README.md

---
library_name: transformers
tags:
- agent
- code
license: mit
datasets:
- ricdomolm/mini-coder-trajs-400k
base_model:
- Qwen/Qwen3-1.7B
---

# mini-coder-1.7b

`mini-coder-1.7b` is a 1.7B parameter model distilled from Qwen 3 Coder 30B A3B. It punches well above its weight, outperforming SWE-agent-LM 7B on [SWE-bench Verified Bash only](https://www.swebench.com/):

<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.