Qwen2.5-Coder-0.5B-Instruct-GGUF
| Property | Value |
|---|---|
| Parameter Count | 0.49B (0.36B Non-Embedding) |
| Context Length | 32,768 tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Number of Layers | 24 |
| Attention Heads | 14 for Q, 2 for KV (GQA) |
| Model Type | Causal Language Model |
| Paper | Qwen2.5-Coder Technical Report |
What is Qwen2.5-Coder-0.5B-Instruct-GGUF?
Qwen2.5-Coder-0.5B-Instruct-GGUF is part of the latest Qwen2.5-Coder series, specifically designed for code generation and reasoning tasks. This 0.5B parameter model represents the lightweight version in the series, optimized for efficient deployment while maintaining strong coding capabilities.
Implementation Details
The model implements advanced architectural features including Rotary Position Embedding (RoPE), SwiGLU activation functions, and RMSNorm normalization. It supports multiple quantization formats (q2_K to q8_0) for flexible deployment options and features a full 32,768 token context window.
- Advanced attention mechanism with Group Query Attention (GQA)
- Comprehensive training on 5.5 trillion tokens including source code and text-code grounding
- Support for efficient quantization options
- Optimized for both code generation and general language tasks
Core Capabilities
- Code generation and completion
- Code reasoning and problem-solving
- Bug fixing and code optimization
- Mathematical computation
- General language understanding
Frequently Asked Questions
Q: What makes this model unique?
The model combines efficient architecture with extensive code-specific training, making it particularly suitable for deployment in resource-constrained environments while maintaining strong coding capabilities. Its 32K context window and various quantization options provide flexibility for different use cases.
Q: What are the recommended use cases?
This model is ideal for code completion tools, automated code review systems, and educational programming assistants. It's particularly suitable for scenarios requiring quick responses and efficient resource usage, such as IDE plugins or mobile development environments.
