Qwen2.5-Coder-14B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 14.8B |
| License | Apache-2.0 |
| Context Length | 131,072 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-Coder-14B-Instruct-GGUF?
This is a GGUF-quantized version of the Qwen2.5-Coder-14B-Instruct model, specifically optimized for code-related tasks. It represents a significant advancement in the Qwen series, trained on 5.5 trillion tokens including source code and text-code grounding data.
Implementation Details
The model features a sophisticated architecture with 48 layers and 40 attention heads for queries and 8 for key-values, implementing Group-Query Attention (GQA). It supports an impressive context length of up to 131,072 tokens through YaRN technology.
- Advanced attention mechanism with GQA architecture
- Full support for long-context processing
- Optimized for both code generation and reasoning tasks
Core Capabilities
- Superior code generation and fixing abilities
- Enhanced code reasoning capabilities
- Strong mathematical and general competencies
- Code agent support for real-world applications
- Long-context processing up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
The model combines state-of-the-art code generation capabilities with extensive context length support and sophisticated attention mechanisms, making it particularly effective for complex coding tasks.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and reasoning tasks. It's particularly well-suited for software development, code review, and educational purposes where long-context understanding is crucial.
