any hassle. The intuitive interface makes it simple for both novice and experienced users to perform these tasks efficiently. Download Pre-Activated: 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-x8ew-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": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "Events", "Meetups", "Networking", "Community", "Social"], "datasets": ["fine-tuned/jina-embeddings-v2-base-en-03052024-x8ew-webapp", "allenai/c4"], "pipeline_tag": "feature-extraction"}

fine-tuned/jina-embeddings-v2-base-en-03052024-x8ew-webapp

2024-05-03T12:19:02+00:00

TAGS #sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #mteb #Events #Meetups #Networking #Community #Social #custom_code #en #dataset-fine-tuned/jina-embeddings-v2-base-en-03052024-x8ew-webapp #dataset-allenai/c4 #license-apache-2.0 #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:

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": []}

SageLiao/llava-v1.5-7b-finetune-upload

2024-05-03T12:20:46+00:00

TAGS #transformers #safetensors #llava_llama #text-generation #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

question-answering

transformers

<p align="center" style="margin:0;padding:0"> <img src="https://cdn-uploads.huggingface.co/production/uploads/65e04544f59f66e0e072dc5c/b-OsZLNJtPHMwzbgwmGlV.png" alt="GovLLM Ultra banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> <div style="margin:auto; text-align:center"> <h1 style="margin-bottom: 0">GovLLM</h1> <em>A question answering model about the Dutch Government.</em> </div> ## Model description This model is a fine-tuned version of the Dutch conversational model [BramVanroy/GEITje-7B-ULTRA](https://huggingface.co/BramVanroy/GEITje-7B-ultra) on a [Dutch question-answer pair dataset](https://huggingface.co/datasets/Nelis5174473/Dutch-QA-Pairs-Rijksoverheid) of the Dutch Government. This is a Dutch question/answer model ultimately based on Mistral and fine-tuned with SFT and LoRA. # Usage with Inference Endpoints (Dedicated) ```python import requests API_URL = "https://your-own-endpoint.us-east-1.aws.endpoints.huggingface.cloud" headers = {"Authorization": "Bearer hf_your_own_token"} def query(payload): response = requests.post(API_URL, headers=headers, json=payload) return response.json() output = query({ "inputs": "Geeft de overheid subsidie aan bedrijven?" }) # print generated answer print(output[0]['generated_text']) ``` ## Training hyperparameters The following hyperparameters were used during training: - block_size: 1024, - model_max_length: 2048, - padding: right, - mixed_precision: fp16, - learning rate (lr): 0.00003, - epochs: 3, - batch_size: 2, - optimizer: adamw_torch, - schedular: linear, - quantization: int4, - peft: true, - lora_r: 16, - lora_alpha: 32, - lora_dropout: 0.05

{"language": ["nl"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "text-generation", "peft", "geitje", "conversational", "question-answering"], "datasets": ["Nelis5174473/Dutch-QA-Pairs-Rijksoverheid"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}], "pipeline_tag": "question-answering"}

Nelis5174473/GovLLM

2024-05-03T12:22:32+00:00

TAGS #transformers #safetensors #mistral #text-generation #text-generation-inference #peft #geitje #conversational #question-answering #nl #dataset-Nelis5174473/Dutch-QA-Pairs-Rijksoverheid #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

<p align="center" style="margin:0;padding:0"> <img src="URL alt="GovLLM Ultra banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> </p> <div style="margin:auto; text-align:center"> <h1 style="margin-bottom: 0">GovLLM</h1> <em>A question answering model about the Dutch Government.</em> </div> ## Model description This model is a fine-tuned version of the Dutch conversational model BramVanroy/GEITje-7B-ULTRA on a Dutch question-answer pair dataset of the Dutch Government. This is a Dutch question/answer model ultimately based on Mistral and fine-tuned with SFT and LoRA. # Usage with Inference Endpoints (Dedicated) ## Training hyperparameters The following hyperparameters were used during training: - block_size: 1024, - model_max_length: 2048, - padding: right, - mixed_precision: fp16, - learning rate (lr): 0.00003, - epochs: 3, - batch_size: 2, - optimizer: adamw_torch, - schedular: linear, - quantization: int4, - peft: true, - lora_r: 16, - lora_alpha: 32, - lora_dropout: 0.05

fill-mask

transformers

<!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # awwab-ahmed/bert-base-arabic-camelbert-mix-finetuned-AR-dotted-mediumPlus This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-mix](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-mix) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 9.5038 - Validation Loss: 8.4886 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Epoch | |:----------:|:---------------:|:-----:| | 9.5038 | 8.4886 | 0 | ### Framework versions - Transformers 4.40.1 - TensorFlow 2.15.0 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "CAMeL-Lab/bert-base-arabic-camelbert-mix", "model-index": [{"name": "awwab-ahmed/bert-base-arabic-camelbert-mix-finetuned-AR-dotted-mediumPlus", "results": []}]}

awwab-ahmed/bert-base-arabic-camelbert-mix-finetuned-AR-dotted-mediumPlus

2024-05-03T12:22:57+00:00

TAGS #transformers #tf #bert #fill-mask #generated_from_keras_callback #base_model-CAMeL-Lab/bert-base-arabic-camelbert-mix #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

awwab-ahmed/bert-base-arabic-camelbert-mix-finetuned-AR-dotted-mediumPlus ========================================================================= This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-mix on an unknown dataset. It achieves the following results on the evaluation set: * Train Loss: 9.5038 * Validation Loss: 8.4886 * Epoch: 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: * optimizer: {'name': 'AdamWeightDecay', 'learning\_rate': {'module': 'transformers.optimization\_tf', 'class\_name': 'WarmUp', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_schedule\_fn': {'module': 'keras.optimizers.schedules', 'class\_name': 'PolynomialDecay', 'config': {'initial\_learning\_rate': 2e-05, 'decay\_steps': -688, 'end\_learning\_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered\_name': None}, 'warmup\_steps': 1000, 'power': 1.0, 'name': None}, 'registered\_name': 'WarmUp'}, 'decay': 0.0, 'beta\_1': 0.9, 'beta\_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight\_decay\_rate': 0.01} * training\_precision: mixed\_float16 ### Training results ### Framework versions * Transformers 4.40.1 * TensorFlow 2.15.0 * Datasets 2.19.0 * Tokenizers 0.19.1

text-generation

transformers

# karasu-moe1 karasu-moe is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [niryuu/Karasu-1.1b-chat-vector](https://huggingface.co/niryuu/Karasu-1.1b-chat-vector) * [lightblue/karasu-1.1B](https://huggingface.co/lightblue/karasu-1.1B) ## 🧩 Configuration ```yaml base_model: lightblue/karasu-1.1B experts: - source_model: niryuu/Karasu-1.1b-chat-vector positive_prompts: - "chat" - "assistant" - "tell me" - "explain" - source_model: lightblue/karasu-1.1B positive_prompts: - "reason" - "provide" - "instruct" - "summarize" - "count" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "aipib/karasu-moe" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) 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"]) ```

{"license": "apache-2.0", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "niryuu/Karasu-1.1b-chat-vector", "lightblue/karasu-1.1B"], "base_model": ["niryuu/Karasu-1.1b-chat-vector", "lightblue/karasu-1.1B"]}

aipib/karasu-moe1

2024-05-03T12:24:28+00:00

TAGS #transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #niryuu/Karasu-1.1b-chat-vector #lightblue/karasu-1.1B #base_model-niryuu/Karasu-1.1b-chat-vector #base_model-lightblue/karasu-1.1B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# karasu-moe1 karasu-moe is a Mixture of Experts (MoE) made with the following models using LazyMergekit: * niryuu/Karasu-1.1b-chat-vector * lightblue/karasu-1.1B ## 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. --> # Whisper Base Noise Ko - Dearlie This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Noise Data dataset. It achieves the following results on the evaluation set: - Loss: 1.3670 - Cer: 57.4924 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 1.6034 | 0.8780 | 1000 | 1.6217 | 75.3884 | | 1.4053 | 1.7559 | 2000 | 1.4598 | 60.7893 | | 1.2681 | 2.6339 | 3000 | 1.3881 | 61.1636 | | 1.1608 | 3.5119 | 4000 | 1.3670 | 57.4924 | ### Framework versions - Transformers 4.41.0.dev0 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["ko"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["AIHub/noise"], "base_model": "openai/whisper-base", "model-index": [{"name": "Whisper Base Noise Ko - Dearlie", "results": []}]}

Dearlie/whisper-noise2

2024-05-03T12:25:39+00:00

TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #ko #dataset-AIHub/noise #base_model-openai/whisper-base #license-apache-2.0 #endpoints_compatible #region-us

Whisper Base Noise Ko - Dearlie =============================== This model is a fine-tuned version of openai/whisper-base on the Noise Data dataset. It achieves the following results on the evaluation set: * Loss: 1.3670 * Cer: 57.4924 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 1e-05 * train\_batch\_size: 16 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 500 * training\_steps: 4000 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.41.0.dev0 * Pytorch 2.3.0+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

# #llama-3 #roleplay GGUF-IQ-Imatrix quants for [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3). > [!IMPORTANT] > These quants have already been done after the fixes from [llama.cpp/pull/6920](https://github.com/ggerganov/llama.cpp/pull/6920). <br> > Use **KoboldCpp version 1.64** or higher. > [!NOTE] > **Prompt formatting...** <br> > Prompt format is relatively simple, author seems to recommend **ChatML**.  # Original model information by the author: Now not overtrained and with the tokenizer fix to base llama3. Trained for 3 epochs. The latest TheSpice, dipped in Mama Liz's LimaRP Oil. I've focused on making the model more flexible and provide a more unique experience. I'm still working on cleaning up my dataset, but I've shrunken it down a lot to focus on a "less is more" approach. This is ultimate a return to form of the way I used to train Thespis, with more of a focus on a small hand edited dataset. ## Datasets Used * Capybara * Claude Multiround 30k * Augmental * ToxicQA * Yahoo Answers * Airoboros 3.1 * LimaRP ## Features ( Examples from 0.1.1 because I'm too lazy to take new screenshots. Its tested tho. ) Narration If you request information on objects or characters in the scene, the model will narrate it to you. Most of the time, without moving the story forward. # You can look at anything mostly as long as you end it with "What do I see?"  # You can also request to know what a character is thinking or planning.  # You can ask for a quick summary on the character as well.  # Before continuing the conversation as normal.  ## Prompt Format: Chat ( The default Ooba template and Silly Tavern Template )  If you're using Ooba in verbose mode as a server, you can check if you're console is logging something that looks like this.  ``` {System Prompt} Username: {Input} BotName: {Response} Username: {Input} BotName: {Response} ``` ## Presets All screenshots above were taken with the below SillyTavern Preset. ## Recommended Silly Tavern Preset -> (Temp: 1.25, MinP: 0.1, RepPen: 1.05) This is a roughly equivalent Kobold Horde Preset. ## Recommended Kobold Horde Preset -> MinP # Disclaimer Please prompt responsibly and take anything outputted by any Language Model with a huge grain of salt. Thanks!

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

Lewdiculous/L3-TheSpice-8b-v0.8.3-GGUF-IQ-Imatrix

2024-05-03T12:26:37+00:00

TAGS #gguf #license-cc-by-4.0 #region-us

# #llama-3 #roleplay GGUF-IQ-Imatrix quants for cgato/L3-TheSpice-8b-v0.8.3. > [!IMPORTANT] > These quants have already been done after the fixes from URL <br> > Use KoboldCpp version 1.64 or higher. > [!NOTE] > Prompt formatting... <br> > Prompt format is relatively simple, author seems to recommend ChatML. !image/png # Original model information by the author: Now not overtrained and with the tokenizer fix to base llama3. Trained for 3 epochs. The latest TheSpice, dipped in Mama Liz's LimaRP Oil. I've focused on making the model more flexible and provide a more unique experience. I'm still working on cleaning up my dataset, but I've shrunken it down a lot to focus on a "less is more" approach. This is ultimate a return to form of the way I used to train Thespis, with more of a focus on a small hand edited dataset. ## Datasets Used * Capybara * Claude Multiround 30k * Augmental * ToxicQA * Yahoo Answers * Airoboros 3.1 * LimaRP ## Features ( Examples from 0.1.1 because I'm too lazy to take new screenshots. Its tested tho. ) Narration If you request information on objects or characters in the scene, the model will narrate it to you. Most of the time, without moving the story forward. # You can look at anything mostly as long as you end it with "What do I see?" !image/png # You can also request to know what a character is thinking or planning. !image/png # You can ask for a quick summary on the character as well. !image/png # Before continuing the conversation as normal. !image/png ## Prompt Format: Chat ( The default Ooba template and Silly Tavern Template ) !image/png If you're using Ooba in verbose mode as a server, you can check if you're console is logging something that looks like this. !image/png ## Presets All screenshots above were taken with the below SillyTavern Preset. ## Recommended Silly Tavern Preset -> (Temp: 1.25, MinP: 0.1, RepPen: 1.05) This is a roughly equivalent Kobold Horde Preset. ## Recommended Kobold Horde Preset -> MinP # Disclaimer Please prompt responsibly and take anything outputted by any Language Model with a huge grain of salt. Thanks!

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) OpenCodeInterpreter-DS-6.7B - bnb 4bits - Model creator: https://huggingface.co/m-a-p/ - Original model: https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B/ Original model description: --- language: - en pipeline_tag: text-generation tags: - code license: apache-2.0 --- <h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1> <p align="center"> <img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png"> </p> <p align="center"> <a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a> | <a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a> </p> <hr> ## Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv. ## Model Information This model is based on [deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base). ## Benchmark Scores The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks. | **Benchmark** | **HumanEval (+)** | **MBPP (+)** | **Average (+)** | |---------------|-------------------|--------------|-----------------| | **OpenCodeInterpreter-DS-1.3B** | 65.2 (61.0) | 63.4 (52.4) | 64.3 (56.7) | | + Execution Feedback | 65.2 (62.2) | 65.2 (55.6) | 65.2 (58.9) | | **OpenCodeInterpreter-DS-6.7B** | 76.2 (72.0) | 73.9 (63.7) | 75.1 (67.9) | | + Execution Feedback | 81.1 (78.7) | 82.7 (72.4) | 81.9 (75.6) | | + Synth. Human Feedback | 87.2 (86.6) | 86.2 (74.2) | 86.7 (80.4) | | + Synth. Human Feedback (Oracle) | 89.7 (86.6) | 87.2 (75.2) | 88.5 (80.9) | | **OpenCodeInterpreter-DS-33B** | 79.3 (74.3) | 78.7 (66.4) | 79.0 (70.4) | | + Execution Feedback | 82.9 (80.5) | 83.5 (72.2) | 83.2 (76.4) | | + Synth. Human Feedback | 88.4 (86.0) | 87.5 (75.9) | 88.0 (81.0) | | + Synth. Human Feedback (Oracle) | 92.7 (89.7) | 90.5 (79.5) | 91.6 (84.6) | | **OpenCodeInterpreter-CL-7B** | 72.6 (67.7) | 66.4 (55.4) | 69.5 (61.6) | | + Execution Feedback | 75.6 (70.1) | 69.9 (60.7) | 72.8 (65.4) | | **OpenCodeInterpreter-CL-13B** | 77.4 (73.8) | 70.7 (59.2) | 74.1 (66.5) | | + Execution Feedback | 81.1 (76.8) | 78.2 (67.2) | 79.7 (72.0) | | **OpenCodeInterpreter-CL-34B** | 78.0 (72.6) | 73.4 (61.4) | 75.7 (67.0) | | + Execution Feedback | 81.7 (78.7) | 80.2 (67.9) | 81.0 (73.3) | | **OpenCodeInterpreter-CL-70B** | 76.2 (70.7) | 73.0 (61.9) | 74.6 (66.3) | | + Execution Feedback | 79.9 (77.4) | 81.5 (69.9) | 80.7 (73.7) | | **OpenCodeInterpreter-GM-7B** | 56.1 (50.0) | 39.8 (34.6) | 48.0 (42.3) | | + Execution Feedback | 64.0 (54.3) | 48.6 (40.9) | 56.3 (47.6) | | **OpenCodeInterpreter-SC2-3B** | 65.2 (57.9) | 62.7 (52.9) | 64.0 (55.4) | | + Execution Feedback | 67.1 (60.4) | 63.4 (54.9) | 65.3 (57.7) | | **OpenCodeInterpreter-SC2-7B** | 73.8 (68.9) | 61.7 (51.1) | 67.8 (60.0) | | + Execution Feedback | 75.6 (69.5) | 66.9 (55.4) | 71.3 (62.5) | | **OpenCodeInterpreter-SC2-15B** | 75.6 (69.5) | 71.2 (61.2) | 73.4 (65.4) | | + Execution Feedback | 77.4 (72.0) | 74.2 (63.4) | 75.8 (67.7) | *Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks.* ## Model Usage ### Inference ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_path="m-a-p/OpenCodeInterpreter-DS-6.7B" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto", ) model.eval() prompt = "Write a function to find the shared elements from the given two lists." inputs = tokenizer.apply_chat_template( [{'role': 'user', 'content': prompt }], return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=1024, do_sample=False, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ## Contact If you have any inquiries, please feel free to raise an issue or reach out to us via email at: xiangyue.work@gmail.com, zhengtianyu0428@gmail.com. We're here to assist you!"

