Qwen2.5-Coder-32B-Instruct-GPTQ-Int4
| Property | Value |
|---|---|
| Parameter Count | 32.5B (31.0B Non-Embedding) |
| Model Type | Causal Language Model (Code-Specific) |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and QKV bias |
| Quantization | GPTQ 4-bit |
| Paper | Qwen2.5-Coder Technical Report |
What is Qwen2.5-Coder-32B-Instruct-GPTQ-Int4?
Qwen2.5-Coder-32B-Instruct-GPTQ-Int4 represents the latest advancement in code-specific language models from the Qwen series. This model has been trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data, making it a state-of-the-art open-source codeLLM that rivals GPT-4's coding capabilities.
Implementation Details
The model features a sophisticated architecture with 64 layers and 40 attention heads for queries and 8 for key-values (GQA). It's been quantized to 4-bit precision using GPTQ while maintaining high performance. The model supports an impressive context length of up to 128K tokens through YaRN scaling technology.
- Advanced transformer architecture with RoPE, SwiGLU, and RMSNorm
- 4-bit quantization for efficient deployment
- Support for extensive context handling up to 131,072 tokens
- Integration with modern deployment solutions like vLLM
Core Capabilities
- State-of-the-art code generation and reasoning
- Advanced code fixing and debugging
- Strong mathematical and general competencies
- Long-context support for complex programming tasks
- Efficient deployment through quantization
Frequently Asked Questions
Q: What makes this model unique?
This model combines state-of-the-art coding capabilities with efficient 4-bit quantization and exceptional context length support, making it particularly suitable for real-world applications and code agents. Its training on 5.5 trillion tokens puts it at the forefront of open-source code-specific models.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and complex programming tasks. It's particularly well-suited for developing code agents, handling long-context programming scenarios, and supporting comprehensive software development workflows.
