| Property | Value |
|---|---|
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| License | Apache-2.0 |
| Context Length | 32,768 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias |
| Papers | Technical Report |
What is Qwen2.5-Coder-7B-Instruct-GGUF?
Qwen2.5-Coder-7B-Instruct-GGUF is part of the latest series of Code-Specific Qwen large language models, specifically designed for code-related tasks. This GGUF-formatted model represents a significant advancement in code generation and reasoning capabilities, trained on 5.5 trillion tokens including source code and text-code grounding data.
Implementation Details
The model features 28 layers with 28 attention heads for queries and 4 for key-values, implementing Group-Query Attention (GQA). It supports multiple quantization options including q2_K through q8_0, making it adaptable to different computational resources.
- Full 32,768 token context length with potential for extension
- Advanced architecture combining RoPE, SwiGLU, and RMSNorm
- Flexible quantization options for different deployment scenarios
Core Capabilities
- Enhanced code generation and reasoning
- Improved code fixing abilities
- Strong mathematical reasoning
- Support for Code Agents applications
- Long-context processing up to 128K tokens
Frequently Asked Questions
Q: What makes this model unique?
This model combines advanced code generation capabilities with practical features like multiple quantization options and extensive context length support, making it particularly suitable for real-world coding applications and development workflows.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and mathematical reasoning tasks. It's particularly well-suited for developers needing AI assistance in coding projects, code review processes, and technical documentation generation.