qwen2-5-coder-32b-instruct-84ff31a1·2 events·first seen Aliases: Qwen2.5-Coder-32B-Instruct, Qwen2.5-Coder-Instruct
Researchers propose a self-supervised mid-training objective called function-aware fill-in-the-middle (FIM) that exploits the structural isomorphism between a coding agent's action-observation-continuation loop and function call sites in ordinary code. Applied to Qwen2.5-Coder-Instruct (7B/14B) and Qwen3-8B on a 2.6B-token GitHub corpus, the method yields +2.8 to +5.4 point gains on SWE-Bench-Verified and SWE-Bench-Lite across multiple post-training pipelines. Notably, the technique also mitigates capability erosion on non-agent coding and tool-use benchmarks, suggesting the function-call inductive bias generalizes beyond the training domain.
Alibaba's Qwen team has open-sourced the Qwen2.5-Coder family of code-specialized language models, with the flagship 32B-Instruct variant claiming state-of-the-art performance among open-source code models and parity with GPT-4o on coding benchmarks. The release spans multiple model sizes, expanding on previously released smaller variants. The models are described as combining strong coding ability with general reasoning and mathematical skills.