{}

RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-4bits

2024-05-03T12:26:50+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-2402.14658 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models OpenCodeInterpreter-DS-6.7B - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- language: * en pipeline\_tag: text-generation tags: * code license: apache-2.0 --- OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement OpenCodeInterpreter --- Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and related work, refer to our paper: "OpenCodeInterpreter: A System for Enhanced Code Generation and Execution" available on arXiv. Model Information This model is based on deepseek-coder-6.7b-base. Benchmark Scores The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks. *Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks.* Model Usage Inference Contact If you have any inquiries, please feel free to raise an issue or reach out to us via email at: URL@URL, zhengtianyu0428@URL. We're here to assist you!" ================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================

transformers

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

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

Shanwun/uncdrew

2024-05-03T12:28:06+00:00

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

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

reinforcement-learning

# **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

{"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "9.10 +/- 10.33", "name": "mean_reward", "verified": false}]}]}]}

raulgadea/Reinforce-Pixelcopter-PLE-v0

2024-05-03T12:29:37+00:00

TAGS #Pixelcopter-PLE-v0 #reinforce #reinforcement-learning #custom-implementation #deep-rl-class #model-index #region-us

# Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: URL

text-generation

transformers

# Llama3-ChatQA-1.5-8B-lora This is a LoRA extracted from a language model. It was extracted using [mergekit](https://github.com/arcee-ai/mergekit). ## LoRA Details This LoRA adapter was extracted from [nvidia/Llama3-ChatQA-1.5-8B](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) and uses [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) as a base. ### Parameters The following command was used to extract this LoRA adapter: ```sh mergekit-extract-lora meta-llama/Meta-Llama-3-8B nvidia/Llama3-ChatQA-1.5-8B OUTPUT_PATH --no-lazy-unpickle --rank=64 ```

{"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["mergekit", "peft", "nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "base_model": ["meta-llama/Meta-Llama-3-8B", "nvidia/Llama3-ChatQA-1.5-8B"], "pipeline_tag": "text-generation"}

beratcmn/Llama3-ChatQA-1.5-8B-lora

2024-05-03T12:29:56+00:00

TAGS #transformers #safetensors #mergekit #peft #nvidia #chatqa-1.5 #chatqa #llama-3 #pytorch #text-generation #en #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #endpoints_compatible #region-us

# Llama3-ChatQA-1.5-8B-lora This is a LoRA extracted from a language model. It was extracted using mergekit. ## LoRA Details This LoRA adapter was extracted from nvidia/Llama3-ChatQA-1.5-8B and uses meta-llama/Meta-Llama-3-8B as a base. ### Parameters The following command was used to extract this LoRA adapter:

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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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/cn73ttt

2024-05-03T12:30:47+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) stablelm-2-zephyr-1_6b - bnb 4bits - Model creator: https://huggingface.co/stabilityai/ - Original model: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b/ Original model description: --- datasets: - HuggingFaceH4/ultrachat_200k - allenai/ultrafeedback_binarized_cleaned - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - Intel/orca_dpo_pairs - hkust-nlp/deita-10k-v0 language: - en tags: - causal-lm extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: other --- # `StableLM 2 Zephyr 1.6B` ## Model Description `Stable LM 2 Zephyr 1.6B` is a 1.6 billion parameter instruction tuned language model inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). ## Usage `StableLM 2 Zephyr 1.6B` uses the following instruction format: ``` <|user|> Which famous math number begins with 1.6 ...?<|endoftext|> <|assistant|> The number you are referring to is 1.618033988749895. This is the famous value known as the golden ratio<|endoftext|> ``` This format is also available through the tokenizer's `apply_chat_template` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b') model = AutoModelForCausalLM.from_pretrained( 'stabilityai/stablelm-2-zephyr-1_6b', device_map="auto" ) prompt = [{'role': 'user', 'content': 'Which famous math number begins with 1.6 ...?'}] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to(model.device), max_new_tokens=1024, temperature=0.5, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ``` ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `StableLM 2 Zephyr 1.6B` model is an auto-regressive language model based on the transformer decoder architecture. * **Language(s)**: English * **Paper**: [Stable LM 2 1.6B Technical Report](https://drive.google.com/file/d/1JYJHszhS8EFChTbNAf8xmqhKjogWRrQF/view?usp=sharing) * **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git) * **Finetuned from model**: [https://huggingface.co/stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b) * **License**: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b/blob/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more. * **Contact**: For questions and comments about the model, please email `lm@stability.ai` ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): 1. SFT Datasets - HuggingFaceH4/ultrachat_200k - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Open-Orca/SlimOrca - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - hkust-nlp/deita-10k-v0 2. Preference Datasets: - allenai/ultrafeedback_binarized_cleaned - Intel/orca_dpo_pairs ## Performance ### MT-Bench <img src="https://cdn-uploads.huggingface.co/production/uploads/61b2bf4f5b1f7cad1799cfbb/QH00HVM3lg-5f17U_py4K.png" alt="mt_bench_plot" width="600"/> | Model | Size | MT-Bench | |-------------------------|------|----------| | Mistral-7B-Instruct-v0.2| 7B | 7.61 | | Llama2-Chat | 70B | 6.86 | | stablelm-zephyr-3b | 3B | 6.64 | | MPT-30B-Chat | 30B | 6.39 | | **stablelm-2-zephyr-1.6b** | 1.6B | 5.42 | | Falcon-40B-Instruct | 40B | 5.17 | | Qwen-1.8B-Chat | 1.8B | 4.95 | | dolphin-2.6-phi-2 | 2.7B | 4.93 | | phi-2 | 2.7B | 4.29 | | TinyLlama-1.1B-Chat-v1.0| 1.1B | 3.46 | ### OpenLLM Leaderboard | Model | Size | Average | ARC Challenge (acc_norm) | HellaSwag (acc_norm) | MMLU (acc_norm) | TruthfulQA (mc2) | Winogrande (acc) | Gsm8k (acc) | |----------------------------------------|------|---------|-------------------------|----------------------|-----------------|------------------|------------------|-------------| | microsoft/phi-2 | 2.7B | 61.32% | 61.09% | 75.11% | 58.11% | 44.47% | 74.35% | 54.81% | | **stabilityai/stablelm-2-zephyr-1_6b** | 1.6B | 49.89% | 43.69% | 69.34% | 41.85% | 45.21% | 64.09% | 35.18% | | microsoft/phi-1_5 | 1.3B | 47.69% | 52.90% | 63.79% | 43.89% | 40.89% | 72.22% | 12.43% | | stabilityai/stablelm-2-1_6b | 1.6B | 45.54% | 43.43% | 70.49% | 38.93% | 36.65% | 65.90% | 17.82% | | mosaicml/mpt-7b | 7B | 44.28% | 47.70% | 77.57% | 30.80% | 33.40% | 72.14% | 4.02% | | KnutJaegersberg/Qwen-1_8B-Llamaified* | 1.8B | 44.75% | 37.71% | 58.87% | 46.37% | 39.41% | 61.72% | 24.41% | | openlm-research/open_llama_3b_v2 | 3B | 40.28% | 40.27% | 71.60% | 27.12% | 34.78% | 67.01% | 0.91% | | iiuae/falcon-rw-1b | 1B | 37.07% | 35.07% | 63.56% | 25.28% | 35.96% | 62.04% | 0.53% | | TinyLlama/TinyLlama-1.1B-3T | 1.1B | 36.40% | 33.79% | 60.31% | 26.04% | 37.32% | 59.51% | 1.44% | ### Training Infrastructure * **Hardware**: `StableLM 2 Zephyr 1.6B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training. ## Use and Limitations ### Intended Use The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about [safety and limitations](#limitations-and-bias) below. ### Limitations and Bias ​ This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @misc{StableLM-2-1.6B, url={[https://huggingface.co/stabilityai/stablelm-2-1.6b](https://huggingface.co/stabilityai/stablelm-2-1.6b)}, title={Stable LM 2 1.6B}, author={Stability AI Language Team} } ```

{}

RichardErkhov/stabilityai_-_stablelm-2-zephyr-1_6b-4bits

2024-05-03T12:32:36+00:00

TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-2305.18290 #autotrain_compatible #endpoints_compatible #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models stablelm-2-zephyr-1\_6b - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- datasets: * HuggingFaceH4/ultrachat\_200k * allenai/ultrafeedback\_binarized\_cleaned * meta-math/MetaMathQA * WizardLM/WizardLM\_evol\_instruct\_V2\_196k * openchat/openchat\_sharegpt4\_dataset * LDJnr/Capybara * Intel/orca\_dpo\_pairs * hkust-nlp/deita-10k-v0 language: * en tags: * causal-lm extra\_gated\_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: other --- 'StableLM 2 Zephyr 1.6B' ======================== Model Description ----------------- 'Stable LM 2 Zephyr 1.6B' is a 1.6 billion parameter instruction tuned language model inspired by HugginFaceH4's Zephyr 7B training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO). Usage ----- 'StableLM 2 Zephyr 1.6B' uses the following instruction format: This format is also available through the tokenizer's 'apply\_chat\_template' method: Model Details ------------- * Developed by: Stability AI * Model type: 'StableLM 2 Zephyr 1.6B' model is an auto-regressive language model based on the transformer decoder architecture. * Language(s): English * Paper: Stable LM 2 1.6B Technical Report * Library: Alignment Handbook * Finetuned from model: URL * License: StabilityAI Non-Commercial Research Community License. If you want to use this model for your commercial products or purposes, please contact us here to learn more. * Contact: For questions and comments about the model, please email 'lm@URL' ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the HuggingFace Hub: 1. SFT Datasets * HuggingFaceH4/ultrachat\_200k * meta-math/MetaMathQA * WizardLM/WizardLM\_evol\_instruct\_V2\_196k * Open-Orca/SlimOrca * openchat/openchat\_sharegpt4\_dataset * LDJnr/Capybara * hkust-nlp/deita-10k-v0 2. Preference Datasets: * allenai/ultrafeedback\_binarized\_cleaned * Intel/orca\_dpo\_pairs Performance ----------- ### MT-Bench <img src="URL alt="mt\_bench\_plot" width="600"/> Model: Mistral-7B-Instruct-v0.2, Size: 7B, MT-Bench: 7.61 Model: Llama2-Chat, Size: 70B, MT-Bench: 6.86 Model: stablelm-zephyr-3b, Size: 3B, MT-Bench: 6.64 Model: MPT-30B-Chat, Size: 30B, MT-Bench: 6.39 Model: stablelm-2-zephyr-1.6b, Size: 1.6B, MT-Bench: 5.42 Model: Falcon-40B-Instruct, Size: 40B, MT-Bench: 5.17 Model: Qwen-1.8B-Chat, Size: 1.8B, MT-Bench: 4.95 Model: dolphin-2.6-phi-2, Size: 2.7B, MT-Bench: 4.93 Model: phi-2, Size: 2.7B, MT-Bench: 4.29 Model: TinyLlama-1.1B-Chat-v1.0, Size: 1.1B, MT-Bench: 3.46 ### OpenLLM Leaderboard ### Training Infrastructure * Hardware: 'StableLM 2 Zephyr 1.6B' was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * Code Base: We use our internal script for SFT steps and used HuggingFace Alignment Handbook script for DPO training. Use and Limitations ------------------- ### Intended Use The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about safety and limitations below. ### Limitations and Bias ​ This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. How to Cite -----------

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) OpenCodeInterpreter-DS-6.7B - bnb 8bits - Model creator: https://huggingface.co/m-a-p/ - Original model: https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B/ Original model description: --- language: - en pipeline_tag: text-generation tags: - code license: apache-2.0 --- <h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1> <p align="center"> <img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png"> </p> <p align="center"> <a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a> | <a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a> </p> <hr> ## Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv. ## Model Information This model is based on [deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base). ## Benchmark Scores The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks. | **Benchmark** | **HumanEval (+)** | **MBPP (+)** | **Average (+)** | |---------------|-------------------|--------------|-----------------| | **OpenCodeInterpreter-DS-1.3B** | 65.2 (61.0) | 63.4 (52.4) | 64.3 (56.7) | | + Execution Feedback | 65.2 (62.2) | 65.2 (55.6) | 65.2 (58.9) | | **OpenCodeInterpreter-DS-6.7B** | 76.2 (72.0) | 73.9 (63.7) | 75.1 (67.9) | | + Execution Feedback | 81.1 (78.7) | 82.7 (72.4) | 81.9 (75.6) | | + Synth. Human Feedback | 87.2 (86.6) | 86.2 (74.2) | 86.7 (80.4) | | + Synth. Human Feedback (Oracle) | 89.7 (86.6) | 87.2 (75.2) | 88.5 (80.9) | | **OpenCodeInterpreter-DS-33B** | 79.3 (74.3) | 78.7 (66.4) | 79.0 (70.4) | | + Execution Feedback | 82.9 (80.5) | 83.5 (72.2) | 83.2 (76.4) | | + Synth. Human Feedback | 88.4 (86.0) | 87.5 (75.9) | 88.0 (81.0) | | + Synth. Human Feedback (Oracle) | 92.7 (89.7) | 90.5 (79.5) | 91.6 (84.6) | | **OpenCodeInterpreter-CL-7B** | 72.6 (67.7) | 66.4 (55.4) | 69.5 (61.6) | | + Execution Feedback | 75.6 (70.1) | 69.9 (60.7) | 72.8 (65.4) | | **OpenCodeInterpreter-CL-13B** | 77.4 (73.8) | 70.7 (59.2) | 74.1 (66.5) | | + Execution Feedback | 81.1 (76.8) | 78.2 (67.2) | 79.7 (72.0) | | **OpenCodeInterpreter-CL-34B** | 78.0 (72.6) | 73.4 (61.4) | 75.7 (67.0) | | + Execution Feedback | 81.7 (78.7) | 80.2 (67.9) | 81.0 (73.3) | | **OpenCodeInterpreter-CL-70B** | 76.2 (70.7) | 73.0 (61.9) | 74.6 (66.3) | | + Execution Feedback | 79.9 (77.4) | 81.5 (69.9) | 80.7 (73.7) | | **OpenCodeInterpreter-GM-7B** | 56.1 (50.0) | 39.8 (34.6) | 48.0 (42.3) | | + Execution Feedback | 64.0 (54.3) | 48.6 (40.9) | 56.3 (47.6) | | **OpenCodeInterpreter-SC2-3B** | 65.2 (57.9) | 62.7 (52.9) | 64.0 (55.4) | | + Execution Feedback | 67.1 (60.4) | 63.4 (54.9) | 65.3 (57.7) | | **OpenCodeInterpreter-SC2-7B** | 73.8 (68.9) | 61.7 (51.1) | 67.8 (60.0) | | + Execution Feedback | 75.6 (69.5) | 66.9 (55.4) | 71.3 (62.5) | | **OpenCodeInterpreter-SC2-15B** | 75.6 (69.5) | 71.2 (61.2) | 73.4 (65.4) | | + Execution Feedback | 77.4 (72.0) | 74.2 (63.4) | 75.8 (67.7) | *Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks.* ## Model Usage ### Inference ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_path="m-a-p/OpenCodeInterpreter-DS-6.7B" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto", ) model.eval() prompt = "Write a function to find the shared elements from the given two lists." inputs = tokenizer.apply_chat_template( [{'role': 'user', 'content': prompt }], return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=1024, do_sample=False, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ## Contact If you have any inquiries, please feel free to raise an issue or reach out to us via email at: xiangyue.work@gmail.com, zhengtianyu0428@gmail.com. We're here to assist you!"

{}

RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-8bits

2024-05-03T12:33:34+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #arxiv-2402.14658 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models OpenCodeInterpreter-DS-6.7B - bnb 8bits * Model creator: URL * Original model: URL Original model description: --------------------------- language: * en pipeline\_tag: text-generation tags: * code license: apache-2.0 --- OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement OpenCodeInterpreter --- Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and related work, refer to our paper: "OpenCodeInterpreter: A System for Enhanced Code Generation and Execution" available on arXiv. Model Information This model is based on deepseek-coder-6.7b-base. Benchmark Scores The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks. *Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks.* Model Usage Inference Contact If you have any inquiries, please feel free to raise an issue or reach out to us via email at: URL@URL, zhengtianyu0428@URL. We're here to assist you!" ================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================

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) stablelm-2-zephyr-1_6b - bnb 8bits - Model creator: https://huggingface.co/stabilityai/ - Original model: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b/ Original model description: --- datasets: - HuggingFaceH4/ultrachat_200k - allenai/ultrafeedback_binarized_cleaned - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - Intel/orca_dpo_pairs - hkust-nlp/deita-10k-v0 language: - en tags: - causal-lm extra_gated_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: other --- # `StableLM 2 Zephyr 1.6B` ## Model Description `Stable LM 2 Zephyr 1.6B` is a 1.6 billion parameter instruction tuned language model inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290). ## Usage `StableLM 2 Zephyr 1.6B` uses the following instruction format: ``` <|user|> Which famous math number begins with 1.6 ...?<|endoftext|> <|assistant|> The number you are referring to is 1.618033988749895. This is the famous value known as the golden ratio<|endoftext|> ``` This format is also available through the tokenizer's `apply_chat_template` method: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained('stabilityai/stablelm-2-zephyr-1_6b') model = AutoModelForCausalLM.from_pretrained( 'stabilityai/stablelm-2-zephyr-1_6b', device_map="auto" ) prompt = [{'role': 'user', 'content': 'Which famous math number begins with 1.6 ...?'}] inputs = tokenizer.apply_chat_template( prompt, add_generation_prompt=True, return_tensors='pt' ) tokens = model.generate( inputs.to(model.device), max_new_tokens=1024, temperature=0.5, do_sample=True ) print(tokenizer.decode(tokens[0], skip_special_tokens=False)) ``` ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `StableLM 2 Zephyr 1.6B` model is an auto-regressive language model based on the transformer decoder architecture. * **Language(s)**: English * **Paper**: [Stable LM 2 1.6B Technical Report](https://drive.google.com/file/d/1JYJHszhS8EFChTbNAf8xmqhKjogWRrQF/view?usp=sharing) * **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git) * **Finetuned from model**: [https://huggingface.co/stabilityai/stablelm-2-1_6b](https://huggingface.co/stabilityai/stablelm-2-1_6b) * **License**: [StabilityAI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b/blob/main/LICENSE). If you want to use this model for your commercial products or purposes, please contact us [here](https://stability.ai/contact) to learn more. * **Contact**: For questions and comments about the model, please email `lm@stability.ai` ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): 1. SFT Datasets - HuggingFaceH4/ultrachat_200k - meta-math/MetaMathQA - WizardLM/WizardLM_evol_instruct_V2_196k - Open-Orca/SlimOrca - openchat/openchat_sharegpt4_dataset - LDJnr/Capybara - hkust-nlp/deita-10k-v0 2. Preference Datasets: - allenai/ultrafeedback_binarized_cleaned - Intel/orca_dpo_pairs ## Performance ### MT-Bench <img src="https://cdn-uploads.huggingface.co/production/uploads/61b2bf4f5b1f7cad1799cfbb/QH00HVM3lg-5f17U_py4K.png" alt="mt_bench_plot" width="600"/> | Model | Size | MT-Bench | |-------------------------|------|----------| | Mistral-7B-Instruct-v0.2| 7B | 7.61 | | Llama2-Chat | 70B | 6.86 | | stablelm-zephyr-3b | 3B | 6.64 | | MPT-30B-Chat | 30B | 6.39 | | **stablelm-2-zephyr-1.6b** | 1.6B | 5.42 | | Falcon-40B-Instruct | 40B | 5.17 | | Qwen-1.8B-Chat | 1.8B | 4.95 | | dolphin-2.6-phi-2 | 2.7B | 4.93 | | phi-2 | 2.7B | 4.29 | | TinyLlama-1.1B-Chat-v1.0| 1.1B | 3.46 | ### OpenLLM Leaderboard | Model | Size | Average | ARC Challenge (acc_norm) | HellaSwag (acc_norm) | MMLU (acc_norm) | TruthfulQA (mc2) | Winogrande (acc) | Gsm8k (acc) | |----------------------------------------|------|---------|-------------------------|----------------------|-----------------|------------------|------------------|-------------| | microsoft/phi-2 | 2.7B | 61.32% | 61.09% | 75.11% | 58.11% | 44.47% | 74.35% | 54.81% | | **stabilityai/stablelm-2-zephyr-1_6b** | 1.6B | 49.89% | 43.69% | 69.34% | 41.85% | 45.21% | 64.09% | 35.18% | | microsoft/phi-1_5 | 1.3B | 47.69% | 52.90% | 63.79% | 43.89% | 40.89% | 72.22% | 12.43% | | stabilityai/stablelm-2-1_6b | 1.6B | 45.54% | 43.43% | 70.49% | 38.93% | 36.65% | 65.90% | 17.82% | | mosaicml/mpt-7b | 7B | 44.28% | 47.70% | 77.57% | 30.80% | 33.40% | 72.14% | 4.02% | | KnutJaegersberg/Qwen-1_8B-Llamaified* | 1.8B | 44.75% | 37.71% | 58.87% | 46.37% | 39.41% | 61.72% | 24.41% | | openlm-research/open_llama_3b_v2 | 3B | 40.28% | 40.27% | 71.60% | 27.12% | 34.78% | 67.01% | 0.91% | | iiuae/falcon-rw-1b | 1B | 37.07% | 35.07% | 63.56% | 25.28% | 35.96% | 62.04% | 0.53% | | TinyLlama/TinyLlama-1.1B-3T | 1.1B | 36.40% | 33.79% | 60.31% | 26.04% | 37.32% | 59.51% | 1.44% | ### Training Infrastructure * **Hardware**: `StableLM 2 Zephyr 1.6B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training. ## Use and Limitations ### Intended Use The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about [safety and limitations](#limitations-and-bias) below. ### Limitations and Bias ​ This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @misc{StableLM-2-1.6B, url={[https://huggingface.co/stabilityai/stablelm-2-1.6b](https://huggingface.co/stabilityai/stablelm-2-1.6b)}, title={Stable LM 2 1.6B}, author={Stability AI Language Team} } ```

