Qwen2.5-Coder-3B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 3.09B (2.77B Non-Embedding) |
| License | Qwen Research |
| Context Length | 32,768 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-Coder-3B-Instruct-GGUF?
Qwen2.5-Coder-3B-Instruct-GGUF is a specialized code-generation language model that represents part of the latest Qwen2.5-Coder series. This GGUF-formatted version is specifically optimized for efficient deployment and inference, trained on 5.5 trillion tokens including source code and text-code grounding data.
Implementation Details
The model features a sophisticated architecture with 36 layers and employs Grouped-Query Attention (GQA) with 16 heads for queries and 2 for key-values. It supports multiple quantization options (q2_K to q8_0) for flexible deployment scenarios.
- Full 32K token context length support
- Advanced attention mechanisms with QKV bias
- Tied word embeddings for improved efficiency
- Multiple quantization options for different performance needs
Core Capabilities
- Enhanced code generation and reasoning
- Advanced code fixing capabilities
- Strong mathematical reasoning
- Comprehensive support for code agents
- General language understanding and generation
Frequently Asked Questions
Q: What makes this model unique?
The model combines efficient GGUF formatting with state-of-the-art code generation capabilities, offering a balance between performance and resource usage. Its 3B parameter size makes it accessible for deployment while maintaining strong coding capabilities.
Q: What are the recommended use cases?
This model is ideal for code generation, code reasoning, and fixing tasks. It's particularly well-suited for developers needing an efficient, deployable solution for code-related tasks while maintaining reasonable resource requirements.
