--- license: apache-2.0 datasets: - nebius/SWE-rebench-openhands-trajectories base_model: - Qwen/Qwen3-30B-A3B-Instruct-2507 pipeline_tag: text-generation library_name: transformers tags: - code - agent --- # Model Summary **SWE-rebench-openhands-Qwen3-30B-A3B** is a 30B Rejection Sampling Fine-Tuning (RFT) checkpoint derived from [Qwen/Qwen3-30B-A3B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-30B-A3B-Instruct-2507), trained on the newly released [nebius/SWE-rebench-openhands-trajectories](https://huggingface.co/datasets/nebius/SWE-rebench-openhands-trajectories) dataset. Training used a maximum sequence length of 131k tokens.
Model Size Maximum Number of Turns = 100 Maximum Number of Turns = 500
Pass@1 Pass@5 Pass@1 Pass@5 Pass@1 Pass@5 Pass@1 Pass@5
30B scale
Qwen3-30B-A3B-Instruct-2507 30B 25.2 44.8 11.8 24.4 25.7 44.2 14.2 26.5
Qwen3-Coder-30B-A3B-Instruct 30B 51.9 67.3 28.7 42.8 50.0 63.0 28.1 38.7
nebius/SWE-rebench-openhands-Qwen3-30B-A3B (Ours) 30B 49.7
(+24.5)		 65.4
(+20.6)		 28.1
(+16.3)		 38.7
(+14.3)		 50.3
(+24.6)		 68.3
(+24.1)		 28.1
(+13.9)		 38.7
(+12.2)
100B+ scale
GLM-4.5-Air 106B 58.2 73.5 33.8 42.8 - - - -
200B+ scale
Qwen3-235B-A22B-Instruct-2507 235B 45.2 65.9 29.3 44.8 46.2 67.5 25.3 40.8
nebius/SWE-rebench-openhands-Qwen3-235B-A22B (Ours) 235B 59.9
(+14.7)		 73.9
(+8.0)		 35.1
(+5.8)		 46.9
(+2.1)		 61.7
(+15.5)		 74.3
(+6.8)		 34.2
(+8.9)		 44.8
(+4.0)
300B+ scale
GLM-4.5 355B 64.4 76.2 33.8 44.8 - - - -
Qwen3-Coder-480B-A35B-Instruct 480B 64.7 75.8 36.3 44.8 66.5 77.8 35.5 42.8
**Table 1.** Pass@1 (averaged over 5 runs) and Pass@5 for OpenHands agent with the maximum number of turns set to 100 (highlighted in yellow) and 500 (highlighted in green). Metrics are reported in percentages. Deltas vs base models are shown in parentheses for fine-tuned models. We explicitly excluded all [SWE-bench Verified](https://huggingface.co/datasets/princeton-nlp/SWE-bench_Verified) and [SWE-rebench September](https://huggingface.co/datasets/nebius/SWE-rebench-leaderboard) issues from training to avoid contamination. SWE-rebench Verified was additionally decontaminated on repository level. When evaluated with the OpenHands (v0.54.0) agent, our 30B model: * Substantially improves over the base Qwen3-30B-A3B-Instruct-2507 model, with **+24.6 Pass@1** and **+24.1 Pass@5** gains at 500-turn settings. * Matches or surpasses the specialized Qwen3-Coder-30B-A3B-Instruct baseline at 500 turns on SWE-bench Verified (**50.3%** vs **50.0% Pass@1**; **68.3%** vs **63.0% Pass@5**). * Generalizes to longer interaction horizons, despite training on trajectories capped at 100 turns. For more details see our report in [Nebius blog](https://nebius.com/blog/posts/openhands-trajectories-with-qwen3-coder-480b). --- # Best Practices 1. **Deployment:** * Use the following configuration to serve the model with vLLM: ```bash VLLM_USE_V1=1 vllm serve nebius/SWE-rebench-openhands-Qwen3-30B-A3B --tensor-parallel-size 8 --served-model-name qwen_3_instruct_2507 --disable-log-requests --enable-prefix-caching --max-model-len 131072 --enable-auto-tool-choice --tool-call-parser hermes ``` Tested using `vllm/vllm-openai:v0.9.0` Docker image. 2. **Sampling Parameters:** * For optimal performance, we recommend `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0` that are consistent with the base model. --- # Citation ``` @article{trofimova2025openhandstrajs, title={OpenHands Trajectories with Qwen3-Coder-480B-A35B-Instruct}, author={Trofimova, Maria and Shevtsov, Anton and Ibragim, Badertdinov and Pyaev, Konstantin and Karasik, Simon and Golubev, Alexander}, year={2025}, journal={Nebius blog}, note={} } ```