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ricdomolm
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Model Details
Model Provider
ricdomolm
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
This example shows how to generate SWE-bench trajectories using mini-swe-agent as the agentic scaffolding (recommended) and vLLM as the local inference engine.
First, launch a vLLM server with your chosen model. For example:
bash
vllm serve ricdomolm/mini-coder-1.7b &
By default, the server will be available at http://localhost:8000.
Second, edit the mini-swe-agent SWE-bench config file located in src/minisweagent/config/extra/swebench.yaml to include your local vLLM model:
yaml
model:model_name: "hosted_vllm/ricdomolm/mini-coder-1.7b" # or hosted_vllm/path/to/local/modelmodel_kwargs:api_base: "http://localhost:8000/v1" # adjust if using a non-default port/address
Create a litellm registry.json file:
bash
cat > registry.json <<'EOF'{"ricdomolm/mini-coder-1.7b": {"max_tokens": 40960,"input_cost_per_token": 0.0,"output_cost_per_token": 0.0,"litellm_provider": "hosted_vllm","mode": "chat"}}EOF
Now you’re ready to generate trajectories! Let's solve the django__django-11099 instance of SWE-bench Verified:
bash
LITELLM_MODEL_REGISTRY_PATH=registry.json mini-extra swebench --output test/ --subset verified --split test --filter '^(django__django-11099)$'
You should now see the generated trajectory in the test/ directory.
bibtext
@article{olmedo2026computational,title={Computational Arbitrage in AI Model Markets},author={Olmedo, Ricardo and Sch{\"o}lkopf, Bernhard and Hardt, Moritz},journal={arXiv preprint arXiv:2603.22404},year={2026}}