The authors identify three key ingredients for building a top-tier code LLM: optimized heuristic rules for data cleaning and methods for data deduplication, recall of text corpus related to code, and high-quality synthetic data in both annealing and supervised fine-tuning stages. The aim of OpenCoder is to broaden access to all aspects of a top-tier code LLM, thereby accelerating research and enabling reproducible advancements in code AI.
Key takeaways:
- The authors introduce OpenCoder, a top-tier code Large Language Model (LLM) that matches the performance of leading models and serves as an open resource for the research community.
- Unlike most previous efforts, the authors release not only the model weights and inference code, but also the reproducible training data, complete data processing pipeline, rigorous experimental ablation results, and detailed training protocols.
- The key ingredients for building a top-tier code LLM are identified as code optimized heuristic rules for data cleaning and methods for data deduplication, recall of text corpus related to code, and high-quality synthetic data in both annealing and supervised fine-tuning stages.
- The authors aim to broaden access to all aspects of a top-tier code LLM with OpenCoder, to accelerate research and enable reproducible advancements in code AI.