CodeLlama-34B-GGUF
| Property | Value |
|---|---|
| Parameter Count | 33.7B |
| Model Type | Code Generation (LLaMA Architecture) |
| License | LLaMA 2 |
| Research Paper | Code Llama: Open Foundation Models for Code |
| Author | Meta (Original), TheBloke (GGUF Conversion) |
What is CodeLlama-34B-GGUF?
CodeLlama-34B-GGUF is a powerful code generation model converted to the efficient GGUF format, offering various quantization options from 2-bit to 8-bit precision. This model is specifically designed for code synthesis and understanding, building upon Meta's original CodeLlama architecture with optimizations for deployment across different computing environments.
Implementation Details
The model utilizes the new GGUF format, which replaced GGML, providing improved tokenization and special token support. It offers multiple quantization methods, from Q2_K (14.21GB) to Q8_0 (35.86GB), allowing users to balance between model size and performance based on their requirements.
- Supports GPU acceleration with layer offloading
- Compatible with popular frameworks like llama.cpp, text-generation-webui, and KoboldCpp
- Offers extended context length support with automatic RoPE scaling
- Includes Python integration through libraries like llama-cpp-python and ctransformers
Core Capabilities
- General code synthesis and understanding
- Code completion and generation
- Multiple programming language support
- Flexible deployment options from CPU to GPU
Frequently Asked Questions
Q: What makes this model unique?
CodeLlama-34B-GGUF stands out for its efficient GGUF format implementation and multiple quantization options, making it highly adaptable for different hardware configurations while maintaining code generation capabilities of the original 34B parameter model.
Q: What are the recommended use cases?
The model is ideal for code completion, development assistance, and general programming tasks. For optimal performance-to-resource ratio, the Q4_K_M quantization is recommended for most users, offering a good balance between model size (20.22GB) and quality.
