qwen2-5-coder-45ec51c1·4 events·first seen Aliases: Qwen2.5-Coder, Qwen2.5-Coder-0.5B, Qwen2.5-Coder-7B
A new arXiv preprint investigates whether independently trained code language models converge on the same internal representations, using a 2x2 factorial design crossing Python/Rust with Qwen2.5-Coder-7B and DeepSeek-Coder-V1-6.7B. The study measures 58 Python and 57 Rust grammatical concepts via concept-circuit extraction across all four conditions. Key findings: which concepts earn dedicated circuits is consistent across models (task-determined), but where and how those circuits are organized differs substantially by model — Qwen processes concepts in late layers (~L17-19), DeepSeek in early layers (L6-7). The work reframes the 'circuit universality' question, concluding universality holds for representational content but not computational organization.
A pre-registered two-tier ablation study tests whether 'Popperian falsificationist' prompt skills improve LLM code generation through their procedural content or merely through structural scaffolding. Using Claude Sonnet 4.6 and Qwen2.5-Coder-0.5B with execution-based evaluation (HumanEval+ unit tests) rather than LLM-as-judge, the authors find that on the small model, structured prompts lift correctness by 20-22 points but the full Popperian skill shows no separable benefit over a labels-only scaffold. The paper contributes a calibrated negative result and a reusable disambiguation protocol for evaluating prompt-skill families, while also documenting that LLM self-judges at 0.5B scale perform no better than random selection.
Alibaba's Qwen team has released Qwen2.5-Coder, the next generation of their open-source coding-specialized language model, succeeding CodeQwen1.5 which launched in April 2024. The release also marks a rebranding from CodeQwen to Qwen-Coder. The model is available on GitHub, Hugging Face, and ModelScope.
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.