Skywork/Skywork-SWE-32B

Hugging Face
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Jun 11, 2025License:apache-2.0Architecture:Transformer0.1K Open Weights Warm

Skywork-SWE-32B is a 32.8 billion parameter code agent model developed by Skywork AI, specifically designed for software engineering (SWE) tasks. Built on the Qwen2.5-Coder-32B-Instruct backbone, it achieves 38.0% pass@1 accuracy on the SWE-bench Verified benchmark, outperforming previous open-source models in its class. With a notable context length of 131072 tokens, this model excels at automated code generation and bug fixing within comprehensive executable runtime environments. Its performance can further improve to 47.0% accuracy with test-time scaling techniques, making it suitable for complex software development workflows.

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Skywork-SWE-32B: A Code Agent for Software Engineering

Skywork-SWE-32B, developed by Skywork AI, is a 32.8 billion parameter code agent model built upon the Qwen2.5-Coder-32B-Instruct backbone. It is specifically engineered for software engineering (SWE) tasks, demonstrating strong performance in automated code generation and bug fixing.

Key Capabilities & Performance

  • SWE-bench Verified Accuracy: Achieves 38.0% pass@1 accuracy on the SWE-bench Verified benchmark, surpassing previous open-source Qwen2.5-Coder-32B-based LLMs when integrated with the OpenHands agent framework.
  • Enhanced Performance with Test-Time Scaling: Performance further improves to 47.0% accuracy when incorporating test-time scaling techniques, setting a new state-of-the-art for sub-32B parameter models.
  • Data Scaling Law Demonstration: The model's development clearly illustrates the data scaling law phenomenon for software engineering capabilities in LLMs, indicating continued performance gains with more training data.
  • High-Quality Training Data: Trained on the Skywork-SWE dataset, a large-scale, high-quality collection featuring comprehensive executable runtime environments, gathered via an efficient and automated pipeline.

Recommended Use Cases

  • Automated Software Development: Ideal for tasks requiring automated code generation, bug fixing, and patch creation within software projects.
  • Integration with Agent Frameworks: Designed to work effectively with agent frameworks like OpenHands for complex software engineering workflows.
  • Research on LLM Scaling Laws: Useful for researchers studying the impact of data scaling on LLM performance in specialized domains like software engineering.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p