{}

RichardErkhov/stabilityai_-_stablelm-2-zephyr-1_6b-8bits

2024-05-03T12:34:34+00:00

TAGS #transformers #safetensors #stablelm #text-generation #conversational #arxiv-2305.18290 #autotrain_compatible #endpoints_compatible #8-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models stablelm-2-zephyr-1\_6b - bnb 8bits * Model creator: URL * Original model: URL Original model description: --------------------------- datasets: * HuggingFaceH4/ultrachat\_200k * allenai/ultrafeedback\_binarized\_cleaned * meta-math/MetaMathQA * WizardLM/WizardLM\_evol\_instruct\_V2\_196k * openchat/openchat\_sharegpt4\_dataset * LDJnr/Capybara * Intel/orca\_dpo\_pairs * hkust-nlp/deita-10k-v0 language: * en tags: * causal-lm extra\_gated\_fields: Name: text Email: text Country: text Organization or Affiliation: text I ALLOW Stability AI to email me about new model releases: checkbox license: other --- 'StableLM 2 Zephyr 1.6B' ======================== Model Description ----------------- 'Stable LM 2 Zephyr 1.6B' is a 1.6 billion parameter instruction tuned language model inspired by HugginFaceH4's Zephyr 7B training pipeline. The model is trained on a mix of publicly available datasets and synthetic datasets, utilizing Direct Preference Optimization (DPO). Usage ----- 'StableLM 2 Zephyr 1.6B' uses the following instruction format: This format is also available through the tokenizer's 'apply\_chat\_template' method: Model Details ------------- * Developed by: Stability AI * Model type: 'StableLM 2 Zephyr 1.6B' model is an auto-regressive language model based on the transformer decoder architecture. * Language(s): English * Paper: Stable LM 2 1.6B Technical Report * Library: Alignment Handbook * Finetuned from model: URL * License: StabilityAI Non-Commercial Research Community License. If you want to use this model for your commercial products or purposes, please contact us here to learn more. * Contact: For questions and comments about the model, please email 'lm@URL' ### Training Dataset The dataset is comprised of a mixture of open datasets large-scale datasets available on the HuggingFace Hub: 1. SFT Datasets * HuggingFaceH4/ultrachat\_200k * meta-math/MetaMathQA * WizardLM/WizardLM\_evol\_instruct\_V2\_196k * Open-Orca/SlimOrca * openchat/openchat\_sharegpt4\_dataset * LDJnr/Capybara * hkust-nlp/deita-10k-v0 2. Preference Datasets: * allenai/ultrafeedback\_binarized\_cleaned * Intel/orca\_dpo\_pairs Performance ----------- ### MT-Bench <img src="URL alt="mt\_bench\_plot" width="600"/> Model: Mistral-7B-Instruct-v0.2, Size: 7B, MT-Bench: 7.61 Model: Llama2-Chat, Size: 70B, MT-Bench: 6.86 Model: stablelm-zephyr-3b, Size: 3B, MT-Bench: 6.64 Model: MPT-30B-Chat, Size: 30B, MT-Bench: 6.39 Model: stablelm-2-zephyr-1.6b, Size: 1.6B, MT-Bench: 5.42 Model: Falcon-40B-Instruct, Size: 40B, MT-Bench: 5.17 Model: Qwen-1.8B-Chat, Size: 1.8B, MT-Bench: 4.95 Model: dolphin-2.6-phi-2, Size: 2.7B, MT-Bench: 4.93 Model: phi-2, Size: 2.7B, MT-Bench: 4.29 Model: TinyLlama-1.1B-Chat-v1.0, Size: 1.1B, MT-Bench: 3.46 ### OpenLLM Leaderboard ### Training Infrastructure * Hardware: 'StableLM 2 Zephyr 1.6B' was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes. * Code Base: We use our internal script for SFT steps and used HuggingFace Alignment Handbook script for DPO training. Use and Limitations ------------------- ### Intended Use The model is intended to be used in chat-like applications. Developers must evaluate the model for safety performance in their specific use case. Read more about safety and limitations below. ### Limitations and Bias ​ This model is not trained against adversarial inputs. We strongly recommend pairing this model with an input and output classifier to prevent harmful responses. Through our internal red teaming, we discovered that while the model will not output harmful information if not prompted to do so, it will hallucinate many facts. It is also willing to output potentially harmful outputs or misinformation when the user requests it. Using this model will require guardrails around your inputs and outputs to ensure that any outputs returned are not misinformation or harmful. Additionally, as each use case is unique, we recommend running your own suite of tests to ensure proper performance of this model. Finally, do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. How to Cite -----------

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 Tiny chinese - VingeNie This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set: - Loss: 0.7976 - Cer Ortho: 35.9855 - Cer: 29.9636 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 25 - training_steps: 1500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer Ortho | Cer | |:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:| | 0.8364 | 1.0 | 300 | 0.8423 | 44.8697 | 32.3523 | | 0.5618 | 2.0 | 600 | 0.7816 | 44.3497 | 31.3998 | | 0.3559 | 3.0 | 900 | 0.7747 | 41.4869 | 30.1052 | | 0.2016 | 4.0 | 1200 | 0.7828 | 37.2903 | 30.2107 | | 0.0953 | 5.0 | 1500 | 0.7976 | 35.9855 | 29.9636 | ### Framework versions - Transformers 4.40.1 - Pytorch 2.0.1+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1

{"language": ["zh"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "Whisper Tiny chinese - VingeNie", "results": []}]}

VingeNie/whisper-tiny-zh_CN_lr4_lowdata

2024-05-03T12:34:38+00:00

TAGS #transformers #tensorboard #safetensors #whisper #automatic-speech-recognition #generated_from_trainer #zh #dataset-mozilla-foundation/common_voice_16_1 #base_model-openai/whisper-tiny #license-apache-2.0 #endpoints_compatible #region-us

Whisper Tiny chinese - VingeNie =============================== This model is a fine-tuned version of openai/whisper-tiny on the Common Voice 16.1 dataset. It achieves the following results on the evaluation set: * Loss: 0.7976 * Cer Ortho: 35.9855 * Cer: 29.9636 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 32 * eval\_batch\_size: 32 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_steps: 25 * training\_steps: 1500 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * Transformers 4.40.1 * Pytorch 2.0.1+cu118 * 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": []}

cilantro9246/pwk36wu

2024-05-03T12:35:04+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

<br/><br/> 8bpw/h8 exl2 quantization of [xxx777xxxASD/L3-ChaoticSoliloquy-v1.5-4x8B](https://huggingface.co/xxx777xxxASD/L3-ChaoticSoliloquy-v1.5-4x8B) using default exllamav2 calibration dataset. --- **ORIGINAL CARD:**  > [!IMPORTANT] > [GGUF / Exl2 quants](https://huggingface.co/collections/xxx777xxxASD/chaoticsoliloquy-v15-4x8b-6633f96430c0652a8ad527a3) Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than the Mixtral 8x7B and it's finetunes in RP/ERP tasks. Im not sure but it should be better than the [first version](https://huggingface.co/xxx777xxxASD/ChaoticSoliloquy-4x8B) ### Llama 3 ChaoticSoliloquy-v1.5-4x8B ``` base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1 gate_mode: random dtype: bfloat16 experts_per_token: 2 experts: - source_model: ChaoticNeutrals_Poppy_Porpoise-v0.7-L3-8B - source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1 - source_model: openlynn_Llama-3-Soliloquy-8B - source_model: Sao10K_L3-Solana-8B-v1 ``` ## Models used - [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) - [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [openlynn/Llama-3-Soliloquy-8B](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B) - [Sao10K/L3-Solana-8B-v1](https://huggingface.co/Sao10K/L3-Solana-8B-v1) ## Difference - Update from [ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B) to [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B) - Change [jeiku/Chaos_RP_l3_8B](https://huggingface.co/jeiku/Chaos_RP_l3_8B) to [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) ## Vision [llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)  ## Prompt format: Llama 3

{"language": ["en"], "license": "llama3", "tags": ["moe"]}

JayhC/L3-ChaoticSoliloquy-v1.5-4x8B-8bpw-h8-exl2

2024-05-03T12:36:34+00:00

TAGS #transformers #safetensors #mixtral #text-generation #moe #conversational #en #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us

<br/><br/> 8bpw/h8 exl2 quantization of xxx777xxxASD/L3-ChaoticSoliloquy-v1.5-4x8B using default exllamav2 calibration dataset. --- ORIGINAL CARD: !image/png > [!IMPORTANT] > GGUF / Exl2 quants Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than the Mixtral 8x7B and it's finetunes in RP/ERP tasks. Im not sure but it should be better than the first version ### Llama 3 ChaoticSoliloquy-v1.5-4x8B ## Models used - ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B - NeverSleep/Llama-3-Lumimaid-8B-v0.1 - openlynn/Llama-3-Soliloquy-8B - Sao10K/L3-Solana-8B-v1 ## Difference - Update from ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B to ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B - Change jeiku/Chaos_RP_l3_8B to NeverSleep/Llama-3-Lumimaid-8B-v0.1 ## Vision llama3_mmproj !image/png ## Prompt format: Llama 3

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": []}

ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_2_0.0002

2024-05-03T12:36:36+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

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) stablelm-2-1_6b - bnb 4bits - Model creator: https://huggingface.co/stabilityai/ - Original model: https://huggingface.co/stabilityai/stablelm-2-1_6b/ Original model description: --- license: other datasets: - tiiuae/falcon-refinedweb - togethercomputer/RedPajama-Data-1T - uonlp/CulturaX - CarperAI/pilev2-dev - bigcode/starcoderdata - DataProvenanceInitiative/Commercially-Verified-Licenses language: - en - de - es - fr - it - nl - pt tags: - causal-lm --- # `Stable LM 2 1.6B` Please note: For commercial use, please refer to https://stability.ai/membership ## Model Description `Stable LM 2 1.6B` is a 1.6 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. ## Usage Get started generating text with `Stable LM 2 1.6B` by using the following code snippet: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-2-1_6b", torch_dtype="auto", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=64, temperature=0.70, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` ### Run with Flash Attention 2 ⚡️ <details> <summary> Click to expand </summary> ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-2-1_6b", torch_dtype="auto", attn_implementation="flash_attention_2", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=64, temperature=0.70, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` </details> ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `Stable LM 2 1.6B` models are auto-regressive language models based on the transformer decoder architecture. * **Language(s)**: English * **Paper**: [Stable LM 2 1.6B Technical Report](https://drive.google.com/file/d/1JYJHszhS8EFChTbNAf8xmqhKjogWRrQF/view?usp=sharing) * **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) * **License**: [Stability AI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-1_6b/blob/main/LICENSE). * **Commercial License**: to use this model commercially, please refer to https://stability.ai/membership * **Contact**: For questions and comments about the model, please email `lm@stability.ai` ### Model Architecture The model is a decoder-only transformer similar to the LLaMA ([Touvron et al., 2023](https://arxiv.org/abs/2307.09288)) architecture with the following modifications: | Parameters | Hidden Size | Layers | Heads | Sequence Length | |----------------|-------------|--------|-------|-----------------| | 1,644,417,024 | 2048 | 24 | 32 | 4096 | * **Position Embeddings**: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864)) applied to the first 25% of head embedding dimensions for improved throughput following [Black et al. (2022)](https://arxiv.org/pdf/2204.06745.pdf). * **Normalization**: LayerNorm ([Ba et al., 2016](https://arxiv.org/abs/1607.06450)) with learned bias terms as opposed to RMSNorm ([Zhang & Sennrich, 2019](https://arxiv.org/abs/1910.07467)). * **Biases**: We remove all bias terms from the feed-forward networks and multi-head self-attention layers, except for the biases of the query, key, and value projections ([Bai et al., 2023](https://arxiv.org/abs/2309.16609)). * **Tokenizer**: We use Arcade100k, a BPE tokenizer extended from OpenAI's [`tiktoken.cl100k_base`](https://github.com/openai/tiktoken). We split digits into individual tokens following findings by [Liu & Low (2023)](https://arxiv.org/abs/2305.14201). ## Training ### Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)), RedPajama-Data ([Together Computer., 2023](https://github.com/togethercomputer/RedPajama-Data)) and The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)) both without the *Books3* subset, and StarCoder ([Li et al., 2023](https://arxiv.org/abs/2305.06161)). We further supplement our training with multi-lingual data from CulturaX ([Nguyen et al., 2023](https://arxiv.org/abs/2309.09400)) and, in particular, from its OSCAR corpora, as well as restructured data in the style of [Yuan & Liu (2022)](https://arxiv.org/abs/2206.11147). * Given the large amount of web data, we recommend fine-tuning the base `Stable LM 2 1.6B` for your downstream tasks. ### Training Procedure The model is pre-trained on the aforementioned datasets in `bfloat16` precision, optimized with AdamW, and trained using the Arcade100k tokenizer with a vocabulary size of 100,352. We outline the complete hyperparameters choices in the project's [GitHub repository - config*](https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-2-1_6b.yml). The final checkpoint of pre-training, before cooldown, is provided in the `global_step420000` [branch](https://huggingface.co/stabilityai/stablelm-2-1_6b/blob/global_step420000/README.md). ### Training Infrastructure * **Hardware**: `Stable LM 2 1.6B` was trained on the Stability AI cluster across 512 NVIDIA A100 40GB GPUs (AWS P4d instances). * **Software**: We use a fork of `gpt-neox` ([EleutherAI, 2021](https://github.com/EleutherAI/gpt-neox)), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 ([Rajbhandari et al., 2019](https://arxiv.org/abs/1910.02054v3)), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 ([Dao et al., 2023](https://tridao.me/publications/flash2/flash2.pdf)) ## Use and Limitations ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. For commercial use, please refer to https://stability.ai/membership. ### Limitations and Bias ​ As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @article{bellagente2024stable, title={Stable LM 2 1.6 B Technical Report}, author={Bellagente, Marco and Tow, Jonathan and Mahan, Dakota and Phung, Duy and Zhuravinskyi, Maksym and Adithyan, Reshinth and Baicoianu, James and Brooks, Ben and Cooper, Nathan and Datta, Ashish and others}, journal={arXiv preprint arXiv:2402.17834}, year={2024} } ```

{}

RichardErkhov/stabilityai_-_stablelm-2-1_6b-4bits

2024-05-03T12:36:46+00:00

TAGS #transformers #safetensors #stablelm #text-generation #arxiv-2307.09288 #arxiv-2104.09864 #arxiv-2204.06745 #arxiv-1607.06450 #arxiv-1910.07467 #arxiv-2309.16609 #arxiv-2305.14201 #arxiv-2101.00027 #arxiv-2305.06161 #arxiv-2309.09400 #arxiv-2206.11147 #arxiv-1910.02054 #autotrain_compatible #endpoints_compatible #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models stablelm-2-1\_6b - bnb 4bits * Model creator: URL * Original model: URL Original model description: --------------------------- license: other datasets: * tiiuae/falcon-refinedweb * togethercomputer/RedPajama-Data-1T * uonlp/CulturaX * CarperAI/pilev2-dev * bigcode/starcoderdata * DataProvenanceInitiative/Commercially-Verified-Licenses language: * en * de * es * fr * it * nl * pt tags: * causal-lm --- 'Stable LM 2 1.6B' ================== Please note: For commercial use, please refer to URL Model Description ----------------- 'Stable LM 2 1.6B' is a 1.6 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. Usage ----- Get started generating text with 'Stable LM 2 1.6B' by using the following code snippet: ### Run with Flash Attention 2 ️ Click to expand Model Details ------------- * Developed by: Stability AI * Model type: 'Stable LM 2 1.6B' models are auto-regressive language models based on the transformer decoder architecture. * Language(s): English * Paper: Stable LM 2 1.6B Technical Report * Library: GPT-NeoX * License: Stability AI Non-Commercial Research Community License. * Commercial License: to use this model commercially, please refer to URL * Contact: For questions and comments about the model, please email 'lm@URL' ### Model Architecture The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications: * Position Embeddings: Rotary Position Embeddings (Su et al., 2021) applied to the first 25% of head embedding dimensions for improved throughput following Black et al. (2022). * Normalization: LayerNorm (Ba et al., 2016) with learned bias terms as opposed to RMSNorm (Zhang & Sennrich, 2019). * Biases: We remove all bias terms from the feed-forward networks and multi-head self-attention layers, except for the biases of the query, key, and value projections (Bai et al., 2023). * Tokenizer: We use Arcade100k, a BPE tokenizer extended from OpenAI's 'tiktoken.cl100k\_base'. We split digits into individual tokens following findings by Liu & Low (2023). Training -------- ### Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the HuggingFace Hub: Falcon RefinedWeb extract (Penedo et al., 2023), RedPajama-Data (Together Computer., 2023) and The Pile (Gao et al., 2020) both without the *Books3* subset, and StarCoder (Li et al., 2023). We further supplement our training with multi-lingual data from CulturaX (Nguyen et al., 2023) and, in particular, from its OSCAR corpora, as well as restructured data in the style of Yuan & Liu (2022). * Given the large amount of web data, we recommend fine-tuning the base 'Stable LM 2 1.6B' for your downstream tasks. ### Training Procedure The model is pre-trained on the aforementioned datasets in 'bfloat16' precision, optimized with AdamW, and trained using the Arcade100k tokenizer with a vocabulary size of 100,352. We outline the complete hyperparameters choices in the project's GitHub repository - config\*. The final checkpoint of pre-training, before cooldown, is provided in the 'global\_step420000' branch. ### Training Infrastructure * Hardware: 'Stable LM 2 1.6B' was trained on the Stability AI cluster across 512 NVIDIA A100 40GB GPUs (AWS P4d instances). * Software: We use a fork of 'gpt-neox' (EleutherAI, 2021), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 (Rajbhandari et al., 2019), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 (Dao et al., 2023) Use and Limitations ------------------- ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. For commercial use, please refer to URL ### Limitations and Bias ​ As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. How to Cite -----------

# Senko-san-AI Senko-san AI, a cutting-edge 33 billion parameter large language model. This page contains version changelogs for Senko-san AI. to visit the official site [click here](https://senkosan.love/ai.html) # Changelog v1 (April 16, 2023) - initial release v1.1 (September 22, 2023) - slightly faster response to the `!ai <prompt>` command - smarter when it comes to programming (Python & JavaScript) (finetuned on a private dataset) v2 (September 29, 2023) - significant reduction in VRAM usage - faster response times v3 (January 19, 2024) - it's a bit better when it comes to creative writing than Senko-san v2 while remaining as good (if not tiny bit better) at coding/math - finetuned on a synthetic dataset v3.1 (January 20, 2024) - the responses can now be much longer than before (up to the 2000 discord limit) v3.2 (February 27, 2024) - can now search the web, to enable the functionality you must type the parameter '-web' before typing your message v4 (April 15, 2024) - now you can queue multiple requests via discord - finetuned to be able to understand and respond with emojis v4.1 (May 3, 2024) - now it can understand Japanese and a few other languages - better at logic, reasoning and coding in general

