Qwen2.5-Coder-7B
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Apache 2.0 |
| Context Length | 128K tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Research Paper | arXiv:2409.12186 |
What is Qwen2.5-Coder-7B?
Qwen2.5-Coder-7B is part of the latest series of Code-Specific Qwen large language models, specifically designed for code-related tasks. Built upon the strong foundation of Qwen2.5, this model represents a significant advancement in code generation, reasoning, and fixing capabilities, trained on 5.5 trillion tokens including source code and text-code grounding data.
Implementation Details
The model features a sophisticated architecture with 28 layers and an equal number of attention heads for queries, while using 4 heads for keys and values through Group Query Attention (GQA). It implements advanced techniques including RoPE positional embeddings, SwiGLU activations, and RMSNorm for enhanced performance.
- 28 transformer layers with specialized attention mechanism
- Support for context lengths up to 131,072 tokens using YaRN technology
- Optimized for both short and long-context processing
- 6.53B non-embedding parameters for efficient computation
Core Capabilities
- Advanced code generation and completion
- Sophisticated code reasoning and problem-solving
- Efficient code fixing and debugging
- Long-context processing up to 128K tokens
- Mathematics and general task competency
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its specialized focus on code-related tasks while maintaining strong general capabilities. Its implementation of YaRN technology for handling long contexts and its efficient architecture make it particularly suitable for real-world coding applications.
Q: What are the recommended use cases?
While the model excels at code-related tasks, it's not recommended for direct conversational use. Instead, it's ideal for code generation, analysis, and fixing tasks, and can be further enhanced through post-training methods like SFT or RLHF for specific applications.
