OpenCoder Series

OpenCoder is an open and reproducible code LLM family which includes 1.5B and 8B base and chat models, supporting both English and Chinese languages. Starting from scratch, OpenCoder is trained on 2.5 trillion tokens composed of 90% raw code and 10% code-related web data, reaching the performance of top-tier code LLMs. We provide not only model weights and inference code, but also the reproducible training data, the complete data processing pipeline, rigorous experimental ablation results, and detailed training protocols. Empowering researchers to build and innovate, OpenCoder is your open foundation for advancing code AI.

State of the art code LLM that beats Qwen2.5-Coder of equivalent size.

https://opencoder-llm.github.io/
https://arxiv.org/pdf/2411.04905

https://huggingface.co/infly/OpenCoder-1.5B
https://huggingface.co/infly/OpenCoder-1.5B-Instruct
https://huggingface.co/infly/OpenCoder-8B
https://huggingface.co/infly/OpenCoder-8B-Instruct

Read the OpenCoder project page, paper, and Hugging Face model links first to understand the proposed model family. The issue does not name repository files, tests, an entry point, or acceptance criteria, so the intended integration and definition of done need to be clarified before implementation.