Qwen2.5-Coder-32B-Instruct-GPTQ-Int8
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, GQA |
| Quantization | GPTQ 8-bit |
| Model Hub | Hugging Face |
What is Qwen2.5-Coder-32B-Instruct-GPTQ-Int8?
Qwen2.5-Coder-32B-Instruct-GPTQ-Int8 is a state-of-the-art code-specific language model that represents the latest advancement in the Qwen series. Trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data, this model achieves coding capabilities that rival GPT-4, while maintaining strong performance in mathematics and general tasks.
Implementation Details
The model features a sophisticated architecture utilizing 64 layers and a unique attention mechanism with 40 heads for queries and 8 for key-values. It implements advanced techniques like RoPE (Rotary Position Embedding), SwiGLU activation, and RMSNorm, optimized through GPTQ 8-bit quantization for efficient deployment.
- Full 128K token context length support through YaRN scaling
- Comprehensive code generation, reasoning, and fixing capabilities
- Optimized for real-world applications and Code Agents
- Advanced attention mechanism with grouped-query attention (GQA)
Core Capabilities
- State-of-the-art code generation matching GPT-4
- Enhanced code reasoning and debugging
- Long-context processing up to 128K tokens
- Efficient deployment through 8-bit quantization
- Strong performance in mathematics and general tasks
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its exceptional code generation capabilities matching GPT-4, while being open-source and optimized for efficient deployment through GPTQ quantization. Its 128K context length and sophisticated architecture make it particularly suitable for complex coding tasks.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and analysis tasks. It's particularly well-suited for software development, code review, and educational purposes. The long context length makes it effective for handling large codebases and detailed documentation.