{"tags": ["Senko-san AI"]}

Senko-sanAI/Senko-sanAI

2024-05-03T12:37:23+00:00

TAGS #Senko-san AI #region-us

# Senko-san-AI Senko-san AI, a cutting-edge 33 billion parameter large language model. This page contains version changelogs for Senko-san AI. to visit the official site click here # Changelog v1 (April 16, 2023) - initial release v1.1 (September 22, 2023) - slightly faster response to the '!ai <prompt>' command - smarter when it comes to programming (Python & JavaScript) (finetuned on a private dataset) v2 (September 29, 2023) - significant reduction in VRAM usage - faster response times v3 (January 19, 2024) - it's a bit better when it comes to creative writing than Senko-san v2 while remaining as good (if not tiny bit better) at coding/math - finetuned on a synthetic dataset v3.1 (January 20, 2024) - the responses can now be much longer than before (up to the 2000 discord limit) v3.2 (February 27, 2024) - can now search the web, to enable the functionality you must type the parameter '-web' before typing your message v4 (April 15, 2024) - now you can queue multiple requests via discord - finetuned to be able to understand and respond with emojis v4.1 (May 3, 2024) - now it can understand Japanese and a few other languages - better at logic, reasoning and coding in general

text-generation

mlx

# aloizidis/phi3-mini-4k-8bit-mlx This model was converted to MLX format from [`microsoft/Phi-3-mini-4k-instruct`]() using mlx-lm version **0.12.1**. Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for more details on the model. ## Use with mlx ```bash pip install mlx-lm ``` ```python from mlx_lm import load, generate model, tokenizer = load("aloizidis/phi3-mini-4k-8bit-mlx") response = generate(model, tokenizer, prompt="hello", verbose=True) ```

{"language": ["en"], "license": "mit", "tags": ["nlp", "code", "mlx"], "license_link": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE", "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}]}]}

aloizidis/phi3-mini-4k-8bit-mlx

2024-05-03T12:38:18+00:00

TAGS #mlx #safetensors #phi3 #nlp #code #text-generation #conversational #custom_code #en #license-mit #region-us

# aloizidis/phi3-mini-4k-8bit-mlx This model was converted to MLX format from ['microsoft/Phi-3-mini-4k-instruct']() using mlx-lm version 0.12.1. Refer to the original model card for more details on the model. ## Use with mlx

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) stablelm-2-1_6b - bnb 8bits - Model creator: https://huggingface.co/stabilityai/ - Original model: https://huggingface.co/stabilityai/stablelm-2-1_6b/ Original model description: --- license: other datasets: - tiiuae/falcon-refinedweb - togethercomputer/RedPajama-Data-1T - uonlp/CulturaX - CarperAI/pilev2-dev - bigcode/starcoderdata - DataProvenanceInitiative/Commercially-Verified-Licenses language: - en - de - es - fr - it - nl - pt tags: - causal-lm --- # `Stable LM 2 1.6B` Please note: For commercial use, please refer to https://stability.ai/membership ## Model Description `Stable LM 2 1.6B` is a 1.6 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. ## Usage Get started generating text with `Stable LM 2 1.6B` by using the following code snippet: ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-2-1_6b", torch_dtype="auto", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=64, temperature=0.70, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` ### Run with Flash Attention 2 ⚡️ <details> <summary> Click to expand </summary> ```python from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-2-1_6b") model = AutoModelForCausalLM.from_pretrained( "stabilityai/stablelm-2-1_6b", torch_dtype="auto", attn_implementation="flash_attention_2", ) model.cuda() inputs = tokenizer("The weather is always wonderful", return_tensors="pt").to(model.device) tokens = model.generate( **inputs, max_new_tokens=64, temperature=0.70, top_p=0.95, do_sample=True, ) print(tokenizer.decode(tokens[0], skip_special_tokens=True)) ``` </details> ## Model Details * **Developed by**: [Stability AI](https://stability.ai/) * **Model type**: `Stable LM 2 1.6B` models are auto-regressive language models based on the transformer decoder architecture. * **Language(s)**: English * **Paper**: [Stable LM 2 1.6B Technical Report](https://drive.google.com/file/d/1JYJHszhS8EFChTbNAf8xmqhKjogWRrQF/view?usp=sharing) * **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) * **License**: [Stability AI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stablelm-2-1_6b/blob/main/LICENSE). * **Commercial License**: to use this model commercially, please refer to https://stability.ai/membership * **Contact**: For questions and comments about the model, please email `lm@stability.ai` ### Model Architecture The model is a decoder-only transformer similar to the LLaMA ([Touvron et al., 2023](https://arxiv.org/abs/2307.09288)) architecture with the following modifications: | Parameters | Hidden Size | Layers | Heads | Sequence Length | |----------------|-------------|--------|-------|-----------------| | 1,644,417,024 | 2048 | 24 | 32 | 4096 | * **Position Embeddings**: Rotary Position Embeddings ([Su et al., 2021](https://arxiv.org/abs/2104.09864)) applied to the first 25% of head embedding dimensions for improved throughput following [Black et al. (2022)](https://arxiv.org/pdf/2204.06745.pdf). * **Normalization**: LayerNorm ([Ba et al., 2016](https://arxiv.org/abs/1607.06450)) with learned bias terms as opposed to RMSNorm ([Zhang & Sennrich, 2019](https://arxiv.org/abs/1910.07467)). * **Biases**: We remove all bias terms from the feed-forward networks and multi-head self-attention layers, except for the biases of the query, key, and value projections ([Bai et al., 2023](https://arxiv.org/abs/2309.16609)). * **Tokenizer**: We use Arcade100k, a BPE tokenizer extended from OpenAI's [`tiktoken.cl100k_base`](https://github.com/openai/tiktoken). We split digits into individual tokens following findings by [Liu & Low (2023)](https://arxiv.org/abs/2305.14201). ## Training ### Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the [HuggingFace Hub](https://huggingface.co/datasets): Falcon RefinedWeb extract ([Penedo et al., 2023](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)), RedPajama-Data ([Together Computer., 2023](https://github.com/togethercomputer/RedPajama-Data)) and The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)) both without the *Books3* subset, and StarCoder ([Li et al., 2023](https://arxiv.org/abs/2305.06161)). We further supplement our training with multi-lingual data from CulturaX ([Nguyen et al., 2023](https://arxiv.org/abs/2309.09400)) and, in particular, from its OSCAR corpora, as well as restructured data in the style of [Yuan & Liu (2022)](https://arxiv.org/abs/2206.11147). * Given the large amount of web data, we recommend fine-tuning the base `Stable LM 2 1.6B` for your downstream tasks. ### Training Procedure The model is pre-trained on the aforementioned datasets in `bfloat16` precision, optimized with AdamW, and trained using the Arcade100k tokenizer with a vocabulary size of 100,352. We outline the complete hyperparameters choices in the project's [GitHub repository - config*](https://github.com/Stability-AI/StableLM/blob/main/configs/stablelm-2-1_6b.yml). The final checkpoint of pre-training, before cooldown, is provided in the `global_step420000` [branch](https://huggingface.co/stabilityai/stablelm-2-1_6b/blob/global_step420000/README.md). ### Training Infrastructure * **Hardware**: `Stable LM 2 1.6B` was trained on the Stability AI cluster across 512 NVIDIA A100 40GB GPUs (AWS P4d instances). * **Software**: We use a fork of `gpt-neox` ([EleutherAI, 2021](https://github.com/EleutherAI/gpt-neox)), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 ([Rajbhandari et al., 2019](https://arxiv.org/abs/1910.02054v3)), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 ([Dao et al., 2023](https://tridao.me/publications/flash2/flash2.pdf)) ## Use and Limitations ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. For commercial use, please refer to https://stability.ai/membership. ### Limitations and Bias ​ As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. ## How to Cite ```bibtex @article{bellagente2024stable, title={Stable LM 2 1.6 B Technical Report}, author={Bellagente, Marco and Tow, Jonathan and Mahan, Dakota and Phung, Duy and Zhuravinskyi, Maksym and Adithyan, Reshinth and Baicoianu, James and Brooks, Ben and Cooper, Nathan and Datta, Ashish and others}, journal={arXiv preprint arXiv:2402.17834}, year={2024} } ```

{}

RichardErkhov/stabilityai_-_stablelm-2-1_6b-8bits

2024-05-03T12:38:28+00:00

TAGS #transformers #safetensors #stablelm #text-generation #arxiv-2307.09288 #arxiv-2104.09864 #arxiv-2204.06745 #arxiv-1607.06450 #arxiv-1910.07467 #arxiv-2309.16609 #arxiv-2305.14201 #arxiv-2101.00027 #arxiv-2305.06161 #arxiv-2309.09400 #arxiv-2206.11147 #arxiv-1910.02054 #autotrain_compatible #endpoints_compatible #8-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models stablelm-2-1\_6b - bnb 8bits * Model creator: URL * Original model: URL Original model description: --------------------------- license: other datasets: * tiiuae/falcon-refinedweb * togethercomputer/RedPajama-Data-1T * uonlp/CulturaX * CarperAI/pilev2-dev * bigcode/starcoderdata * DataProvenanceInitiative/Commercially-Verified-Licenses language: * en * de * es * fr * it * nl * pt tags: * causal-lm --- 'Stable LM 2 1.6B' ================== Please note: For commercial use, please refer to URL Model Description ----------------- 'Stable LM 2 1.6B' is a 1.6 billion parameter decoder-only language model pre-trained on 2 trillion tokens of diverse multilingual and code datasets for two epochs. Usage ----- Get started generating text with 'Stable LM 2 1.6B' by using the following code snippet: ### Run with Flash Attention 2 ️ Click to expand Model Details ------------- * Developed by: Stability AI * Model type: 'Stable LM 2 1.6B' models are auto-regressive language models based on the transformer decoder architecture. * Language(s): English * Paper: Stable LM 2 1.6B Technical Report * Library: GPT-NeoX * License: Stability AI Non-Commercial Research Community License. * Commercial License: to use this model commercially, please refer to URL * Contact: For questions and comments about the model, please email 'lm@URL' ### Model Architecture The model is a decoder-only transformer similar to the LLaMA (Touvron et al., 2023) architecture with the following modifications: * Position Embeddings: Rotary Position Embeddings (Su et al., 2021) applied to the first 25% of head embedding dimensions for improved throughput following Black et al. (2022). * Normalization: LayerNorm (Ba et al., 2016) with learned bias terms as opposed to RMSNorm (Zhang & Sennrich, 2019). * Biases: We remove all bias terms from the feed-forward networks and multi-head self-attention layers, except for the biases of the query, key, and value projections (Bai et al., 2023). * Tokenizer: We use Arcade100k, a BPE tokenizer extended from OpenAI's 'tiktoken.cl100k\_base'. We split digits into individual tokens following findings by Liu & Low (2023). Training -------- ### Training Dataset The dataset is comprised of a filtered mixture of open-source large-scale datasets available on the HuggingFace Hub: Falcon RefinedWeb extract (Penedo et al., 2023), RedPajama-Data (Together Computer., 2023) and The Pile (Gao et al., 2020) both without the *Books3* subset, and StarCoder (Li et al., 2023). We further supplement our training with multi-lingual data from CulturaX (Nguyen et al., 2023) and, in particular, from its OSCAR corpora, as well as restructured data in the style of Yuan & Liu (2022). * Given the large amount of web data, we recommend fine-tuning the base 'Stable LM 2 1.6B' for your downstream tasks. ### Training Procedure The model is pre-trained on the aforementioned datasets in 'bfloat16' precision, optimized with AdamW, and trained using the Arcade100k tokenizer with a vocabulary size of 100,352. We outline the complete hyperparameters choices in the project's GitHub repository - config\*. The final checkpoint of pre-training, before cooldown, is provided in the 'global\_step420000' branch. ### Training Infrastructure * Hardware: 'Stable LM 2 1.6B' was trained on the Stability AI cluster across 512 NVIDIA A100 40GB GPUs (AWS P4d instances). * Software: We use a fork of 'gpt-neox' (EleutherAI, 2021), train under 2D parallelism (Data and Tensor Parallel) with ZeRO-1 (Rajbhandari et al., 2019), and rely on flash-attention as well as SwiGLU and Rotary Embedding kernels from FlashAttention-2 (Dao et al., 2023) Use and Limitations ------------------- ### Intended Use The model is intended to be used as a foundational base model for application-specific fine-tuning. Developers must evaluate and fine-tune the model for safe performance in downstream applications. For commercial use, please refer to URL ### Limitations and Bias ​ As a base model, this model may exhibit unreliable, unsafe, or other undesirable behaviors that must be corrected through evaluation and fine-tuning prior to deployment. The pre-training dataset may have contained offensive or inappropriate content, even after applying data cleansing filters, which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models if they are unsuitable for your application, or for any applications that may cause deliberate or unintentional harm to others. How to Cite -----------

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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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": []}

Oshchepkov/saiga30

2024-05-03T12:39:22+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

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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

OwOpeepeepoopoo/herewegoagain13

2024-05-03T12:39:36+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-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-1b_mz-133_EnronSpam_n-its-10-seed-2 This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) 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: 64 - 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

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-133_EnronSpam_n-its-10-seed-2", "results": []}]}

AlignmentResearch/robust_llm_pythia-1b_mz-133_EnronSpam_n-its-10-seed-2

2024-05-03T12:41:08+00:00

TAGS #transformers #tensorboard #safetensors #gpt_neox #text-classification #generated_from_trainer #base_model-EleutherAI/pythia-1b #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# robust_llm_pythia-1b_mz-133_EnronSpam_n-its-10-seed-2 This model is a fine-tuned version of EleutherAI/pythia-1b 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: 64 - 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

transformers

## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/DutchMjr/Methed-Emaid-13B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q2_K.gguf) | Q2_K | 5.0 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.IQ3_XS.gguf) | IQ3_XS | 5.5 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q3_K_S.gguf) | Q3_K_S | 5.8 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.IQ3_M.gguf) | IQ3_M | 6.1 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q3_K_L.gguf) | Q3_K_L | 7.0 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.IQ4_XS.gguf) | IQ4_XS | 7.1 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q5_K_S.gguf) | Q5_K_S | 9.1 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q5_K_M.gguf) | Q5_K_M | 9.3 | | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q6_K.gguf) | Q6_K | 10.8 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Methed-Emaid-13B-GGUF/resolve/main/Methed-Emaid-13B.Q8_0.gguf) | Q8_0 | 13.9 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

{"language": ["en"], "library_name": "transformers", "base_model": "DutchMjr/Methed-Emaid-13B", "quantized_by": "mradermacher"}

mradermacher/Methed-Emaid-13B-GGUF

2024-05-03T12:41:38+00:00

TAGS #transformers #gguf #en #base_model-DutchMjr/Methed-Emaid-13B #endpoints_compatible #region-us

About ----- static quants of URL weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. Usage ----- If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. Provided Quants --------------- (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): !URL And here are Artefact2's thoughts on the matter: URL FAQ / Model Request ------------------- See URL for some answers to questions you might have and/or if you want some other model quantized. Thanks ------ I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) OpenCodeInterpreter-DS-6.7B - GGUF - Model creator: https://huggingface.co/m-a-p/ - Original model: https://huggingface.co/m-a-p/OpenCodeInterpreter-DS-6.7B/ | Name | Quant method | Size | | ---- | ---- | ---- | | [OpenCodeInterpreter-DS-6.7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q2_K.gguf) | Q2_K | 2.36GB | | [OpenCodeInterpreter-DS-6.7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.IQ3_XS.gguf) | IQ3_XS | 2.61GB | | [OpenCodeInterpreter-DS-6.7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.IQ3_S.gguf) | IQ3_S | 2.75GB | | [OpenCodeInterpreter-DS-6.7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q3_K_S.gguf) | Q3_K_S | 2.75GB | | [OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.IQ3_M.gguf) | IQ3_M | 2.9GB | | [OpenCodeInterpreter-DS-6.7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q3_K.gguf) | Q3_K | 3.07GB | | [OpenCodeInterpreter-DS-6.7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q3_K_M.gguf) | Q3_K_M | 3.07GB | | [OpenCodeInterpreter-DS-6.7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q3_K_L.gguf) | Q3_K_L | 3.35GB | | [OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.IQ4_XS.gguf) | IQ4_XS | 3.4GB | | [OpenCodeInterpreter-DS-6.7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q4_0.gguf) | Q4_0 | 3.56GB | | [OpenCodeInterpreter-DS-6.7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.IQ4_NL.gguf) | IQ4_NL | 3.59GB | | [OpenCodeInterpreter-DS-6.7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q4_K_S.gguf) | Q4_K_S | 3.59GB | | [OpenCodeInterpreter-DS-6.7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q4_K.gguf) | Q4_K | 3.8GB | | [OpenCodeInterpreter-DS-6.7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q4_K_M.gguf) | Q4_K_M | 3.8GB | | [OpenCodeInterpreter-DS-6.7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q4_1.gguf) | Q4_1 | 3.95GB | | [OpenCodeInterpreter-DS-6.7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q5_0.gguf) | Q5_0 | 4.33GB | | [OpenCodeInterpreter-DS-6.7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q5_K_S.gguf) | Q5_K_S | 4.33GB | | [OpenCodeInterpreter-DS-6.7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q5_K.gguf) | Q5_K | 4.46GB | | [OpenCodeInterpreter-DS-6.7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q5_K_M.gguf) | Q5_K_M | 4.46GB | | [OpenCodeInterpreter-DS-6.7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q5_1.gguf) | Q5_1 | 4.72GB | | [OpenCodeInterpreter-DS-6.7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf/blob/main/OpenCodeInterpreter-DS-6.7B.Q6_K.gguf) | Q6_K | 5.15GB | Original model description: --- language: - en pipeline_tag: text-generation tags: - code license: apache-2.0 --- <h1 align="center"> OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement<h1> <p align="center"> <img width="1000px" alt="OpenCodeInterpreter" src="https://opencodeinterpreter.github.io/static/images/figure1.png"> </p> <p align="center"> <a href="https://opencodeinterpreter.github.io/">[🏠Homepage]</a> | <a href="https://github.com/OpenCodeInterpreter/OpenCodeInterpreter/">[🛠️Code]</a> </p> <hr> ## Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and related work, refer to our paper: ["OpenCodeInterpreter: A System for Enhanced Code Generation and Execution"](https://arxiv.org/abs/2402.14658) available on arXiv. ## Model Information This model is based on [deepseek-coder-6.7b-base](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base). ## Benchmark Scores The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks. | **Benchmark** | **HumanEval (+)** | **MBPP (+)** | **Average (+)** | |---------------|-------------------|--------------|-----------------| | **OpenCodeInterpreter-DS-1.3B** | 65.2 (61.0) | 63.4 (52.4) | 64.3 (56.7) | | + Execution Feedback | 65.2 (62.2) | 65.2 (55.6) | 65.2 (58.9) | | **OpenCodeInterpreter-DS-6.7B** | 76.2 (72.0) | 73.9 (63.7) | 75.1 (67.9) | | + Execution Feedback | 81.1 (78.7) | 82.7 (72.4) | 81.9 (75.6) | | + Synth. Human Feedback | 87.2 (86.6) | 86.2 (74.2) | 86.7 (80.4) | | + Synth. Human Feedback (Oracle) | 89.7 (86.6) | 87.2 (75.2) | 88.5 (80.9) | | **OpenCodeInterpreter-DS-33B** | 79.3 (74.3) | 78.7 (66.4) | 79.0 (70.4) | | + Execution Feedback | 82.9 (80.5) | 83.5 (72.2) | 83.2 (76.4) | | + Synth. Human Feedback | 88.4 (86.0) | 87.5 (75.9) | 88.0 (81.0) | | + Synth. Human Feedback (Oracle) | 92.7 (89.7) | 90.5 (79.5) | 91.6 (84.6) | | **OpenCodeInterpreter-CL-7B** | 72.6 (67.7) | 66.4 (55.4) | 69.5 (61.6) | | + Execution Feedback | 75.6 (70.1) | 69.9 (60.7) | 72.8 (65.4) | | **OpenCodeInterpreter-CL-13B** | 77.4 (73.8) | 70.7 (59.2) | 74.1 (66.5) | | + Execution Feedback | 81.1 (76.8) | 78.2 (67.2) | 79.7 (72.0) | | **OpenCodeInterpreter-CL-34B** | 78.0 (72.6) | 73.4 (61.4) | 75.7 (67.0) | | + Execution Feedback | 81.7 (78.7) | 80.2 (67.9) | 81.0 (73.3) | | **OpenCodeInterpreter-CL-70B** | 76.2 (70.7) | 73.0 (61.9) | 74.6 (66.3) | | + Execution Feedback | 79.9 (77.4) | 81.5 (69.9) | 80.7 (73.7) | | **OpenCodeInterpreter-GM-7B** | 56.1 (50.0) | 39.8 (34.6) | 48.0 (42.3) | | + Execution Feedback | 64.0 (54.3) | 48.6 (40.9) | 56.3 (47.6) | | **OpenCodeInterpreter-SC2-3B** | 65.2 (57.9) | 62.7 (52.9) | 64.0 (55.4) | | + Execution Feedback | 67.1 (60.4) | 63.4 (54.9) | 65.3 (57.7) | | **OpenCodeInterpreter-SC2-7B** | 73.8 (68.9) | 61.7 (51.1) | 67.8 (60.0) | | + Execution Feedback | 75.6 (69.5) | 66.9 (55.4) | 71.3 (62.5) | | **OpenCodeInterpreter-SC2-15B** | 75.6 (69.5) | 71.2 (61.2) | 73.4 (65.4) | | + Execution Feedback | 77.4 (72.0) | 74.2 (63.4) | 75.8 (67.7) | *Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks.* ## Model Usage ### Inference ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_path="m-a-p/OpenCodeInterpreter-DS-6.7B" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.bfloat16, device_map="auto", ) model.eval() prompt = "Write a function to find the shared elements from the given two lists." inputs = tokenizer.apply_chat_template( [{'role': 'user', 'content': prompt }], return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=1024, do_sample=False, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id, ) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ## Contact If you have any inquiries, please feel free to raise an issue or reach out to us via email at: xiangyue.work@gmail.com, zhengtianyu0428@gmail.com. We're here to assist you!"

