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dsl-debug-7b-sft-step100/README.md

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---
license: mit
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- debugging
- tool-use
- multi-turn
- sft
datasets:
- custom
language:
- en
pipeline_tag: text-generation
---
# DSL Debug 7B — SFT Step 100
Qwen2.5-7B-Instruct fine-tuned on 1,593 debugging trajectories for the DSL Debug environment.
**Blog post:** [Multi-Turn RL for Code Debugging](https://andrewlngdn.github.io/dsl_debugger/)
**Code + environment:** [github.com/AndrewLngdn/dsl-debug](https://github.com/AndrewLngdn/dsl-debug)
## Training
- **Method**: Supervised fine-tuning (verl 0.7)
- **Data**: 1,593 multi-turn trajectories with tool calls (run, inspect, read_docs, submit)
- **Base model**: Qwen2.5-7B-Instruct
- **Epochs**: 2 (step 100 checkpoint)
- **LR**: 5e-6
- **Hardware**: 2x A100-SXM4-80GB
## Results (held-out test, one-shot)
| Split | Base Model | This Model |
|-------|:---:|:---:|
| Standard (481) | 50.5% | **56.3%** |
| Nonlocal (200) | 12.0% | **40.0%** |
| Intent-Mismatch (177) | 0.6% | **7.9%** |
## Alignment Tax
| Benchmark | Base | This Model |
|-----------|:---:|:---:|
| MMLU (5-shot) | 74.6% | 74.6% |
| GSM8K (8-shot) | 84.9% | 83.9% |
| HumanEval (0-shot) | 65.9% | 62.2% |
## Usage
This checkpoint is primarily used as the starting point for SFT then RL training (GRPO), which achieves the best results.
```python
from huggingface_hub import snapshot_download
snapshot_download("andrewlngdn/dsl-debug-7b-sft-step100",
local_dir="/workspace/models/sft_7b_step100")
```
## Related Models
| Model | Repo |
|-------|------|
| **SFT then RL step 35 (best)** | [andrewlngdn/dsl-debug-7b-sft-rl](https://huggingface.co/andrewlngdn/dsl-debug-7b-sft-rl) |
| RL-only step 30 | [andrewlngdn/dsl-debug-7b-rl-only-step30](https://huggingface.co/andrewlngdn/dsl-debug-7b-rl-only-step30) |