Qwen2.5-Coder Technical Report
원문 초록
In this report, we introduce the Qwen2.5-Coder series, a significant upgrade from its predecessor, CodeQwen1.5. This series includes six models: Qwen2.5-Coder-(0.5B/1.5B/3B/7B/14B/32B). 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 and math skills. These models have 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 advance research in code intelligence and, with its permissive licensing, support wider adoption by developers in real-world applications.
함께 살펴볼 논문
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EvoAgentX: An Automated Framework for Evolving Agentic Workflows
ViCrit: A Verifiable Reinforcement Learning Proxy Task for Visual Perception in VLMs
LLM Performance for Code Generation on Noisy Tasks
Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
Where to find Grokking in LLM Pretraining? Monitor Memorization-to-Generalization without Test
Xolver: Multi-Agent Reasoning with Holistic Experience Learning Just Like an Olympiad Team
토론
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