{}

RichardErkhov/m-a-p_-_OpenCodeInterpreter-DS-6.7B-gguf

2024-05-03T12:43:26+00:00

TAGS #gguf #arxiv-2402.14658 #region-us

Quantization made by Richard Erkhov. Github Discord Request more models OpenCodeInterpreter-DS-6.7B - GGUF * Model creator: URL * Original model: URL Name: OpenCodeInterpreter-DS-6.7B.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.36GB Name: OpenCodeInterpreter-DS-6.7B.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.61GB Name: OpenCodeInterpreter-DS-6.7B.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.75GB Name: OpenCodeInterpreter-DS-6.7B.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.75GB Name: OpenCodeInterpreter-DS-6.7B.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 2.9GB Name: OpenCodeInterpreter-DS-6.7B.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.07GB Name: OpenCodeInterpreter-DS-6.7B.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.07GB Name: OpenCodeInterpreter-DS-6.7B.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.35GB Name: OpenCodeInterpreter-DS-6.7B.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.4GB Name: OpenCodeInterpreter-DS-6.7B.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.56GB Name: OpenCodeInterpreter-DS-6.7B.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.59GB Name: OpenCodeInterpreter-DS-6.7B.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.59GB Name: OpenCodeInterpreter-DS-6.7B.Q4\_K.gguf, Quant method: Q4\_K, Size: 3.8GB Name: OpenCodeInterpreter-DS-6.7B.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 3.8GB Name: OpenCodeInterpreter-DS-6.7B.Q4\_1.gguf, Quant method: Q4\_1, Size: 3.95GB Name: OpenCodeInterpreter-DS-6.7B.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.33GB Name: OpenCodeInterpreter-DS-6.7B.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.33GB Name: OpenCodeInterpreter-DS-6.7B.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.46GB Name: OpenCodeInterpreter-DS-6.7B.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.46GB Name: OpenCodeInterpreter-DS-6.7B.Q5\_1.gguf, Quant method: Q5\_1, Size: 4.72GB Name: OpenCodeInterpreter-DS-6.7B.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.15GB Original model description: --------------------------- language: * en pipeline\_tag: text-generation tags: * code license: apache-2.0 --- OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement OpenCodeInterpreter --- Introduction OpenCodeInterpreter is a family of open-source code generation systems designed to bridge the gap between large language models and advanced proprietary systems like the GPT-4 Code Interpreter. It significantly advances code generation capabilities by integrating execution and iterative refinement functionalities. For further information and related work, refer to our paper: "OpenCodeInterpreter: A System for Enhanced Code Generation and Execution" available on arXiv. Model Information This model is based on deepseek-coder-6.7b-base. Benchmark Scores The OpenCodeInterpreter Models series exemplifies the evolution of coding model performance, particularly highlighting the significant enhancements brought about by the integration of execution feedback. In an effort to quantify these improvements, we present a detailed comparison across two critical benchmarks: HumanEval and MBPP. This comparison not only showcases the individual performance metrics on each benchmark but also provides an aggregated view of the overall performance enhancement. The subsequent table succinctly encapsulates the performance data, offering a clear perspective on how execution feedback contributes to elevating the models' capabilities in code interpretation and execution tasks. *Note: The "(+)" notation represents scores from extended versions of the HumanEval and MBPP benchmarks. To ensure a fair comparison, the results shown for adding execution feedback are based on outcomes after just one iteration of feedback, without unrestricted iterations. This approach highlights the immediate impact of execution feedback on performance improvements across benchmarks.* Model Usage Inference Contact If you have any inquiries, please feel free to raise an issue or reach out to us via email at: URL@URL, zhengtianyu0428@URL. We're here to assist you!" ================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================

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) deepseek-coder-7b-instruct-v1.5 - bnb 4bits - Model creator: https://huggingface.co/deepseek-ai/ - Original model: https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5/ Original model description: --- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5 Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder) - **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/) ### 2. Evaluation Results <img width="1000px" alt="DeepSeek Coder" src="https://cdn-uploads.huggingface.co/production/uploads/6538815d1bdb3c40db94fbfa/xOtCTW5xdoLCKY4FR6tri.png"> ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True).cuda() messages=[ { 'role': 'user', 'content': "write a quick sort algorithm in python."} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).

{}

RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-4bits

2024-05-03T12:44:26+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 deepseek-coder-7b-instruct-v1.5 - bnb 4bits - Model creator: URL - Original model: URL Original model description: --- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="1000px" alt="DeepSeek Coder" src="URL </p> <p align="center"><a href="URL | <a href="URL Chat with DeepSeek Coder]</a> | <a href="URL | <a href="URL(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5 Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data. - Home Page: DeepSeek - Repository: deepseek-ai/deepseek-coder - Chat With DeepSeek Coder: DeepSeek-Coder ### 2. Evaluation Results <img width="1000px" alt="DeepSeek Coder" src="URL ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the LICENSE-MODEL for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at service@URL.

text-generation

transformers

# Uploaded model - **Developed by:** duxx - **License:** apache-2.0 - **Finetuned from model :** duxx/rag-v.01 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": "duxx/rag-v.01"}

duxx/rag-v.02

2024-05-03T12:45:34+00:00

TAGS #transformers #safetensors #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-duxx/rag-v.01 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us

# Uploaded model - Developed by: duxx - License: apache-2.0 - Finetuned from model : duxx/rag-v.01 This llama model was trained 2x faster with Unsloth and Huggingface's TRL library. <img src="URL width="200"/>

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. --> # t5-sent-rebuilder-v2 This model is a fine-tuned version of [ai-forever/ruT5-base](https://huggingface.co/ai-forever/ruT5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.7782 - Bleu: 44.0278 - Meteor: 0.6656 - Gen Len: 8.234 ## 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: 6 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Bleu | Meteor | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:| | 1.4487 | 1.0 | 1470 | 0.8752 | 38.0222 | 0.6229 | 8.1517 | | 0.9863 | 2.0 | 2940 | 0.8036 | 41.364 | 0.6467 | 8.1911 | | 0.7951 | 3.0 | 4410 | 0.7789 | 43.1979 | 0.6612 | 8.2431 | | 0.6899 | 4.0 | 5880 | 0.7773 | 43.1943 | 0.6602 | 8.1772 | | 0.6226 | 5.0 | 7350 | 0.7714 | 43.695 | 0.663 | 8.2343 | | 0.5762 | 6.0 | 8820 | 0.7782 | 44.0278 | 0.6656 | 8.234 | ### 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": "ai-forever/ruT5-base", "model-index": [{"name": "t5-sent-rebuilder-v2", "results": []}]}

XvKuoMing/t5-sent-rebuilder-v2

2024-05-03T12:45:50+00:00

TAGS #transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-ai-forever/ruT5-base #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

t5-sent-rebuilder-v2 ==================== This model is a fine-tuned version of ai-forever/ruT5-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 0.7782 * Bleu: 44.0278 * Meteor: 0.6656 * Gen Len: 8.234 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: 6 * 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

image-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. --> # batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.0768 - Accuracy: 0.9718 - Recall: 0.8900 - Precision: 0.9781 - F1: 0.9320 - Roc Auc: 0.9977 ## 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 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Recall | Precision | F1 | Roc Auc | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| | 0.059 | 1.0 | 1377 | 0.0768 | 0.9718 | 0.8900 | 0.9781 | 0.9320 | 0.9977 | ### Framework versions - Transformers 4.39.2 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.15.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy", "recall", "precision", "f1"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9717747424785588, "name": "Accuracy"}, {"type": "recall", "value": 0.8900083524744206, "name": "Recall"}, {"type": "precision", "value": 0.9781411359724613, "name": "Precision"}, {"type": "f1", "value": 0.9319958454053463, "name": "F1"}]}]}]}

hchcsuim/batch-size-16_FFPP-Raw_1FPS_faces-expand-0-aligned

2024-05-03T12:46:46+00:00

TAGS #transformers #tensorboard #safetensors #swin #image-classification #generated_from_trainer #dataset-imagefolder #base_model-microsoft/swin-tiny-patch4-window7-224 #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us

batch-size-16\_FFPP-Raw\_1FPS\_faces-expand-0-aligned ===================================================== This model is a fine-tuned version of microsoft/swin-tiny-patch4-window7-224 on the imagefolder dataset. It achieves the following results on the evaluation set: * Loss: 0.0768 * Accuracy: 0.9718 * Recall: 0.8900 * Precision: 0.9781 * F1: 0.9320 * Roc Auc: 0.9977 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 * gradient\_accumulation\_steps: 4 * total\_train\_batch\_size: 64 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * num\_epochs: 1 ### Training results ### Framework versions * Transformers 4.39.2 * Pytorch 2.2.2 * 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. --> # gemma-7b-request_to_configuration This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

{"license": "gemma", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "google/gemma-7b", "model-index": [{"name": "gemma-7b-request_to_configuration", "results": []}]}

simengel/gemma-7b-request_to_configuration

2024-05-03T12:49:26+00:00

TAGS #peft #tensorboard #safetensors #trl #sft #generated_from_trainer #base_model-google/gemma-7b #license-gemma #region-us

# gemma-7b-request_to_configuration This model is a fine-tuned version of google/gemma-7b 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: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2 - training_steps: 10 - mixed_precision_training: Native AMP ### Training results ### Framework versions - PEFT 0.8.2 - Transformers 4.38.1 - Pytorch 2.2.1+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2

reinforcement-learning

ml-agents

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: elisamammi/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

{"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]}

elisamammi/poca-SoccerTwos

2024-05-03T12:52:33+00:00

TAGS #ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us

# poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A *longer tutorial* to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: elisamammi/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play

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": []}

golf2248/060g7wb

2024-05-03T12:54:08+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

<br/><br/> 8bpw/h8 exl2 quantization of [Endevor/InfinityRP-v2-8B](https://huggingface.co/Endevor/InfinityRP-v2-8B) using default exllamav2 calibration dataset. --- **ORIGINAL CARD:**  The idea is the same as [InfinityRP v1](https://huggingface.co/Endevor/InfinityRP-v1-7B), but this one is Llama 3 with 16k ctx! Have fun... ### Prompt format: Alpaca. ``"You are now in roleplay chat mode. Engage in an endless chat, always with a creative response. Follow lengths very precisely and create paragraphs accurately. Always wait your turn, next actions and responses. Your internal thoughts are wrapped with ` marks."`` **User Message Prefix = ### Input:** **Assistant Message Prefix = ### Response:** **System Message Prefix = ### Instruction:** **Turn on "Include Names"** (optional) ### Text Length: (use on your System Prompt or ### Response:) Response: (length = medium) <- [tiny, micro, short, medium, long, enormous, huge, massive, humongous] ### Example: 

{"language": ["en"], "license": "apache-2.0", "tags": ["safetensors", "llama", "not-for-all-audiences", "nsfw", "rp", "roleplay"], "pipeline_tag": "text-generation"}

JayhC/InfinityRP-v2-8B-8bpw-h8-exl2

2024-05-03T12:54:10+00:00

TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #rp #roleplay #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us

<br/><br/> 8bpw/h8 exl2 quantization of Endevor/InfinityRP-v2-8B using default exllamav2 calibration dataset. --- ORIGINAL CARD: !example The idea is the same as InfinityRP v1, but this one is Llama 3 with 16k ctx! Have fun... ### Prompt format: Alpaca. ''"You are now in roleplay chat mode. Engage in an endless chat, always with a creative response. Follow lengths very precisely and create paragraphs accurately. Always wait your turn, next actions and responses. Your internal thoughts are wrapped with ' marks."'' User Message Prefix = ### Input: Assistant Message Prefix = ### Response: System Message Prefix = ### Instruction: Turn on "Include Names" (optional) ### Text Length: (use on your System Prompt or ### Response:) Response: (length = medium) <- [tiny, micro, short, medium, long, enormous, huge, massive, humongous] ### Example: !example

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) deepseek-coder-7b-instruct-v1.5 - bnb 8bits - Model creator: https://huggingface.co/deepseek-ai/ - Original model: https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5/ Original model description: --- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5 Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder) - **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/) ### 2. Evaluation Results <img width="1000px" alt="DeepSeek Coder" src="https://cdn-uploads.huggingface.co/production/uploads/6538815d1bdb3c40db94fbfa/xOtCTW5xdoLCKY4FR6tri.png"> ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True).cuda() messages=[ { 'role': 'user', 'content': "write a quick sort algorithm in python."} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).

{}

RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-8bits

2024-05-03T12:54:51+00:00

TAGS #transformers #safetensors #llama #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #8-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models deepseek-coder-7b-instruct-v1.5 - bnb 8bits - Model creator: URL - Original model: URL Original model description: --- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="1000px" alt="DeepSeek Coder" src="URL </p> <p align="center"><a href="URL | <a href="URL Chat with DeepSeek Coder]</a> | <a href="URL | <a href="URL(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5 Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data. - Home Page: DeepSeek - Repository: deepseek-ai/deepseek-coder - Chat With DeepSeek Coder: DeepSeek-Coder ### 2. Evaluation Results <img width="1000px" alt="DeepSeek Coder" src="URL ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the LICENSE-MODEL for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at service@URL.

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": []}

golf2248/4fms3oz

2024-05-03T12:58:10+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

# karasu-moe karasu-moe is a Mixture of Experts (MoE) made with the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [niryuu/Karasu-1.1b-chat-vector](https://huggingface.co/niryuu/Karasu-1.1b-chat-vector) * [lightblue/karasu-1.1B](https://huggingface.co/lightblue/karasu-1.1B) ## 🧩 Configuration ```yaml base_model: niryuu/Karasu-1.1b-chat-vector experts: - source_model: niryuu/Karasu-1.1b-chat-vector positive_prompts: - "chat" - "assistant" - "explain" - source_model: lightblue/karasu-1.1B positive_prompts: - "reason" - "instruct" - "count" ``` ## 💻 Usage ```python !pip install -qU transformers bitsandbytes accelerate from transformers import AutoTokenizer import transformers import torch model = "aipib/karasu-moe" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, ) messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) 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"]) ```

{"license": "apache-2.0", "tags": ["moe", "frankenmoe", "merge", "mergekit", "lazymergekit", "niryuu/Karasu-1.1b-chat-vector", "lightblue/karasu-1.1B"], "base_model": ["niryuu/Karasu-1.1b-chat-vector", "lightblue/karasu-1.1B"]}

aipib/karasu-moe

2024-05-03T13:01:15+00:00

TAGS #transformers #safetensors #mixtral #text-generation #moe #frankenmoe #merge #mergekit #lazymergekit #niryuu/Karasu-1.1b-chat-vector #lightblue/karasu-1.1B #conversational #base_model-niryuu/Karasu-1.1b-chat-vector #base_model-lightblue/karasu-1.1B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# karasu-moe karasu-moe is a Mixture of Experts (MoE) made with the following models using LazyMergekit: * niryuu/Karasu-1.1b-chat-vector * lightblue/karasu-1.1B ## Configuration ## Usage

text-generation

transformers

# Uploaded model - **Developed by:** VinhLlama - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-2b 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"}

VinhLlama/Gemma2bVinhntV02

2024-05-03T13:01:29+00:00

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

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

transformers

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

cdactvm/w2v-bert-2.0-unified_speech_cls.v1

2024-05-03T13:02:28+00:00

TAGS #transformers #arxiv-1910.09700 #endpoints_compatible #region-us

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text-generation

transformers

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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": []}

OwOpeepeepoopoo/herewegoagain13c

2024-05-03T13:04:38+00:00

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

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automatic-speech-recognition

transformers

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

ezyyeah/murix-large-v2-100steps-MERGED

2024-05-03T13:05:26+00:00

TAGS #transformers #safetensors #whisper #automatic-speech-recognition #arxiv-1910.09700 #endpoints_compatible #region-us

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transformers

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

ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_4_0.0002

2024-05-03T13:06:09+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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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": []}

cilantro9246/ywf6ss2

2024-05-03T13:06:17+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

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. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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

aashish-249/Emotion_classification

2024-05-03T13:06:24+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

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: 0.6922 - Accuracy: 0.5 - F1: 0.5 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---:| | 0.7089 | 1.0 | 50 | 0.6923 | 0.5 | 0.5 | | 0.7026 | 2.0 | 100 | 0.6923 | 0.5 | 0.5 | | 0.6995 | 3.0 | 150 | 0.6923 | 0.5 | 0.5 | | 0.7009 | 4.0 | 200 | 0.6923 | 0.5 | 0.5 | | 0.6997 | 5.0 | 250 | 0.6923 | 0.5 | 0.5 | | 0.7028 | 6.0 | 300 | 0.6922 | 0.5 | 0.5 | | 0.6951 | 7.0 | 350 | 0.6922 | 0.5 | 0.5 | | 0.6949 | 8.0 | 400 | 0.6922 | 0.5 | 0.5 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - 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": []}]}

lenatr99/loha_fine_tuned_copa

2024-05-03T13:07:09+00:00

TAGS #peft #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: 0.6922 * Accuracy: 0.5 * F1: 0.5 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * PEFT 0.10.1.dev0 * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

transformers

# nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF This model was converted to GGUF format from [`nonsonpratico/phi3-3.8-128k-italian-v2`](https://huggingface.co/nonsonpratico/phi3-3.8-128k-italian-v2) 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/nonsonpratico/phi3-3.8-128k-italian-v2) 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 nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF --model phi3-3.8-128k-italian-v2.Q4_K_M.gguf -p "The meaning to life and the universe is" ``` Server: ```bash llama-server --hf-repo nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF --model phi3-3.8-128k-italian-v2.Q4_K_M.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. ``` git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi3-3.8-128k-italian-v2.Q4_K_M.gguf -n 128 ```

