| Property | Value |
|---|---|
| Parameter Count | 0.49B (0.36B Non-Embedding) |
| Model Type | Causal Language Model |
| Architecture | Transformer with RoPE, SwiGLU, RMSNorm |
| Context Length | 32,768 tokens |
| Number of Layers | 24 |
| Attention Heads | 14 for Q, 2 for KV (GQA) |
What is Qwen2.5-Coder-0.5B-Instruct?
Qwen2.5-Coder-0.5B-Instruct is part of the latest Qwen2.5-Coder series, specifically designed for code generation and understanding. This instruction-tuned model represents the compact version of the series, optimized for efficiency while maintaining strong coding capabilities.
Implementation Details
The model implements advanced architectural features including Rotary Position Embedding (RoPE), SwiGLU activations, and RMSNorm. It utilizes a GroupQuery Attention mechanism with 14 heads for queries and 2 for key/values, optimizing computational efficiency while maintaining performance.
- Full 32K token context window
- 24-layer architecture with tied word embeddings
- Comprehensive code generation and fixing capabilities
- Optimized for real-world code applications
Core Capabilities
- Code generation and completion
- Code reasoning and debugging
- Mathematical problem-solving
- Text-code grounding
- Code agent functionality
Frequently Asked Questions
Q: What makes this model unique?
The model combines efficient architecture with extensive training on 5.5 trillion tokens, including source code and synthetic data, making it particularly effective for code-related tasks despite its compact size.
Q: What are the recommended use cases?
The model is ideal for code generation, debugging, and general programming assistance, particularly in scenarios where computational resources are limited but high-quality code generation is required.