| Property | Value |
|---|---|
| Parameter Count | 32.5B |
| Context Length | 131,072 tokens |
| License | Apache 2.0 |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | View Paper |
What is Qwen2.5-Coder-32B-Instruct-128K-GGUF?
Qwen2.5-Coder is a state-of-the-art code-specific language model that represents the latest advancement in Alibaba Cloud's Qwen series. This particular version is the instruction-tuned 32B parameter model optimized for GGUF format with extended 128K context window. It has been trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data.
Implementation Details
The model features a sophisticated architecture with 64 layers and employs 40 attention heads for queries and 8 for key-values using Group Query Attention (GQA). It leverages advanced techniques including RoPE for positional encoding, SwiGLU activations, and RMSNorm for normalization.
- Full 131,072 token context length support
- 31.0B non-embedding parameters
- Advanced attention mechanism with GQA
- Comprehensive instruction tuning
Core Capabilities
- Superior code generation and completion
- Advanced code reasoning and problem-solving
- Efficient code fixing and debugging
- Strong mathematical reasoning abilities
- Enhanced performance for Code Agents applications
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its exceptional code generation capabilities that rival GPT-4, along with its extensive 128K context window and optimized GGUF format for efficient deployment. It's particularly notable for combining strong coding abilities with mathematical reasoning and general competencies.
Q: What are the recommended use cases?
The model excels in software development tasks, including code generation, debugging, and technical problem-solving. It's particularly well-suited for building Code Agents, supporting software development workflows, and handling complex programming challenges that require extended context understanding.