{"language": ["it"], "license": "apache-2.0", "library_name": "transformers", "tags": ["llama-cpp", "gguf-my-repo"], "datasets": ["cosimoiaia/Loquace-102k"]}

nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF

2024-05-03T13:11:50+00:00

TAGS #transformers #gguf #llama-cpp #gguf-my-repo #it #dataset-cosimoiaia/Loquace-102k #license-apache-2.0 #endpoints_compatible #region-us

# nonsonpratico/phi3-3.8-128k-italian-v2-Q4_K_M-GGUF This model was converted to GGUF format from 'nonsonpratico/phi3-3.8-128k-italian-v2' 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. 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": []}

ferrazzipietro/LS_Mistral-7B-v0.1_adapters_en.layer1_NoQuant_16_32_0.01_8_0.0002

2024-05-03T13:12:18+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

# lina-speech (beta) Exploring "linear attention" for text-to-speech. It predicts audio codec "à la" [MusicGen](https://arxiv.org/abs/2306.05284) : delayed residual vector quantizers so that we do not need multiple models. Featuring [RWKV](https://github.com/BlinkDL/RWKV-LM), [Mamba](https://github.com/state-spaces/mamba), [Gated Linear Attention](https://github.com/sustcsonglin/flash-linear-attention). Compared to other LM TTS model : - Can be easily pretrained and finetuned on midrange GPUs. - Tiny memory footprint. - Trained on long context (up to 2000 tokens : ~27s). ### Models | Model | #Params | Dataset | Checkpoint | Steps | Note | | :---: | :---: |:---: |:---: |:---: |:---: | | GLA | 60M, 130M | Librilight-medium | [Download](https://nubo.ircam.fr/index.php/s/wjNYLb54m7L8xf9) | 300k | GPU inference only | | Mamba| 60M | Librilight-medium |[Download](https://nubo.ircam.fr/index.php/s/wjNYLb54m7L8xf9)| 300k | GPU inference only | | RWKV v6 | 60M | LibriTTS |[Download](https://nubo.ircam.fr/index.php/s/wjNYLb54m7L8xf9) | 150k | GPU inference only | ### Installation Following the linear complexity LM you choose, follow respective instructions first: - For Mamba check the [official repo](https://github.com/state-spaces/mamba). - For GLA/RWKV inference check [flash-linear-attention](https://github.com/sustcsonglin/flash-linear-attention). - For RWKV training check [RWKV-LM](https://github.com/BlinkDL/RWKV-LM) ### Acknowledgment - The RWKV authors and the community around for carrying high-level truly opensource research. - @SmerkyG for making my life easy at testing cutting edge language model. - @lucidrains for its huge codebase. - @sustcsonglin who made [GLA and FLA](https://github.com/sustcsonglin/flash-linear-attention). - @harrisonvanderbyl fixing RWKV inference. ### Cite ```bib @software{lemerle2024linaspeech, title = {LinaSpeech: Exploring "linear attention" for text-to-speech.}, author = {Lemerle, Théodor}, url = {https://github.com/theodorblackbird/lina-speech}, month = april, year = {2024} } ``` ### IRCAM This work takes place at IRCAM, and is part of the following project : [ANR Exovoices](https://anr.fr/Projet-ANR-21-CE23-0040) <img align="left" width="200" height="200" src="logo_ircam.jpeg">

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

lina-speech/all-models

2024-05-03T13:12:32+00:00

TAGS #arxiv-2306.05284 #license-cc-by-nc-4.0 #region-us

lina-speech (beta) ================== Exploring "linear attention" for text-to-speech. It predicts audio codec "à la" MusicGen : delayed residual vector quantizers so that we do not need multiple models. Featuring RWKV, Mamba, Gated Linear Attention. Compared to other LM TTS model : * Can be easily pretrained and finetuned on midrange GPUs. * Tiny memory footprint. * Trained on long context (up to 2000 tokens : ~27s). ### Models ### Installation Following the linear complexity LM you choose, follow respective instructions first: * For Mamba check the official repo. * For GLA/RWKV inference check flash-linear-attention. * For RWKV training check RWKV-LM ### Acknowledgment * The RWKV authors and the community around for carrying high-level truly opensource research. * @SmerkyG for making my life easy at testing cutting edge language model. * @lucidrains for its huge codebase. * @sustcsonglin who made GLA and FLA. * @harrisonvanderbyl fixing RWKV inference. ### Cite ### IRCAM This work takes place at IRCAM, and is part of the following project : ANR Exovoices 

text-generation

transformers

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> mid level trained on Jack O'Neill request response ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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

nachtwindecho/mistralai-Code-Instruct-Finetune-SG1-V4

2024-05-03T13:15:10+00:00

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

# Model Card for Model ID mid level trained on Jack O'Neill request response ## 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

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) deepseek-coder-7b-instruct-v1.5 - GGUF - Model creator: https://huggingface.co/deepseek-ai/ - Original model: https://huggingface.co/deepseek-ai/deepseek-coder-7b-instruct-v1.5/ | Name | Quant method | Size | | ---- | ---- | ---- | | [deepseek-coder-7b-instruct-v1.5.Q2_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q2_K.gguf) | Q2_K | 2.53GB | | [deepseek-coder-7b-instruct-v1.5.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ3_XS.gguf) | IQ3_XS | 2.79GB | | [deepseek-coder-7b-instruct-v1.5.IQ3_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ3_S.gguf) | IQ3_S | 2.92GB | | [deepseek-coder-7b-instruct-v1.5.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K_S.gguf) | Q3_K_S | 2.92GB | | [deepseek-coder-7b-instruct-v1.5.IQ3_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ3_M.gguf) | IQ3_M | 3.06GB | | [deepseek-coder-7b-instruct-v1.5.Q3_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K.gguf) | Q3_K | 3.22GB | | [deepseek-coder-7b-instruct-v1.5.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K_M.gguf) | Q3_K_M | 3.22GB | | [deepseek-coder-7b-instruct-v1.5.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q3_K_L.gguf) | Q3_K_L | 3.49GB | | [deepseek-coder-7b-instruct-v1.5.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ4_XS.gguf) | IQ4_XS | 3.56GB | | [deepseek-coder-7b-instruct-v1.5.Q4_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_0.gguf) | Q4_0 | 3.73GB | | [deepseek-coder-7b-instruct-v1.5.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.IQ4_NL.gguf) | IQ4_NL | 3.74GB | | [deepseek-coder-7b-instruct-v1.5.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_K_S.gguf) | Q4_K_S | 3.75GB | | [deepseek-coder-7b-instruct-v1.5.Q4_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_K.gguf) | Q4_K | 3.93GB | | [deepseek-coder-7b-instruct-v1.5.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_K_M.gguf) | Q4_K_M | 3.93GB | | [deepseek-coder-7b-instruct-v1.5.Q4_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q4_1.gguf) | Q4_1 | 4.1GB | | [deepseek-coder-7b-instruct-v1.5.Q5_0.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_0.gguf) | Q5_0 | 4.48GB | | [deepseek-coder-7b-instruct-v1.5.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_K_S.gguf) | Q5_K_S | 4.48GB | | [deepseek-coder-7b-instruct-v1.5.Q5_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_K.gguf) | Q5_K | 4.59GB | | [deepseek-coder-7b-instruct-v1.5.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_K_M.gguf) | Q5_K_M | 4.59GB | | [deepseek-coder-7b-instruct-v1.5.Q5_1.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q5_1.gguf) | Q5_1 | 4.86GB | | [deepseek-coder-7b-instruct-v1.5.Q6_K.gguf](https://huggingface.co/RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf/blob/main/deepseek-coder-7b-instruct-v1.5.Q6_K.gguf) | Q6_K | 5.28GB | Original model description: --- license: other license_name: deepseek license_link: LICENSE --- <p align="center"> <img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true"> </p> <p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p> <hr> ### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5 Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data. - **Home Page:** [DeepSeek](https://deepseek.com/) - **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder) - **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/) ### 2. Evaluation Results <img width="1000px" alt="DeepSeek Coder" src="https://cdn-uploads.huggingface.co/production/uploads/6538815d1bdb3c40db94fbfa/xOtCTW5xdoLCKY4FR6tri.png"> ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ```python from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-7b-instruct-v1.5", trust_remote_code=True).cuda() messages=[ { 'role': 'user', 'content': "write a quick sort algorithm in python."} ] inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device) outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id) print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)) ``` ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).

{}

RichardErkhov/deepseek-ai_-_deepseek-coder-7b-instruct-v1.5-gguf

2024-05-03T13:15:35+00:00

TAGS #gguf #region-us

Quantization made by Richard Erkhov. Github Discord Request more models deepseek-coder-7b-instruct-v1.5 - GGUF * Model creator: URL * Original model: URL Name: deepseek-coder-7b-instruct-v1.5.Q2\_K.gguf, Quant method: Q2\_K, Size: 2.53GB Name: deepseek-coder-7b-instruct-v1.5.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 2.79GB Name: deepseek-coder-7b-instruct-v1.5.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 2.92GB Name: deepseek-coder-7b-instruct-v1.5.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 2.92GB Name: deepseek-coder-7b-instruct-v1.5.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 3.06GB Name: deepseek-coder-7b-instruct-v1.5.Q3\_K.gguf, Quant method: Q3\_K, Size: 3.22GB Name: deepseek-coder-7b-instruct-v1.5.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 3.22GB Name: deepseek-coder-7b-instruct-v1.5.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 3.49GB Name: deepseek-coder-7b-instruct-v1.5.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 3.56GB Name: deepseek-coder-7b-instruct-v1.5.Q4\_0.gguf, Quant method: Q4\_0, Size: 3.73GB Name: deepseek-coder-7b-instruct-v1.5.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 3.74GB Name: deepseek-coder-7b-instruct-v1.5.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 3.75GB Name: deepseek-coder-7b-instruct-v1.5.Q4\_K.gguf, Quant method: Q4\_K, Size: 3.93GB Name: deepseek-coder-7b-instruct-v1.5.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 3.93GB Name: deepseek-coder-7b-instruct-v1.5.Q4\_1.gguf, Quant method: Q4\_1, Size: 4.1GB Name: deepseek-coder-7b-instruct-v1.5.Q5\_0.gguf, Quant method: Q5\_0, Size: 4.48GB Name: deepseek-coder-7b-instruct-v1.5.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 4.48GB Name: deepseek-coder-7b-instruct-v1.5.Q5\_K.gguf, Quant method: Q5\_K, Size: 4.59GB Name: deepseek-coder-7b-instruct-v1.5.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 4.59GB Name: deepseek-coder-7b-instruct-v1.5.Q5\_1.gguf, Quant method: Q5\_1, Size: 4.86GB Name: deepseek-coder-7b-instruct-v1.5.Q6\_K.gguf, Quant method: Q6\_K, Size: 5.28GB Original model description: --------------------------- license: other license\_name: deepseek license\_link: LICENSE -------------------------------------------------------------  [|](URL | <a href=) --- ### 1. Introduction of Deepseek-Coder-7B-Instruct v1.5 Deepseek-Coder-7B-Instruct-v1.5 is continue pre-trained from Deepseek-LLM 7B on 2T tokens by employing a window size of 4K and next token prediction objective, and then fine-tuned on 2B tokens of instruction data. * Home Page: DeepSeek * Repository: deepseek-ai/deepseek-coder * Chat With DeepSeek Coder: DeepSeek-Coder ### 2. Evaluation Results <img width="1000px" alt="DeepSeek Coder" src="URL ### 3. How to Use Here give some examples of how to use our model. #### Chat Model Inference ### 4. License This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use. See the LICENSE-MODEL for more details. ### 5. Contact If you have any questions, please raise an issue or contact us at service@URL.

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) starcoder2-15b - bnb 4bits - Model creator: https://huggingface.co/bigcode/ - Original model: https://huggingface.co/bigcode/starcoder2-15b/ Original model description: --- pipeline_tag: text-generation inference: parameters: temperature: 0.2 top_p: 0.95 widget: - text: 'def print_hello_world():' example_title: Hello world group: Python datasets: - bigcode/the-stack-v2-train license: bigcode-openrail-m library_name: transformers tags: - code model-index: - name: starcoder2-15b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 48.1 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 33.8 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 65.1 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 37.8 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 46.3 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 74.08 --- # StarCoder2 <center> <img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/starcoder2_banner.png" alt="SC2" width="900" height="600"> </center> ## Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [License](#license) 6. [Citation](#citation) ## Model Summary StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train), with opt-out requests excluded. The model uses [Grouped Query Attention](https://arxiv.org/abs/2305.13245), [a context window of 16,384 tokens](https://arxiv.org/abs/2205.14135) with [a sliding window attention of 4,096 tokens](https://arxiv.org/abs/2004.05150v2), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 4+ trillion tokens. The model was trained with [NVIDIA NeMo™ Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) using the [NVIDIA Eos Supercomputer](https://blogs.nvidia.com/blog/eos/) built with [NVIDIA DGX H100](https://www.nvidia.com/en-us/data-center/dgx-h100/) systems. - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) - **Paper:** [Link](https://huggingface.co/papers/2402.19173) - **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org) - **Languages:** 600+ Programming languages ## Use ### Intended use The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. ### Generation Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's [GitHub repository](https://github.com/bigcode-project/starcoder2). First, make sure to install `transformers` from source: ```bash pip install git+https://github.com/huggingface/transformers.git ``` #### Running the model on CPU/GPU/multi GPU * _Using full precision_ ```python # pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoder2-15b" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate import torch from transformers import AutoTokenizer, AutoModelForCausalLM checkpoint = "bigcode/starcoder2-15b" tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for fp16 use `torch_dtype=torch.float16` instead model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 32251.33 MB ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig # to use 4bit use `load_in_4bit=True` instead quantization_config = BitsAndBytesConfig(load_in_8bit=True) checkpoint = "bigcode/starcoder2-15b" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") # load_in_8bit Memory footprint: 16900.18 MB # load_in_4bit >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 9224.60 MB ``` ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search-v2) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations. # Training ## Model - **Architecture:** Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective - **Pretraining steps:** 1 million - **Pretraining tokens:** 4+ trillion - **Precision:** bfloat16 ## Hardware - **GPUs:** 1024 x H100 ## Software - **Framework:** [NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). # Citation ```bash @misc{lozhkov2024starcoder, title={StarCoder 2 and The Stack v2: The Next Generation}, author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2024}, eprint={2402.19173}, archivePrefix={arXiv}, primaryClass={cs.SE} } ```

{}

RichardErkhov/bigcode_-_starcoder2-15b-4bits

2024-05-03T13:15:59+00:00

TAGS #transformers #safetensors #starcoder2 #text-generation #arxiv-2305.13245 #arxiv-2205.14135 #arxiv-2004.05150 #arxiv-2207.14255 #arxiv-2402.19173 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us

Quantization made by Richard Erkhov. Github Discord Request more models starcoder2-15b - bnb 4bits - Model creator: URL - Original model: URL Original model description: --- pipeline_tag: text-generation inference: parameters: temperature: 0.2 top_p: 0.95 widget: - text: 'def print_hello_world():' example_title: Hello world group: Python datasets: - bigcode/the-stack-v2-train license: bigcode-openrail-m library_name: transformers tags: - code model-index: - name: starcoder2-15b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 48.1 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 33.8 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 65.1 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 37.8 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 46.3 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 74.08 --- # StarCoder2 <center> <img src="URL alt="SC2" width="900" height="600"> </center> ## Table of Contents 1. Model Summary 2. Use 3. Limitations 4. Training 5. License 6. Citation ## Model Summary StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 4+ trillion tokens. The model was trained with NVIDIA NeMo™ Framework using the NVIDIA Eos Supercomputer built with NVIDIA DGX H100 systems. - Project Website: URL - Paper: Link - Point of Contact: contact@URL - Languages: 600+ Programming languages ## Use ### Intended use The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. ### Generation Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's GitHub repository. First, make sure to install 'transformers' from source: #### Running the model on CPU/GPU/multi GPU * _Using full precision_ * _Using 'torch.bfloat16'_ #### Quantized Versions through 'bitsandbytes' * _Using 8-bit precision (int8)_ ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a search index that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See the paper for an in-depth discussion of the model limitations. # Training ## Model - Architecture: Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective - Pretraining steps: 1 million - Pretraining tokens: 4+ trillion - Precision: bfloat16 ## Hardware - GPUs: 1024 x H100 ## Software - Framework: NeMo Framework - Neural networks: PyTorch # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.

transformers

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

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

Shanwun/uncdrew-gguf

2024-05-03T13:16:13+00:00

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

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

transformers

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More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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

rohangbs/death_se42-type2_May2024

2024-05-03T13:16:21+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

video-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. --> # videomae-base-finetuned-numbers-augmented2 This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.9722 - Accuracy: 0.3269 - F1: 0.2716 - Precision: 0.3970 - Recall: 0.3277 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 2816 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | 2.2279 | 0.2504 | 705 | 2.2645 | 0.1824 | 0.1262 | 0.2559 | 0.1792 | | 1.7024 | 1.25 | 1409 | 2.0462 | 0.3167 | 0.2828 | 0.3354 | 0.3152 | | 1.3164 | 2.25 | 2113 | 1.9759 | 0.3081 | 0.2568 | 0.3022 | 0.3085 | | 1.3877 | 3.2496 | 2816 | 1.9641 | 0.3373 | 0.2839 | 0.3031 | 0.3367 | ### Framework versions - Transformers 4.40.0 - Pytorch 2.1.0+cu121 - Datasets 2.18.0 - Tokenizers 0.19.1

{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-numbers-augmented2", "results": []}]}

latif98/videomae-base-finetuned-numbers-augmented2

2024-05-03T13:17:09+00:00

TAGS #transformers #tensorboard #safetensors #videomae #video-classification #generated_from_trainer #base_model-MCG-NJU/videomae-base #license-cc-by-nc-4.0 #endpoints_compatible #region-us

videomae-base-finetuned-numbers-augmented2 ========================================== This model is a fine-tuned version of MCG-NJU/videomae-base on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 1.9722 * Accuracy: 0.3269 * F1: 0.2716 * Precision: 0.3970 * Recall: 0.3277 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: 4 * eval\_batch\_size: 4 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * lr\_scheduler\_warmup\_ratio: 0.1 * training\_steps: 2816 ### Training results ### Framework versions * Transformers 4.40.0 * Pytorch 2.1.0+cu121 * Datasets 2.18.0 * 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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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/sjrh9dx

