Abstract
In this report, we introduce the Qwen2.5-Coder series, a significant upgrade from its predecessor, CodeQwen1.5. This series includes two models: Qwen2.5-Coder-1.5B and Qwen2.5-Coder-7B. As a code-specific model, Qwen2.5-Coder is built upon the Qwen2.5 architecture and continues pretrained on a vast corpus of over 5.5 trillion tokens. Through meticulous data cleaning, scalable synthetic data generation, and balanced data mixing, Qwen2.5-Coder demonstrates impressive code generation capabilities while retaining general versatility. The model has been evaluated on a wide range of code-related tasks, achieving state-of-the-art (SOTA) performance across more than 10 benchmarks, including code generation, completion, reasoning, and repair, consistently outperforming larger models of the same model size. We believe that the release of the Qwen2.5-Coder series will not only push the boundaries of research in code intelligence but also, through its permissive licensing, encourage broader adoption by developers in real-world applications.
Community
Qwen2.5 Technical Report
@huybery
Congrats on the release of Qwen 2.5 coderπ₯
It would be great if you could link the models to this page by adding arxiv.org/abs/2409.12186 in a model README.md file.
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- To Code, or Not To Code? Exploring Impact of Code in Pre-training (2024)
- XMainframe: A Large Language Model for Mainframe Modernization (2024)
- OriGen:Enhancing RTL Code Generation with Code-to-Code Augmentation and Self-Reflection (2024)
- CodeACT: Code Adaptive Compute-efficient Tuning Framework for Code LLMs (2024)
- DOMAINEVAL: An Auto-Constructed Benchmark for Multi-Domain Code Generation (2024)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
"outperforming larger models of the same model size" -- how do I interpret this phrase in the abstract?
Coffee vending recipe code in python
Models citing this paper 153
Browse 153 models citing this paperDatasets citing this paper 0
No dataset linking this paper