Qwen 2.5 Coder 7B is a specialized code generation language model that belongs to the broader Qwen 2.5 Coder family developed by Alibaba Cloud. The model features 7.61 billion parameters (6.53B non-embedding) and utilizes a transformer architecture incorporating RoPE, SwiGLU, RMSNorm, and Attention QKV bias. As detailed in the technical report, the model represents a significant advancement in code-specific language models.
Architecture and Training
The model was trained on an extensive dataset comprising 5.5 trillion tokens, which includes source code, text-code grounding data, and synthetic data. This comprehensive training approach has enabled the model to achieve state-of-the-art performance among open-source code LLMs, with capabilities comparable to GPT-4o. A key architectural feature is its support for long-context processing, handling up to 131,072 tokens through the implementation of YaRN (Yet another RoPE extension), though it's worth noting that YaRN usage may impact performance on shorter texts.
The model demonstrates proficiency across 92 programming languages and maintains strong capabilities in mathematics and general knowledge tasks. This versatility stems from its foundation on the Qwen 2.5 base model, which has been specifically enhanced for code-related tasks while preserving its broader computational abilities.
Features and Capabilities
Qwen 2.5 Coder 7B shows marked improvements over its predecessor, CodeQwen 1.5, particularly in:
- Code generation and completion
- Multi-programming language support
- Code reasoning and repair
- Mathematical problem-solving
- Long-context understanding
The model supports various specialized tasks, including fill-in-the-middle code completion, as documented in the model's GitHub repository. It can handle both file-level and repository-level code completion tasks, making it versatile for different development scenarios.
Model Family and Variants
The Qwen 2.5 Coder family includes multiple variants optimized for different use cases and computational resources:
- 0.5B: Entry-level model with 32K token context length
- 1.5B: Lightweight variant
- 3B: Mid-range option
- 7B: The subject model, balancing capability and resource requirements
- 14B: Larger variant with enhanced capabilities
- 32B: Upcoming top-tier model
Each variant is available in both base and instruction-tuned versions, with the latter indicated by the "-Instruct" suffix. The instruction-tuned variants demonstrate enhanced performance across various benchmarks, including code reasoning (CRUXEval) and mathematical reasoning tasks.
The model is released under the Apache 2.0 license, making it accessible for both research and commercial applications. Quantized versions (AWQ, GGUF, and GPTQ) are available for more efficient deployment scenarios.
References and Resources
