Hugging Face Guide to Code Llama

Code Llama is a collection of pretrained and fine-tuned generative text models ranging from 7 billion to 70 billion parameters. This repository contains the base 70B version in the Hugging Face Transformers format.

Model Use
To use this model, make sure to install the transformers package by running the following command:
“`bash
pip install transformers
“`

Model Details
– Model Developers: Meta
– Variations: Code Llama comes in four model sizes and three variants
– Code Llama: base models
– Code Llama – Python: designed for Python
– Code Llama – Instruct: for instruction following and safer deployment
– Input: Models input text only
– Output: Models generate text only
– Model Architecture: Code Llama is an auto-regressive language model that uses an optimized transformer architecture fine-tuned with up to 16k tokens
– Model Dates: Trained between January 2023 and January 2024
– License: A custom commercial license is available at [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
– Research Paper: More information can be found in the paper “Code Llama: Open Foundation Models for Code” or its [arXiv page](https://arxiv.org/abs/2308.12950)

Intended Use
– Intended Use Cases: Commercial and research use in English and relevant programming languages
– Out-of-Scope Uses: Use in any manner that violates applicable laws or regulations, use in languages other than English, or in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants

Hardware and Software
– Training Factors: Custom training libraries and training performed on Meta’s Research Super Cluster
– Carbon Footprint: Training all 12 Code Llama models required 1400K GPU hours with 228.55 tCO2eq emissions, 100% of which were offset by Meta’s sustainability program

Evaluation Results
– See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper

Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Potential outputs cannot be predicted in advance, and the model may produce inaccurate or objectionable responses. Developers should perform safety testing and tuning tailored to their specific applications of the model.

For more information, please refer to the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide)

Source link
## Hugging Face Code Llama Model Manual

### Introduction
Code Llama is a collection of pretrained and fine-tuned generative text models offered by Hugging Face. This manual serves as a guide for using the base 70B version of the Code Llama model.

### Model Use
To use this model, it is necessary to install the `transformers` library. You can do this using the following command:
“`bash
pip install transformers accelerate
“`

### Model Details
– **Model Developers**: Meta
– **Variations**: Code Llama comes in four model sizes and three variants: Code Llama, Code Llama – Python, and Code Llama – Instruct.
– All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.
– This repository contains the base version of the 70B parameters model.
– **Input**: Models input text only
– **Output**: Models generate text only
– **Model Architecture**: Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
– **Model Dates**: Code Llama and its variants have been trained between January 2023 and January 2024.
– **Status**: This is a static model trained on an offline dataset. Future versions of Code Llama – Instruct will be released as model safety improves with community feedback.
– **License**: A custom commercial license is available at [Meta’s page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
– **Research Paper**: More information about the model can be found in the paper “Code Llama: Open Foundation Models for Code” available at [Meta’s research page](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code) or its [arXiv page](https://arxiv.org/abs/2308.12950).

### Intended Use
– **Intended Use Cases**: Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. Each variant has specific use cases:
– Code Llama: general code synthesis and understanding
– Code Llama – Python: for Python programming language
– Code Llama – Instruct: for code assistant and generation applications
– **Out-of-Scope Uses**: Use of the model in violation of applicable laws or regulations, use in languages other than English, and any other usage prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.

### Hardware and Software
– **Training Factors**: Custom training libraries and training performed on Meta’s Research Super Cluster.
– **Carbon Footprint**: The training of all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB. Estimated total emissions were offset by Meta’s sustainability program.

### Evaluation Results
For detailed evaluations and safety assessments, refer to Section 3 and 4 of the research paper provided.

### Ethical Considerations and Limitations
Code Llama and its variants are a new technology with potential risks. As the model may produce inaccurate or objectionable responses, developers should perform safety testing and tuning tailored to their specific applications. A Responsible Use Guide is available at [Meta’s Responsible Use Guide page](https://ai.meta.com/llama/responsible-use-guide).

This manual provides an overview of the Hugging Face Code Llama model, including its use, details, intended use, hardware and software requirements, evaluation results, and ethical considerations. For further information and updates, refer to Meta’s official resources.

Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the base 70B version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.



Model Use

To use this model, please make sure to install transformers.

pip install transformers accelerate

Model capabilities:



Model Details

*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).

Model Developers Meta

Variations Code Llama comes in four model sizes, and three variants:

  • Code Llama: base models designed for general code synthesis and understanding
  • Code Llama – Python: designed specifically for Python
  • Code Llama – Instruct: for instruction following and safer deployment

All variants are available in sizes of 7B, 13B, 34B, and 70B parameters.

This repository contains the base version of the 70B parameters model.

Input Models input text only.

Output Models generate text only.

Model Architecture Code Llama is an auto-regressive language model that uses an optimized transformer architecture. It was fine-tuned with up to 16k tokens and supports up to 100k tokens at inference time.

Model Dates Code Llama and its variants have been trained between January 2023 and January 2024.

Status This is a static model trained on an offline dataset. Future versions of Code Llama – Instruct will be released as we improve model safety with community feedback.

License A custom commercial license is available at: https://ai.meta.com/resources/models-and-libraries/llama-downloads/

Research Paper More information can be found in the paper “Code Llama: Open Foundation Models for Code” or its arXiv page.



Intended Use

Intended Use Cases Code Llama and its variants are intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama – Python is designed specifically to handle the Python programming language, and Code Llama – Instruct is intended to be safer to use for code assistant and generation applications.

Out-of-Scope Uses Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.



Hardware and Software

Training Factors We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.

Carbon Footprint In aggregate, training all 12 Code Llama models required 1400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 228.55 tCO2eq, 100% of which were offset by Meta’s sustainability program.



Evaluation Results

See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.



Ethical Considerations and Limitations

Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.

Please see the Responsible Use Guide available available at https://ai.meta.com/llama/responsible-use-guide.

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