2024-05-03T13:17:09+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-to-speech

pretrained models used in https://github.com/RVC-Boss/GPT-SoVITS

{"license": "mit", "pipeline_tag": "text-to-speech"}

blaise-tk/GPT-SoVITS-Fork

2024-05-03T13:20:38+00:00

TAGS #text-to-speech #license-mit #region-us

pretrained models used in URL

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": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.01_2_0.0002

2024-05-03T13:20:47+00:00

TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us

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text-generation

transformers

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

golf2248/uv50wfi

2024-05-03T13:22:22+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

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

megajajo/phi-1_5-finetuned-kotlin-completion

2024-05-03T13:23:58+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

<!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> weighted/imatrix quants of https://huggingface.co/WEMAKE-CX/Llama-3-8B-Instruct-V41-1048k

{}

mradermacher/Llama-3-8B-Instruct-V41-1048k-i1-GGUF

2024-05-03T13:24:05+00:00

TAGS #gguf #region-us

weighted/imatrix quants of URL

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_cb 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: 1.3194 - Accuracy: 0.3182 - F1: 0.1536 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - training_steps: 400 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-------:|:----:|:---------------:|:--------:|:------:| | 1.0888 | 3.5714 | 50 | 1.0482 | 0.2727 | 0.1364 | | 0.8883 | 7.1429 | 100 | 1.1156 | 0.3182 | 0.1536 | | 0.808 | 10.7143 | 150 | 1.1921 | 0.3182 | 0.1536 | | 0.732 | 14.2857 | 200 | 1.2529 | 0.3182 | 0.1536 | | 0.7553 | 17.8571 | 250 | 1.2877 | 0.3182 | 0.1536 | | 0.7436 | 21.4286 | 300 | 1.3078 | 0.3182 | 0.1536 | | 0.7115 | 25.0 | 350 | 1.3167 | 0.3182 | 0.1536 | | 0.7395 | 28.5714 | 400 | 1.3194 | 0.3182 | 0.1536 | ### Framework versions - PEFT 0.10.1.dev0 - Transformers 4.40.1 - Pytorch 2.3.0 - 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_cb", "results": []}]}

lenatr99/loha_fine_tuned_cb

2024-05-03T13:24:57+00:00

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

loha\_fine\_tuned\_cb ===================== 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: 1.3194 * Accuracy: 0.3182 * F1: 0.1536 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 2e-05 * train\_batch\_size: 8 * eval\_batch\_size: 8 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: linear * training\_steps: 400 ### Training results ### Framework versions * PEFT 0.10.1.dev0 * Transformers 4.40.1 * Pytorch 2.3.0 * Datasets 2.19.0 * Tokenizers 0.19.1

transformers

## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: --> <!-- ### vocab_type: --> static quants of https://huggingface.co/adamo1139/Yi-34B-200K-XLCTX <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q2_K.gguf) | Q2_K | 12.9 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ3_XS.gguf) | IQ3_XS | 14.3 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q3_K_S.gguf) | Q3_K_S | 15.1 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ3_S.gguf) | IQ3_S | 15.1 | beats Q3_K* | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ3_M.gguf) | IQ3_M | 15.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q3_K_M.gguf) | Q3_K_M | 16.8 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q3_K_L.gguf) | Q3_K_L | 18.2 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.IQ4_XS.gguf) | IQ4_XS | 18.7 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q4_K_S.gguf) | Q4_K_S | 19.7 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q4_K_M.gguf) | Q4_K_M | 20.8 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q5_K_S.gguf) | Q5_K_S | 23.8 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q5_K_M.gguf) | Q5_K_M | 24.4 | | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q6_K.gguf) | Q6_K | 28.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Yi-34B-200K-XLCTX-GGUF/resolve/main/Yi-34B-200K-XLCTX.Q8_0.gguf) | Q8_0 | 36.6 | fast, best quality | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):  And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->

{"language": ["en"], "license": "other", "library_name": "transformers", "base_model": "adamo1139/Yi-34B-200K-XLCTX", "license_link": "LICENSE", "license_name": "yi-license", "quantized_by": "mradermacher"}

mradermacher/Yi-34B-200K-XLCTX-GGUF

2024-05-03T13:26:50+00:00

TAGS #transformers #gguf #en #base_model-adamo1139/Yi-34B-200K-XLCTX #license-other #endpoints_compatible #region-us

About ----- static quants of URL weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. Usage ----- If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. Provided Quants --------------- (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): !URL And here are Artefact2's thoughts on the matter: URL FAQ / Model Request ------------------- See URL for some answers to questions you might have and/or if you want some other model quantized. Thanks ------ I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

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": []}

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.01_4_0.0002

2024-05-03T13:28:06+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

# Mistral-7b-Instruct-v0.1-fp16-ov * Model creator: [Mistral AI](https://huggingface.co/mistralai) * Original model: [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) ## Description This is [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16. ## Compatibility The provided OpenVINO™ IR model is compatible with: * OpenVINO version 2024.1.0 and higher * Optimum Intel 1.16.0 and higher ## Running Model Inference 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: ``` pip install optimum[openvino] ``` 2. Run model inference: ``` from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM model_id = "OpenVINO/mistral-7b-instrcut-v0.1-fp16-ov" tokenizer = AutoTokenizer.from_pretrained(model_id) model = OVModelForCausalLM.from_pretrained(model_id) messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html). ## Limitations Check the original model card for [limitations](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#limitations). ## Legal information The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).

{"language": ["en"], "license": "apache-2.0"}

OpenVINO/mistral-7b-instruct-v0.1-fp16-ov

2024-05-03T13:29:02+00:00

TAGS #transformers #openvino #mistral #text-generation #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Mistral-7b-Instruct-v0.1-fp16-ov * Model creator: Mistral AI * Original model: Mistral-7b-Instruct-v0.1 ## Description This is Mistral-7b-Instruct-v0.1 model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to FP16. ## Compatibility The provided OpenVINO™ IR model is compatible with: * OpenVINO version 2024.1.0 and higher * Optimum Intel 1.16.0 and higher ## Running Model Inference 1. Install packages required for using Optimum Intel integration with the OpenVINO backend: 2. Run model inference: For more examples and possible optimizations, refer to the OpenVINO Large Language Model Inference Guide. ## Limitations Check the original model card for limitations. ## Legal information The original model is distributed under Apache 2.0 license. More details can be found in original model card.

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/herewegoagain14

2024-05-03T13:29:10+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

Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) starcoder2-15b - GGUF - Model creator: https://huggingface.co/bigcode/ - Original model: https://huggingface.co/bigcode/starcoder2-15b/ | Name | Quant method | Size | | ---- | ---- | ---- | | [starcoder2-15b.Q2_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q2_K.gguf) | Q2_K | 5.77GB | | [starcoder2-15b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ3_XS.gguf) | IQ3_XS | 6.25GB | | [starcoder2-15b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ3_S.gguf) | IQ3_S | 6.52GB | | [starcoder2-15b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K_S.gguf) | Q3_K_S | 6.51GB | | [starcoder2-15b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ3_M.gguf) | IQ3_M | 6.8GB | | [starcoder2-15b.Q3_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K.gguf) | Q3_K | 7.49GB | | [starcoder2-15b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K_M.gguf) | Q3_K_M | 7.49GB | | [starcoder2-15b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q3_K_L.gguf) | Q3_K_L | 8.35GB | | [starcoder2-15b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ4_XS.gguf) | IQ4_XS | 8.12GB | | [starcoder2-15b.Q4_0.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_0.gguf) | Q4_0 | 8.44GB | | [starcoder2-15b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.IQ4_NL.gguf) | IQ4_NL | 8.55GB | | [starcoder2-15b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_K_S.gguf) | Q4_K_S | 8.53GB | | [starcoder2-15b.Q4_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_K.gguf) | Q4_K | 9.18GB | | [starcoder2-15b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_K_M.gguf) | Q4_K_M | 9.18GB | | [starcoder2-15b.Q4_1.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q4_1.gguf) | Q4_1 | 9.35GB | | [starcoder2-15b.Q5_0.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_0.gguf) | Q5_0 | 10.27GB | | [starcoder2-15b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_K_S.gguf) | Q5_K_S | 10.27GB | | [starcoder2-15b.Q5_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_K.gguf) | Q5_K | 10.65GB | | [starcoder2-15b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_K_M.gguf) | Q5_K_M | 10.65GB | | [starcoder2-15b.Q5_1.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q5_1.gguf) | Q5_1 | 11.18GB | | [starcoder2-15b.Q6_K.gguf](https://huggingface.co/RichardErkhov/bigcode_-_starcoder2-15b-gguf/blob/main/starcoder2-15b.Q6_K.gguf) | Q6_K | 12.2GB | Original model description: --- pipeline_tag: text-generation inference: parameters: temperature: 0.2 top_p: 0.95 widget: - text: 'def print_hello_world():' example_title: Hello world group: Python datasets: - bigcode/the-stack-v2-train license: bigcode-openrail-m library_name: transformers tags: - code model-index: - name: starcoder2-15b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 48.1 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 33.8 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 65.1 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 37.8 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 46.3 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 74.08 --- # StarCoder2 <center> <img src="https://huggingface.co/datasets/bigcode/admin_private/resolve/main/starcoder2_banner.png" alt="SC2" width="900" height="600"> </center> ## Table of Contents 1. [Model Summary](#model-summary) 2. [Use](#use) 3. [Limitations](#limitations) 4. [Training](#training) 5. [License](#license) 6. [Citation](#citation) ## Model Summary StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train), with opt-out requests excluded. The model uses [Grouped Query Attention](https://arxiv.org/abs/2305.13245), [a context window of 16,384 tokens](https://arxiv.org/abs/2205.14135) with [a sliding window attention of 4,096 tokens](https://arxiv.org/abs/2004.05150v2), and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 4+ trillion tokens. The model was trained with [NVIDIA NeMo™ Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) using the [NVIDIA Eos Supercomputer](https://blogs.nvidia.com/blog/eos/) built with [NVIDIA DGX H100](https://www.nvidia.com/en-us/data-center/dgx-h100/) systems. - **Project Website:** [bigcode-project.org](https://www.bigcode-project.org) - **Paper:** [Link](https://huggingface.co/papers/2402.19173) - **Point of Contact:** [contact@bigcode-project.org](mailto:contact@bigcode-project.org) - **Languages:** 600+ Programming languages ## Use ### Intended use The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is _not_ an instruction model and commands like "Write a function that computes the square root." do not work well. ### Generation Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's [GitHub repository](https://github.com/bigcode-project/starcoder2). First, make sure to install `transformers` from source: ```bash pip install git+https://github.com/huggingface/transformers.git ``` #### Running the model on CPU/GPU/multi GPU * _Using full precision_ ```python # pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main from transformers import AutoModelForCausalLM, AutoTokenizer checkpoint = "bigcode/starcoder2-15b" device = "cuda" # for GPU usage or "cpu" for CPU usage tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")` model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device) outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` * _Using `torch.bfloat16`_ ```python # pip install accelerate import torch from transformers import AutoTokenizer, AutoModelForCausalLM checkpoint = "bigcode/starcoder2-15b" tokenizer = AutoTokenizer.from_pretrained(checkpoint) # for fp16 use `torch_dtype=torch.float16` instead model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 32251.33 MB ``` #### Quantized Versions through `bitsandbytes` * _Using 8-bit precision (int8)_ ```python # pip install bitsandbytes accelerate from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig # to use 4bit use `load_in_4bit=True` instead quantization_config = BitsAndBytesConfig(load_in_8bit=True) checkpoint = "bigcode/starcoder2-15b" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config) inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0])) ``` ```bash >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") # load_in_8bit Memory footprint: 16900.18 MB # load_in_4bit >>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB") Memory footprint: 9224.60 MB ``` ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/search-v2) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. # Limitations The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See [the paper](https://huggingface.co/papers/2402.19173) for an in-depth discussion of the model limitations. # Training ## Model - **Architecture:** Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective - **Pretraining steps:** 1 million - **Pretraining tokens:** 4+ trillion - **Precision:** bfloat16 ## Hardware - **GPUs:** 1024 x H100 ## Software - **Framework:** [NeMo Framework](https://www.nvidia.com/en-us/ai-data-science/generative-ai/nemo-framework/) - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch) # License The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement). # Citation ```bash @misc{lozhkov2024starcoder, title={StarCoder 2 and The Stack v2: The Next Generation}, author={Anton Lozhkov and Raymond Li and Loubna Ben Allal and Federico Cassano and Joel Lamy-Poirier and Nouamane Tazi and Ao Tang and Dmytro Pykhtar and Jiawei Liu and Yuxiang Wei and Tianyang Liu and Max Tian and Denis Kocetkov and Arthur Zucker and Younes Belkada and Zijian Wang and Qian Liu and Dmitry Abulkhanov and Indraneil Paul and Zhuang Li and Wen-Ding Li and Megan Risdal and Jia Li and Jian Zhu and Terry Yue Zhuo and Evgenii Zheltonozhskii and Nii Osae Osae Dade and Wenhao Yu and Lucas Krauß and Naman Jain and Yixuan Su and Xuanli He and Manan Dey and Edoardo Abati and Yekun Chai and Niklas Muennighoff and Xiangru Tang and Muhtasham Oblokulov and Christopher Akiki and Marc Marone and Chenghao Mou and Mayank Mishra and Alex Gu and Binyuan Hui and Tri Dao and Armel Zebaze and Olivier Dehaene and Nicolas Patry and Canwen Xu and Julian McAuley and Han Hu and Torsten Scholak and Sebastien Paquet and Jennifer Robinson and Carolyn Jane Anderson and Nicolas Chapados and Mostofa Patwary and Nima Tajbakhsh and Yacine Jernite and Carlos Muñoz Ferrandis and Lingming Zhang and Sean Hughes and Thomas Wolf and Arjun Guha and Leandro von Werra and Harm de Vries}, year={2024}, eprint={2402.19173}, archivePrefix={arXiv}, primaryClass={cs.SE} } ```

{}

RichardErkhov/bigcode_-_starcoder2-15b-gguf

2024-05-03T13:30:03+00:00

TAGS #gguf #arxiv-2305.13245 #arxiv-2205.14135 #arxiv-2004.05150 #arxiv-2207.14255 #arxiv-2402.19173 #region-us

Quantization made by Richard Erkhov. Github Discord Request more models starcoder2-15b - GGUF * Model creator: URL * Original model: URL Name: starcoder2-15b.Q2\_K.gguf, Quant method: Q2\_K, Size: 5.77GB Name: starcoder2-15b.IQ3\_XS.gguf, Quant method: IQ3\_XS, Size: 6.25GB Name: starcoder2-15b.IQ3\_S.gguf, Quant method: IQ3\_S, Size: 6.52GB Name: starcoder2-15b.Q3\_K\_S.gguf, Quant method: Q3\_K\_S, Size: 6.51GB Name: starcoder2-15b.IQ3\_M.gguf, Quant method: IQ3\_M, Size: 6.8GB Name: starcoder2-15b.Q3\_K.gguf, Quant method: Q3\_K, Size: 7.49GB Name: starcoder2-15b.Q3\_K\_M.gguf, Quant method: Q3\_K\_M, Size: 7.49GB Name: starcoder2-15b.Q3\_K\_L.gguf, Quant method: Q3\_K\_L, Size: 8.35GB Name: starcoder2-15b.IQ4\_XS.gguf, Quant method: IQ4\_XS, Size: 8.12GB Name: starcoder2-15b.Q4\_0.gguf, Quant method: Q4\_0, Size: 8.44GB Name: starcoder2-15b.IQ4\_NL.gguf, Quant method: IQ4\_NL, Size: 8.55GB Name: starcoder2-15b.Q4\_K\_S.gguf, Quant method: Q4\_K\_S, Size: 8.53GB Name: starcoder2-15b.Q4\_K.gguf, Quant method: Q4\_K, Size: 9.18GB Name: starcoder2-15b.Q4\_K\_M.gguf, Quant method: Q4\_K\_M, Size: 9.18GB Name: starcoder2-15b.Q4\_1.gguf, Quant method: Q4\_1, Size: 9.35GB Name: starcoder2-15b.Q5\_0.gguf, Quant method: Q5\_0, Size: 10.27GB Name: starcoder2-15b.Q5\_K\_S.gguf, Quant method: Q5\_K\_S, Size: 10.27GB Name: starcoder2-15b.Q5\_K.gguf, Quant method: Q5\_K, Size: 10.65GB Name: starcoder2-15b.Q5\_K\_M.gguf, Quant method: Q5\_K\_M, Size: 10.65GB Name: starcoder2-15b.Q5\_1.gguf, Quant method: Q5\_1, Size: 11.18GB Name: starcoder2-15b.Q6\_K.gguf, Quant method: Q6\_K, Size: 12.2GB Original model description: --------------------------- pipeline\_tag: text-generation inference: parameters: temperature: 0.2 top\_p: 0.95 widget: * text: 'def print\_hello\_world():' example\_title: Hello world group: Python datasets: * bigcode/the-stack-v2-train license: bigcode-openrail-m library\_name: transformers tags: * code model-index: * name: starcoder2-15b results: + task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 48.1 + task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 33.8 + task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 65.1 + task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 37.8 + task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 46.3 + task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 74.08 --- StarCoder2 ==========  Table of Contents ----------------- 1. Model Summary 2. Use 3. Limitations 4. Training 5. License 6. Citation Model Summary ------------- StarCoder2-15B model is a 15B parameter model trained on 600+ programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 4+ trillion tokens. The model was trained with NVIDIA NeMo™ Framework using the NVIDIA Eos Supercomputer built with NVIDIA DGX H100 systems. * Project Website: URL * Paper: Link * Point of Contact: contact@URL * Languages: 600+ Programming languages Use --- ### Intended use The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is *not* an instruction model and commands like "Write a function that computes the square root." do not work well. ### Generation Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's GitHub repository. First, make sure to install 'transformers' from source: #### Running the model on CPU/GPU/multi GPU * *Using full precision* * *Using 'torch.bfloat16'* #### Quantized Versions through 'bitsandbytes' * *Using 8-bit precision (int8)* ### Attribution & Other Requirements The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a search index that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. Limitations =========== The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See the paper for an in-depth discussion of the model limitations. Training ======== Model ----- * Architecture: Transformer decoder with grouped-query and sliding window attention and Fill-in-the-Middle objective * Pretraining steps: 1 million * Pretraining tokens: 4+ trillion * Precision: bfloat16 Hardware -------- * GPUs: 1024 x H100 Software -------- * Framework: NeMo Framework * Neural networks: PyTorch License ======= The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.

