swegym-qwen2.5-coder-32b-instruct-sft-64k

SWE-Gym Qwen2.5-Coder-32B-Instruct Full SFT (64K Context)

A full-parameter supervised fine-tuned version of Qwen2.5-Coder-32B-Instruct using SWE-agent trajectory data distilled from Qwen3-Coder-480B-A35B-Instruct on the SWE-Gym dataset.

Model Details

Training Details

Training Data

The training data consists of 634 resolved instances from the SWE-Gym training set. Trajectories were generated by running Qwen3-Coder-480B-A35B-Instruct (via OpenHands CodeActAgent with maxiter=100) on SWE-Gym tasks, then filtering to only resolved (successful) trajectories. Function-calling messages were converted to non-function-calling format for SFT, and trajectories exceeding 64K tokens were excluded.

Training Curve

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "MMR115/swegym-qwen2.5-coder-32b-instruct-sft-64k",
    torch_dtype="auto",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("MMR115/swegym-qwen2.5-coder-32b-instruct-sft-64k")

Citation

If you use this model, please cite SWE-Gym and OpenHands:

@article{pan2024swegym,
  title={Training Software Engineering Agents and Verifiers with SWE-Gym},
  author={Pan, Jiayi and Xiao, Xingyao and Wang, Jinda and Graham, Colin and Wang, Xinran and Hu, Hoang and Wang, Rui and Shi, Heng and Liu, Pengfei and Wang, Huan and Qian, Cong},
  journal={ICML},
  year={2025}
}