CodeLlama-13b-Python-hf Open Source Code Model - Freely Assist in Efficient Generation and Understanding of Python Code

Codellama 13b Python Hf

Developed by codellama

Code Llama is a series of large models for code generation and understanding developed by Meta. This version is a 13B-parameter variant specialized for Python.

Large Language Model

Transformers

Other#Python Code Generation #Large Parameter Model #Autoregressive Architecture

Downloads 2,230

Release Time : 8/24/2023

Model Overview

A generative text model optimized for Python code synthesis and understanding, based on Transformer architecture.

Model Features

Python-Specific Optimization

Specially trained for Python syntax and ecosystem, significantly outperforming general-purpose versions in handling Python code.

Multiple Sizes Available

Offers four parameter sizes (7B/13B/34B/70B) to meet different computational resource requirements.

Industry-Grade License

Uses the business-friendly llama2 license, permitting both research and commercial use.

Model Capabilities

Code Completion

Python Code Generation

Code Understanding

Text Generation

Use Cases

Development Assistance

IDE Smart Completion

Integrated into development environments to provide context-aware code suggestions.

Improves Python development efficiency by 30-50%.

Educational Demo Generation

Automatically generates Python tutorial example code based on natural language descriptions.

Achieves 82% accuracy in educational code generation scenarios.

Code Conversion

Code Refactoring

Automatically optimizes existing Python code structure.

🚀 Code Llama

Code Llama is a collection of pretrained and fine - tuned generative text models with parameter scales ranging from 7 billion to 34 billion. This repository is for the 13B Python specialist version in the Hugging Face Transformers format. It is designed 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.

✨ Features

Code Llama consists of models with different parameter scales (7B, 13B, 34B, 70B) and three variants (base, Python, Instruct).
It is designed for general code synthesis and understanding, with specific variants for Python programming and instruction - following.
The model uses an optimized transformer architecture for text generation.

📦 Installation

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

pip install https://github.com/huggingface/transformers accelerate

📚 Documentation

Model Capabilities

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

Model Details

Property	Details
Model Developers	Meta
Variations	Code Llama comes in three model sizes (7B, 13B, 34B) and three variants: base for general code, Python - specific, and Instruct for instruction following.
This Repository	Contains the Python version of the 13B parameters model.
Input	Models input text only.
Output	Models generate text only.
Model Architecture	An auto - regressive language model using an optimized transformer architecture.
Model Dates	Trained between January 2023 and July 2023.
Status	A static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released with improved safety.
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 can be adapted for various code synthesis and understanding tasks, the Python variant is for Python programming, and the Instruct variant is for safer 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, and use 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 were performed on Meta’s Research Super Cluster.

Carbon Footprint Training all 9 Code Llama models required 400K GPU hours of computation on A100 - 80GB hardware. Estimated total emissions were 65.3 tCO2eq, 100% offset by Meta’s sustainability program.

Training Data

All experiments and released models were trained and fine - tuned using the same data as Llama 2 with different weights. See Section 2 and Table 1 in the research paper for details.

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 new technologies with risks. Testing has been in English and may not cover all scenarios. The model's outputs cannot be fully predicted, and it may produce inaccurate or objectionable responses. Developers should perform safety testing and tuning before deployment.

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

Model Variants Table

	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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