ricdomolm
/

mini-coder-4b

@@ -10,197 +10,68 @@ base_model:
 - Qwen/Qwen3-4B-Instruct-2507
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 - Qwen/Qwen3-4B-Instruct-2507
 ---
+# mini-coder-4b
+`mini-coder-4b` is a 4B parameter model distilled from Qwen 3 Coder 30B A3B. It punches well above its weight, outperforming gpt-oss-120b on SWE-bench Verified Bash only:
+<div align="center">
+| Model                  | pass@1 | pass@100 |
+|-------------------------|--------|----------|
+| Qwen 3 Coder 30B-A3B    | 33.2   | 67.4     |
+| mini-swe-4b             | 26.8   | 60.2     |
+| gpt-oss-120b            | 26.0   | –        |
+| mini-swe-1.7b           | 18.6   | 50.4     |
+| SWE-agent-LM 7B         | 15.2   | –        |
+| Qwen 3 4B Instruct 2507 | 4.0    | 25.1     |
+</div>
+It is trained on 400k training trajectories using the lightweight [mini-swe-agent](https://mini-swe-agent.com/latest/) scaffolding and the [SWE-smith](https://huggingface.co/datasets/SWE-bench/SWE-smith) dataset of GitHub issues.
+Unlike existing agentic SWE models, the `mini-coder` models can be post-trained on a single 80GB GPU—or smaller. They work seamlessly with mini-swe-agent, a lightweight, scalable, and developer-friendly agentic framework well-suited for RL fine-tuning. And because they are dense rather than MoE models, they benefit from a more mature fine-tuning ecosystem.
+## 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](https://mini-swe-agent.com/latest/) as the agentic scaffolding (recommended) and [vLLM](https://docs.vllm.ai/en/latest/) 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/model
+  model_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.