| --- |
| license: mit |
| base_model: Qwen/Qwen2.5-Coder-7B-Instruct |
| library_name: peft |
| datasets: |
| - TIGER-Lab/StopLoss-Training-Data |
| language: |
| - en |
| tags: |
| - lora |
| - agents |
| - coding-agents |
| - early-stopping |
| --- |
| |
| # StopLoss-Qwen2.5-Coder-7B-Instruct |
|
|
| A LoRA adapter for [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) that predicts whether a coding agent's run will fail. |
| It is the Qwen2.5-Coder-7B-Instruct StopLoss monitor from [StopLoss: Teaching Agents to Predict Failure and Stop Early](https://arxiv.org/abs/2610.25525). |
|
|
| [🌐 Homepage](https://tiger-ai-lab.github.io/StopLoss/) | [📄 Paper](https://arxiv.org/abs/2610.25525) | [💻 Code](https://github.com/TIGER-AI-Lab/StopLoss) | [🤗 Training data](https://huggingface.co/datasets/TIGER-Lab/StopLoss-Training-Data) |
|
|
| ## What it does |
|
|
| At tool steps 5, 8, 11, ..., the monitor reads the step count and the three most recent action–observation pairs of |
| a run. Its failure score is the probability of `ABORT` among the labels `CONTINUE`, `DELIVER` and `ABORT`, read from |
| the next-token logits in one forward pass. A run is stopped once the score reaches a threshold calibrated on |
| successful runs. |
|
|
| ## Usage |
|
|
| ```bash |
| git clone https://github.com/TIGER-AI-Lab/StopLoss.git && cd StopLoss |
| pip install -e ".[model]" |
| python scripts/build_windows.py --runs my_runs.jsonl --out windows.jsonl --ks checkpoints |
| python scripts/score_windows.py --base Qwen/Qwen2.5-Coder-7B-Instruct \ |
| --adapter TIGER-Lab/StopLoss-Qwen2.5-Coder-7B-Instruct \ |
| --windows windows.jsonl --out scores.jsonl |
| ``` |
|
|
| This adapter is seed 0 of its backbone; the results in the paper use an ensemble of four seeds. |
|
|
