CodeLlama-70b-Python-hf Open-Source Code Generation Model - Free Deployment for Efficient Python Code Generation

Codellama 70b Python Hf

Developed by codellama

Code Llama is a 70B-parameter Python-specialized code generation model developed by Meta, optimized based on the Llama-2 architecture with support for 16k context length

Large Language Model

Transformers

Other#70B parameters #Python specialized #Code generation

Downloads 115

Release Time : 1/29/2024

Model Overview

A 70B-parameter large language model specifically designed for Python code synthesis and understanding, serving as the Python-specialized version of the Code Llama series

Model Features

Python specialization

Fine-tuned with an additional 100B Python code tokens, excelling in Python-related tasks

Long context support

Supports 16k token context window, ideal for handling complex code files

Commercial license

Uses llama2 license, allowing qualified commercial usage

Model Capabilities

Python code generation

Code completion

Code understanding

Code comment generation

Code refactoring suggestions

Use Cases

Development tools

IDE intelligent completion

Integrated into development environments to provide context-aware code suggestions

Improves developer efficiency by approximately 30-50%

Automated code review

Analyzes code quality and provides improvement suggestions

Can identify common code smells and potential bugs

Education

Programming teaching assistant

Generates teaching example code or explains programming concepts

🚀 Code Llama

Code Llama is a set of pretrained and fine - tuned generative text models with parameter scales ranging from 7 billion to 70 billion. This repository hosts the 70B Python specialist version in the Hugging Face Transformers format. The model is crafted for general code synthesis and understanding. Links to other models can be found in the index at the bottom.

⚠️ Important Note

This is a non - official Code Llama repo. You can find the official Meta repository in the Meta Llama organization.

🚀 Quick Start

To use this model, you need to install transformers.

pip install transformers accelerate

✨ Features

Property	Details
Model Type	Code Llama comes in four model sizes (7B, 13B, 34B, 70B) and three variants: base model for general code synthesis and understanding, Python - specific model, and Instruct model for instruction following and safer deployment. This repo contains the 70B Python version.
Training Data	Trained between January 2023 and January 2024 using custom training libraries on Meta’s Research Super Cluster.
Input	The models only accept text as input.
Output	The models only generate text.
Model Architecture	An auto - regressive language model using an optimized transformer architecture, fine - tuned with up to 16k tokens. This variant does not support a long context of up to 100k tokens.
Status	A static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as model safety is improved with community feedback.

Model Capabilities

[x] Code completion.
[ ] Infilling.
[ ] Instructions / chat.
[x] Python specialist.

📚 Documentation

Intended Use

Intended Use Cases Code Llama and its variants are for commercial and research use in English and relevant programming languages. The base model can be adapted for various code synthesis and understanding tasks. The Python variant is for Python programming, and the Instruct variant is safer for code assistant and generation applications.

Out - of - Scope Uses Using the model in any way that violates applicable laws or regulations (including trade compliance laws), in languages other than English, or in any way prohibited by the Acceptable Use Policy and Licensing Agreement.

Hardware and Software

Training Factors Custom training libraries were used, and the training and fine - tuning of the released models were performed on Meta’s Research Super Cluster.

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

Evaluation Results

Refer to the evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper "Code Llama: Open Foundation Models for Code" or its arXiv page.

Ethical Considerations and Limitations

Code Llama and its variants are new technologies with risks. Testing has been in English and cannot cover all scenarios. As with all LLMs, the model's potential outputs cannot be predicted in advance, and it may produce inaccurate or objectionable responses. Developers should perform safety testing and tuning for their specific applications before deployment. See the Responsible Use Guide at https://ai.meta.com/llama/responsible-use-guide.

📄 License

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

Model Index

	Base Model	Python	Instruct
7B	codellama/CodeLlama-7b-hf	codellama/CodeLlama-7b-Python-hf	codellama/CodeLlama-7b-Instruct-hf
13B	codellama/CodeLlama-13b-hf	codellama/CodeLlama-13b-Python-hf	codellama/CodeLlama-13b-Instruct-hf
34B	codellama/CodeLlama-34b-hf	codellama/CodeLlama-34b-Python-hf	codellama/CodeLlama-34b-Instruct-hf
70B	codellama/CodeLlama-70b-hf	codellama/CodeLlama-70b-Python-hf	codellama/CodeLlama-70b-Instruct-hf

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