Introduction to Qwen2.5-Coder Series

This blog post introduces the open-source Qwen2.5-Coder series, a collection of code-generation models designed for diverse applications and boasting state-of-the-art performance.

Performance

The flagship model, Qwen2.5-Coder-32B-Instruct, achieves state-of-the-art results among open-source models on benchmarks like EvalPlus, LiveCodeBench, and BigCodeBench, rivaling even proprietary models like GPT-4o in code generation, repair, and reasoning across a wide array of programming languages.

Model Variety

The Qwen2.5-Coder series offers a range of model sizes (0.5B, 1.5B, 3B, 7B, 14B, and 32B parameters) to accommodate different resource constraints and research needs. Both base and instruction-tuned versions are available for each size. Scaling Law experiments demonstrate a positive correlation between model size and performance.

Practical Applications

The models are showcased in practical scenarios like code assistants (similar to Cursor) and artifact generation (using Open WebUI), highlighting their real-world utility. A code mode on the Tongyi platform is planned for simplified website, mini-game, and data chart creation.

Open Source Availability

Most models in the series are released under the Apache 2.0 license, promoting accessibility and community involvement.

Conclusion

The Qwen2.5-Coder series represents a significant advancement in open-source code generation models, offering competitive performance, a variety of model sizes, and practical applicability. Future work will focus on enhancing code-centric reasoning capabilities.

Source(s):

• Qwen2.5-Coder Technical Report
• Qwen2 technical report