Example usage: Generating SWE-bench trajectories with mini-swe-agent and vLLM

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
Copy code
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
Copy code
model:
  model_name: "hosted_vllm/ricdomolm/mini-coder-1.7b"  # or hosted_vllm/path/to/local/model
  model_kwargs:
    api_base: "http://localhost:8000/v1"  # adjust if using a non-default port/address

Create a litellm registry.json file:

bash
Copy code
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
Copy code
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.