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": []}

abc88767/model56

2024-05-03T13:30:26+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

# 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": []}

mostlyai/datallm-v2-meta-llama-3-8b

2024-05-03T13:32:31+00:00

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

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text-generation

transformers

<br/><br/> 3.2bpw/h6 exl2 quantization of [NeverSleep/Llama-3-Lumimaid-70B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) using default exllamav2 calibration dataset. --- **ORIGINAL CARD:** ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/630dfb008df86f1e5becadc3/d3QMaxy3peFTpSlWdWF-k.png" style="display: block; margin: auto;"> </div></center> This model uses the Llama3 **prompting format** Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our [Discord Server](https://discord.gg/MtCVRWTZXY). ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-70B-v0.1. Switch: [8B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1) - [70B](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1) - [70B-alt](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-70B-v0.1-alt) ## Training data used: - [Aesir datasets](https://huggingface.co/MinervaAI) - [NoRobots](https://huggingface.co/datasets/Doctor-Shotgun/no-robots-sharegpt) - [limarp](https://huggingface.co/datasets/lemonilia/LimaRP) - 8k ctx - [toxic-dpo-v0.1-sharegpt](https://huggingface.co/datasets/Undi95/toxic-dpo-v0.1-sharegpt) - [ToxicQAFinal](https://huggingface.co/datasets/NobodyExistsOnTheInternet/ToxicQAFinal) - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - [Squish42/bluemoon-fandom-1-1-rp-cleaned](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) - 50% (randomly) - [NobodyExistsOnTheInternet/PIPPAsharegptv2test](https://huggingface.co/datasets/NobodyExistsOnTheInternet/PIPPAsharegptv2test) - 5% (randomly) - [cgato/SlimOrcaDedupCleaned](https://huggingface.co/datasets/cgato/SlimOrcaDedupCleaned) - 5% (randomly) - Airoboros (reduced) - [Capybara](https://huggingface.co/datasets/Undi95/Capybara-ShareGPT/) (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ``` <|begin_of_text|><|start_header_id|>system<|end_header_id|> {system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|> {input}<|eot_id|><|start_header_id|>assistant<|end_header_id|> {output}<|eot_id|> ``` ## Others Undi: If you want to support us, you can [here](https://ko-fi.com/undiai). IkariDev: Visit my [retro/neocities style website](https://ikaridevgit.github.io/) please kek

{"license": "cc-by-nc-4.0", "tags": ["not-for-all-audiences", "nsfw"]}

JayhC/Llama-3-Lumimaid-70B-v0.1-3.2bpw-h6-exl2

2024-05-03T13:33:43+00:00

TAGS #transformers #safetensors #llama #text-generation #not-for-all-audiences #nsfw #conversational #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

<br/><br/> 3.2bpw/h6 exl2 quantization of NeverSleep/Llama-3-Lumimaid-70B-v0.1 using default exllamav2 calibration dataset. --- ORIGINAL CARD: ## Lumimaid 0.1 <center><div style="width: 100%;"> <img src="URL style="display: block; margin: auto;"> </div></center> This model uses the Llama3 prompting format Llama3 trained on our RP datasets, we tried to have a balance between the ERP and the RP, not too horny, but just enough. We also added some non-RP dataset, making the model less dumb overall. It should look like a 40%/60% ratio for Non-RP/RP+ERP data. This model includes the new Luminae dataset from Ikari. If you consider trying this model please give us some feedback either on the Community tab on hf or on our Discord Server. ## Credits: - Undi - IkariDev ## Description This repo contains FP16 files of Lumimaid-70B-v0.1. Switch: 8B - 70B - 70B-alt ## Training data used: - Aesir datasets - NoRobots - limarp - 8k ctx - toxic-dpo-v0.1-sharegpt - ToxicQAFinal - Luminae-i1 (70B/70B-alt) (i2 was not existing when the 70b started training) | Luminae-i2 (8B) (this one gave better results on the 8b) - Ikari's Dataset - Squish42/bluemoon-fandom-1-1-rp-cleaned - 50% (randomly) - NobodyExistsOnTheInternet/PIPPAsharegptv2test - 5% (randomly) - cgato/SlimOrcaDedupCleaned - 5% (randomly) - Airoboros (reduced) - Capybara (reduced) ## Models used (only for 8B) - Initial LumiMaid 8B Finetune - Undi95/Llama-3-Unholy-8B-e4 - Undi95/Llama-3-LewdPlay-8B ## Prompt template: Llama3 ## Others Undi: If you want to support us, you can here. IkariDev: Visit my retro/neocities style website please kek

transformers

# Uploaded model - **Developed by:** yadz45 - **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": ["fr"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "datasets": ["yadz45/V2"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}

yadz45/IA_V1-1

2024-05-03T13:34:15+00:00

TAGS #transformers #gguf #llama #text-generation-inference #unsloth #fr #dataset-yadz45/V2 #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us

# Uploaded model - Developed by: yadz45 - 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"/>

text-generation

transformers

# Llama3-ChatQA-1.5-8B-256K I tried to achive long context RAG pipeline with this model but I have very limited resources to test this workflow. Keep in mind that this is an experimentation. This model is an 'amalgamation' of `winglian/llama-3-8b-256k-PoSE` and `nvidia/Llama3-ChatQA-1.5-8B`. ## Recipe First I extracted the Lora adapter from `nvidia/Llama3-ChatQA-1.5-8B` using `mergekkit`. You can find the adapter [here](https://huggingface.co/beratcmn/Llama3-ChatQA-1.5-8B-lora). After the extraction I merged the adapter with the `winglian/llama-3-8b-256k-PoSE` model. ## Prompt Format Since base model wasn't finetuned for any specific format we can use the ChatQA's chat format. ```text System: {System} {Context} User: {Question} Assistant: {Response} User: {Question} Assistant: ``` Big thanks to Meta Team, Nvidia Team and of course Wing Lian. ## Notes This model has not been tested on any benchmarks due to compute limitations. Base model wasn't evaluated using `Needle in Haystack` as well. There is a big possibility that this model might perform worse than both of the original models.

{"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["mergekit", "peft", "nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "base_model": ["meta-llama/Meta-Llama-3-8B", "nvidia/Llama3-ChatQA-1.5-8B", "winglian/llama-3-8b-256k-PoSE"], "pipeline_tag": "text-generation"}

beratcmn/Llama3-ChatQA-1.5-8B-256K

2024-05-03T13:34:23+00:00

TAGS #transformers #safetensors #llama #text-generation #mergekit #peft #nvidia #chatqa-1.5 #chatqa #llama-3 #pytorch #en #base_model-meta-llama/Meta-Llama-3-8B #license-llama3 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Llama3-ChatQA-1.5-8B-256K I tried to achive long context RAG pipeline with this model but I have very limited resources to test this workflow. Keep in mind that this is an experimentation. This model is an 'amalgamation' of 'winglian/llama-3-8b-256k-PoSE' and 'nvidia/Llama3-ChatQA-1.5-8B'. ## Recipe First I extracted the Lora adapter from 'nvidia/Llama3-ChatQA-1.5-8B' using 'mergekkit'. You can find the adapter here. After the extraction I merged the adapter with the 'winglian/llama-3-8b-256k-PoSE' model. ## Prompt Format Since base model wasn't finetuned for any specific format we can use the ChatQA's chat format. Big thanks to Meta Team, Nvidia Team and of course Wing Lian. ## Notes This model has not been tested on any benchmarks due to compute limitations. Base model wasn't evaluated using 'Needle in Haystack' as well. There is a big possibility that this model might perform worse than both of the original models.

transformers

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

ferrazzipietro/LS_Llama-2-7b-hf_adapters_en.layer1_NoQuant_16_32_0.01_8_0.0002

2024-05-03T13:35:18+00:00

TAGS #transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us

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text-generation

transformers

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

cilantro9246/0wgp5uk

2024-05-03T13:35:48+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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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": "266.23 +/- 20.17", "name": "mean_reward", "verified": false}]}]}]}

Fetanos/Reinforcement_Learning

2024-05-03T13:37:58+00:00

TAGS #stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us

# PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library. ## Usage (with Stable-baselines3) TODO: Add your code

text-generation

transformers

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

massimilianowosz/Llama-3-8B-instruct-Japanese-Chef

2024-05-03T13:38:14+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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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-Instruct-v0.2-finetune-SWE_70_30_EN This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0943 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.8789 | 1.0 | 1443 | 1.4980 | | 1.141 | 2.0 | 2886 | 1.5139 | | 0.941 | 3.0 | 4329 | 1.6951 | | 1.2378 | 4.0 | 5772 | 1.8812 | | 0.3967 | 5.0 | 7215 | 2.0943 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-finetune-SWE_70_30_EN", "results": []}]}

JuanjoLopez19/Mistral-7B-Instruct-v0.2-finetune-SWE_70_30_EN

2024-05-03T13:39:25+00:00

TAGS #peft #safetensors #generated_from_trainer #base_model-mistralai/Mistral-7B-Instruct-v0.2 #license-apache-2.0 #region-us

Mistral-7B-Instruct-v0.2-finetune-SWE\_70\_30\_EN ================================================= This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 2.0943 Model description ----------------- More information needed Intended uses & limitations --------------------------- More information needed Training and evaluation data ---------------------------- More information needed Training procedure ------------------ ### Training hyperparameters The following hyperparameters were used during training: * learning\_rate: 0.0001 * train\_batch\_size: 2 * eval\_batch\_size: 2 * seed: 42 * optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 * lr\_scheduler\_type: constant * num\_epochs: 5 * mixed\_precision\_training: Native AMP ### Training results ### Framework versions * PEFT 0.10.0 * Transformers 4.40.1 * Pytorch 2.3.0+cu121 * Datasets 2.19.0 * Tokenizers 0.19.1

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": []}

wuzhongyanqiu/code-search-net-tokenizer

2024-05-03T13:40:14+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

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

{"license": "apache-2.0", "title": "Pneumonia Detection", "emoji": "\ud83c\udfe2", "colorFrom": "blue", "colorTo": "red", "sdk": "gradio", "sdk_version": "4.25.0", "app_file": "app.py", "pinned": false}

jeysshon/Isatron_V2

2024-05-03T13:40:26+00:00

TAGS #license-apache-2.0 #region-us

Check out the configuration reference at URL

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/xdxodp6

2024-05-03T13:41:10+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

# **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="Dat1710/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}]}]}]}

Dat1710/q-FrozenLake-v1-4x4-noSlippery

2024-05-03T13:41:48+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-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. --> # Llama-3-8B-Instruct-MoE-spider_full This model is a fine-tuned version of [VictorDCh/Llama-3-8B-Instruct-MoE](https://huggingface.co/VictorDCh/Llama-3-8B-Instruct-MoE) on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - Tokenizers 0.15.2

{"tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "VictorDCh/Llama-3-8B-Instruct-MoE", "model-index": [{"name": "Llama-3-8B-Instruct-MoE-spider_full", "results": []}]}

VictorDCh/Llama-3-8B-Instruct-MoE-spider_full

2024-05-03T13:41:55+00:00

TAGS #transformers #tensorboard #safetensors #mixtral #text-generation #trl #sft #generated_from_trainer #conversational #dataset-generator #base_model-VictorDCh/Llama-3-8B-Instruct-MoE #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Llama-3-8B-Instruct-MoE-spider_full This model is a fine-tuned version of VictorDCh/Llama-3-8B-Instruct-MoE on the generator dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 1 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 2 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.16.1 - 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": []}

rohangbs/death_se42-type2_May2024_Second

2024-05-03T13:43:03+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

text-generation

transformers

# Mistral-7b-Instruct-v0.1-int8-ov * Model creator: [Mistral AI](https://huggingface.co/mistralai) * Original model: [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) ## Description This is [Mistral-7b-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf). ## Quantization Parameters Weight compression was performed using `nncf.compress_weights` with the following parameters: * mode: **INT8_ASYM** For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html) ## Compatibility The provided OpenVINO™ IR model is compatible with: * OpenVINO version 2024.1.0 and higher * Optimum Intel 1.16.0 and higher ## Running Model Inference 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: ``` pip install optimum[openvino] ``` 2. Run model inference: ``` from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM model_id = "OepnVINO/mistral-7b-instrcut-v0.1-int8-ov" tokenizer = AutoTokenizer.from_pretrained(model_id) model = OVModelForCausalLM.from_pretrained(model_id) messages = [ {"role": "user", "content": "What is your favourite condiment?"}, {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"}, {"role": "user", "content": "Do you have mayonnaise recipes?"} ] inputs = tokenizer.apply_chat_template(messages, return_tensors="pt") outputs = model.generate(inputs, max_new_tokens=20) print(tokenizer.decode(outputs[0], skip_special_tokens=True)) ``` For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html). ## Limitations Check the original model card for [limitations](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1#limitations). ## Legal information The original model is distributed under [Apache 2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1).

{"language": ["en"], "license": "apache-2.0"}

OpenVINO/mistral-7b-instrcut-v0.1-int8-ov

2024-05-03T13:43:52+00:00

TAGS #transformers #openvino #mistral #text-generation #conversational #en #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# Mistral-7b-Instruct-v0.1-int8-ov * Model creator: Mistral AI * Original model: Mistral-7b-Instruct-v0.1 ## Description This is Mistral-7b-Instruct-v0.1 model converted to the OpenVINO™ IR (Intermediate Representation) format with weights compressed to INT8 by NNCF. ## Quantization Parameters Weight compression was performed using 'nncf.compress_weights' with the following parameters: * mode: INT8_ASYM For more information on quantization, check the OpenVINO model optimization guide ## Compatibility The provided OpenVINO™ IR model is compatible with: * OpenVINO version 2024.1.0 and higher * Optimum Intel 1.16.0 and higher ## Running Model Inference 1. Install packages required for using Optimum Intel integration with the OpenVINO backend: 2. Run model inference: For more examples and possible optimizations, refer to the OpenVINO Large Language Model Inference Guide. ## Limitations Check the original model card for limitations. ## Legal information The original model is distributed under Apache 2.0 license. More details can be found in original model card.

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/wtqfpaj

2024-05-03T13:45:59+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

# NeuralMillama-3-8B-MS NeuralMillama-3-8B-MS is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): ## 🧩 Configuration ```yaml models: - model: Kukedlc/NeuralMiLLaMa-8B-slerp - model: Kukedlc/SmartLlama-3-8B-MS-v0.1 - model: mlabonne/ChimeraLlama-3-8B-v2 - model: mlabonne/ChimeraLlama-3-8B-v3 merge_method: model_stock base_model: mlabonne/ChimeraLlama-3-8B-v3 dtype: float16 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "Kukedlc/NeuralMillama-3-8B-MS" 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"]}

Kukedlc/NeuralMillama-3-8B-MS

2024-05-03T13:51:40+00:00

TAGS #transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us

# NeuralMillama-3-8B-MS NeuralMillama-3-8B-MS is a merge of the following models using LazyMergekit: ## Configuration ## Usage

feature-extraction

sentence-transformers

This model is a fine-tuned version of [**jinaai/jina-embeddings-v2-base-en**](https://huggingface.co/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-03052024-73xx-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"], "license": "apache-2.0", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "mteb", "Events", "Meetups", "Networking", "Community", "Social"], "datasets": ["fine-tuned/jina-embeddings-v2-base-en-03052024-73xx-webapp", "allenai/c4"], "pipeline_tag": "feature-extraction"}

fine-tuned/jina-embeddings-v2-base-en-03052024-73xx-webapp

2024-05-03T13:52:22+00:00

TAGS #sentence-transformers #safetensors #bert #feature-extraction #sentence-similarity #mteb #Events #Meetups #Networking #Community #Social #custom_code #en #dataset-fine-tuned/jina-embeddings-v2-base-en-03052024-73xx-webapp #dataset-allenai/c4 #license-apache-2.0 #endpoints_compatible #region-us

This 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:

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. --> # Llama3_instruct_on_charttotext_server This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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-06 - train_batch_size: 4 - eval_batch_size: 8 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Llama3_instruct_on_charttotext_server", "results": []}]}

moetezsa/Llama3_instruct_on_charttotext_server

2024-05-03T13:56:58+00:00

TAGS #peft #safetensors #trl #sft #generated_from_trainer #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #region-us

# Llama3_instruct_on_charttotext_server This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct 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-06 - train_batch_size: 4 - eval_batch_size: 8 - 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: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 30 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.3.0+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

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="Sweety07/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}]}]}]}

Sweety07/q-FrozenLake-v1-4x4-noSlippery

2024-05-03T13:57:26+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

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. --> # Pho4B-ft This model is a fine-tuned version of [vinai/PhoGPT-4B](https://huggingface.co/vinai/PhoGPT-4B) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 38.3770 - Rewards/chosen: -3.5500 - Rewards/rejected: -3.5422 - Rewards/accuracies: 0.4533 - Rewards/margins: -0.0077 - Logps/rejected: -35.4224 - Logps/chosen: -35.4996 - Logits/rejected: 0.8757 - Logits/chosen: 0.8807 - Nll Loss: 38.3031 - Log Odds Ratio: -0.8538 - Log Odds Chosen: -0.0772 ## 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: 8e-06 - 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: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Nll Loss | Log Odds Ratio | Log Odds Chosen | |:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|:--------:|:--------------:|:---------------:| | 38.4131 | 0.5689 | 32 | 38.3770 | -3.5500 | -3.5422 | 0.4533 | -0.0077 | -35.4224 | -35.4996 | 0.8757 | 0.8807 | 38.3031 | -0.8538 | -0.0772 | ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.0 - Tokenizers 0.19.1

{"library_name": "peft", "tags": ["trl", "orpo", "generated_from_trainer"], "base_model": "vinai/PhoGPT-4B", "model-index": [{"name": "Pho4B-ft", "results": []}]}

iamnguyen/Pho4B-ft

2024-05-03T13:57:36+00:00

TAGS #peft #safetensors #mpt #trl #orpo #generated_from_trainer #custom_code #base_model-vinai/PhoGPT-4B #region-us

Pho4B-ft ======== This model is a fine-tuned version of vinai/PhoGPT-4B on an unknown dataset. It achieves the following results on the evaluation set: * Loss: 38.3770 * Rewards/chosen: -3.5500 * Rewards/rejected: -3.5422 * Rewards/accuracies: 0.4533 * Rewards/margins: -0.0077 * Logps/rejected: -35.4224 * Logps/chosen: -35.4996 * Logits/rejected: 0.8757 * Logits/chosen: 0.8807 * Nll Loss: 38.3031 * Log Odds Ratio: -0.8538 * Log Odds Chosen: -0.0772 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: 8e-06 * 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: cosine * lr\_scheduler\_warmup\_steps: 10 * num\_epochs: 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

# Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

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

vc64/llama2-7b_combinedQA

2024-05-03T13:57:47+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

reinforcement-learning

ml-agents

# **poca** Agent playing **SoccerTwos** This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/ We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction - A *longer tutorial* to understand how works ML-Agents: https://huggingface.co/learn/deep-rl-course/unit5/introduction ### Resume the training ```bash mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent **playing directly in your browser** 1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity 2. Step 1: Find your model_id: pietroorlandi/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play 👀

{"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]}

pietroorlandi/poca-SoccerTwos

2024-05-03T13:59:38+00:00

TAGS #ml-agents #tensorboard #onnx #SoccerTwos #deep-reinforcement-learning #reinforcement-learning #ML-Agents-SoccerTwos #region-us

# poca Agent playing SoccerTwos This is a trained model of a poca agent playing SoccerTwos using the Unity ML-Agents Library. ## Usage (with ML-Agents) The Documentation: URL We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: - A *short tutorial* where you teach Huggy the Dog to fetch the stick and then play with him directly in your browser: URL - A *longer tutorial* to understand how works ML-Agents: URL ### Resume the training ### Watch your Agent play You can watch your agent playing directly in your browser 1. If the environment is part of ML-Agents official environments, go to URL 2. Step 1: Find your model_id: pietroorlandi/poca-SoccerTwos 3. Step 2: Select your *.nn /*.onnx file 4. Click on Watch the agent play

reinforcement-learning

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Dat1710/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}

Dat1710/q-Taxi-v3

2024-05-03T14:01:14+00:00

TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us

# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage

reinforcement-learning

# **Q-Learning** Agent playing1 **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="Sweety07/Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```

{"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]}

Sweety07/Taxi-v3

2024-05-03T14:01:15+00:00

TAGS #Taxi-v3 #q-learning #reinforcement-learning #custom-implementation #model-index #region-us

# Q-Learning Agent playing1 Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage