Qwen2.5-Coder-7B-Instruct
| Property | Value |
|---|---|
| Parameter Count | 7.62B |
| License | Apache 2.0 |
| Context Length | 128K tokens |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm |
| Paper | Technical Report |
What is Qwen2.5-Coder-7B-Instruct?
Qwen2.5-Coder-7B-Instruct is a specialized instruction-tuned language model designed specifically for code-related tasks. It represents part of the latest Qwen2.5-Coder series, trained on 5.5 trillion tokens including source code, text-code grounding, and synthetic data. This model combines advanced coding capabilities with strong mathematical and general competencies.
Implementation Details
The model architecture features 28 layers with 28 attention heads for queries and 4 for key-values, implementing Group Query Attention (GQA). It utilizes advanced components including RoPE for position encoding, SwiGLU activations, and RMSNorm for normalization. The model supports an extensive context length of 131,072 tokens through YaRN technology.
- 7.61B total parameters (6.53B non-embedding)
- 28 transformer layers with GQA attention mechanism
- Full 128K token context support with YaRN scaling
- BF16 tensor type for efficient computation
Core Capabilities
- Advanced code generation and completion
- Sophisticated code reasoning and problem-solving
- Code fixing and debugging assistance
- Long-context understanding up to 128K tokens
- Mathematical reasoning and general task competency
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized code-focused training on 5.5 trillion tokens and its ability to handle extremely long contexts up to 128K tokens. It balances specialized coding capabilities with general-purpose abilities, making it versatile for both development and broader tasks.
Q: What are the recommended use cases?
The model excels in code generation, debugging, and technical problem-solving. It's particularly suitable for software development workflows, code review processes, and educational contexts where detailed code explanation is needed. The long context window makes it especially valuable for analyzing and working with large codebases.
