Qwen2.5-Coder-32B-Instruct-bnb-4bit
| Property | Value |
|---|---|
| Model Size | 32B parameters |
| Context Length | 32,768 tokens |
| Quantization | 4-bit BNB |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | arXiv:2409.12186 |
What is Qwen2.5-Coder-32B-Instruct-bnb-4bit?
Qwen2.5-Coder-32B-Instruct is a state-of-the-art code-specific language model that has been optimized through 4-bit quantization. Built on the foundation of Qwen2.5, this model has been trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data, positioning it as one of the most capable open-source code LLMs available.
Implementation Details
The model implements advanced architectural features including RoPE (Rotary Position Embedding), SwiGLU activation functions, and RMSNorm. It utilizes Group Query Attention (GQA) with 14 heads for queries and 2 for key/values, optimizing both performance and efficiency.
- Full 32K context window support
- 4-bit quantization for reduced memory footprint
- Integrated with Hugging Face transformers library
- Requires transformers >= 4.37.0
Core Capabilities
- Advanced code generation and completion
- Code reasoning and debugging
- Mathematical problem-solving
- Text-code grounding
- Code fixing and optimization
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its extensive training on 5.5 trillion tokens and its ability to match GPT-4's coding capabilities while being open-source. The 4-bit quantization makes it more accessible for deployment while maintaining performance.
Q: What are the recommended use cases?
This model excels in code generation, debugging, and optimization tasks. It's particularly well-suited for development environments where code understanding and generation are primary requirements. However, it's not recommended for general conversational tasks without additional fine-tuning.
