Qwen2.5-Coder-32B-Instruct-128K-GGUF
| Property | Value |
|---|---|
| Parameter Count | 32.5B |
| Context Length | 131,072 tokens |
| License | Apache 2.0 |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Number of Layers | 64 |
| Attention Heads | 40 Q heads, 8 KV heads (GQA) |
What is Qwen2.5-Coder-32B-Instruct-128K-GGUF?
Qwen2.5-Coder-32B-Instruct is a state-of-the-art code-focused large language model developed by Alibaba Cloud. It represents the latest iteration in the Qwen series, specifically optimized for code generation, reasoning, and fixing tasks. Trained on 5.5 trillion tokens including source code and text-code grounding data, this model achieves performance levels comparable to GPT-4 in coding tasks.
Implementation Details
The model implements advanced architectural features including Rotary Position Embedding (RoPE), SwiGLU activations, and RMSNorm normalization. It employs Grouped-Query Attention (GQA) with 40 query heads and 8 key-value heads, optimizing both performance and efficiency.
- Full 131,072 token context window support
- Efficient GGUF format implementation
- Comprehensive instruction tuning for enhanced interaction
- Optimized for both code generation and general tasks
Core Capabilities
- Advanced code generation and completion
- Sophisticated code reasoning and analysis
- Robust code fixing and debugging
- Mathematical problem-solving
- General-purpose assistance and reasoning
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its extensive 32.5B parameter size combined with a massive 128K context window, making it particularly powerful for handling large codebases and complex programming tasks. Its instruction-tuned nature ensures better alignment with user intentions.
Q: What are the recommended use cases?
The model excels in professional software development scenarios, including code generation, debugging, code review, and technical documentation. It's particularly suitable for projects requiring deep code understanding and complex problem-solving capabilities.
