CodeLlama-7B-Python-GGUF
| Property | Value |
|---|---|
| Parameter Count | 6.74B parameters |
| License | Llama 2 |
| Research Paper | Code Llama Paper |
| Author | Meta (Original), TheBloke (GGUF conversion) |
What is CodeLlama-7B-Python-GGUF?
CodeLlama-7B-Python-GGUF is a specialized version of Meta's CodeLlama model, specifically optimized for Python programming tasks and converted to the efficient GGUF format. This model represents a significant advancement in code-specific AI, offering various quantization options for different performance and resource requirements.
Implementation Details
The model is available in multiple quantization formats ranging from 2-bit to 8-bit precision, allowing users to balance between model size and performance. The Q4_K_M variant (4-bit quantization) is recommended for most users, offering a good balance between size (4.08GB) and quality.
- Multiple quantization options (Q2_K to Q8_0)
- GPU layer offloading support
- Optimized for Python code generation
- Compatible with various frameworks including llama.cpp, text-generation-webui, and others
Core Capabilities
- Python code generation and completion
- Code understanding and analysis
- Support for extended context lengths
- Efficient CPU and GPU inference
Frequently Asked Questions
Q: What makes this model unique?
This model is specifically trained for Python programming tasks and offers various quantization options through the GGUF format, making it highly versatile for different deployment scenarios.
Q: What are the recommended use cases?
The model is ideal for Python code generation, code completion, and understanding tasks. It's particularly useful in development environments where Python expertise is needed.
