CodeLlama-7B
CodeLlama-7B is a fine-tuned variant of the Llama 2-7B model, specifically optimized for code generation tasks. The model is designed to assist in generating and completing code snippets in various programming languages, making it ideal for use in code autocompletion, code suggestions, and software development tools.
Model Details
- Model Name: CodeLlama-7B
- Base Model: Llama 2-7B
- Model Developers: Fine-tuned by MertML
- License: Custom commercial license. Please refer to the repository for terms.
- Intended Use: Designed for code generation tasks, including autocompletion, code suggestions, and assisting developers in writing efficient code.
Model Architecture
CodeLlama-7B is based on the Llama 2-7B architecture, an autoregressive language model using the transformer architecture. It has been fine-tuned specifically for programming-related tasks, where the model is trained to generate code in response to natural language prompts or partially written code.
Intended Use Cases
Generating code snippets, completing code, and assisting developers with writing and debugging code in various programming languages such as Python, JavaScript, Java, C++, and more.
Out-of-Scope Uses
While the model is capable of natural language generation, it is specifically optimized for code-related tasks and may not perform well for general text generation tasks.
Training Data
The model was fine-tuned on a large corpus of publicly available code from platforms like GitHub, Stack Overflow, and other open-source repositories. The training dataset includes millions of code examples in various languages and styles to enhance the model's capability in generating functional and efficient code.
- Training Data Size: Over 100 million code snippets from publicly available repositories.
- Data Source: GitHub, Stack Overflow, and other open-source code repositories.
- Data Preprocessing: Code formatting was standardized, and non-functional or broken code was filtered out. Special attention was given to the inclusion of multiple programming languages to ensure multi-language support.
Model Performance
CodeLlama-7B has demonstrated high proficiency in generating syntactically correct and contextually relevant code across a range of programming languages. The model has been evaluated on several coding challenges and benchmarks, including tasks like autocompletion, code generation, and fixing bugs in code.
Evaluation Metrics
- Accuracy: The percentage of generated code snippets that are syntactically correct and can be executed successfully.
- Completion Rate: Measures how often the model generates code completions that match the user's intent.
- Code Quality: Evaluates the efficiency and readability of the generated code, including considerations for performance and adherence to best practices.