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base_model: unsloth/Qwen3-4B-Instruct-2507
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-4B-Instruct-2507
- grpo
- lora
- transformers
- trl
- unsloth
- swe-gym
- code-repair
---
# Qwen3-4B SWE-Gym Moto KL02 Multi-Hint GRPO Step-25 Adapter
This is a PEFT LoRA adapter for `unsloth/Qwen3-4B-Instruct-2507`, trained for agentic code repair on the local SWE-Gym moto held-out investigation using a search/replace patch format and honest anchored retrieval.
This checkpoint is a short GRPO continuation from the stronger KL02 adapter with the structural multi-file prompt hint enabled during training. It is an ablation artifact, not the best Qwen3-4B checkpoint from the investigation.
Local source checkpoint:
`/mnt/disks/unslothai/datta0/cache/qwen3-grpo-patch/20260605_005347_swegym_q4b-kl02-multihint-grpo-b02-lr2e6-s25_c1a36f8/checkpoints/checkpoint-25`
## Training
- Base model: `unsloth/Qwen3-4B-Instruct-2507`
- Initial adapter: `imdatta0/qwen3-4b-swegym-moto-kl02-adapter`
- Prompt mode: structural multi-file search/replace hint enabled
- Objective: GRPO
- Beta: `0.02`
- Learning rate: `2e-6`
- Steps: `25`
- Eval split: local SWE-Gym moto held-out, 35 tasks
## Held-Out Result
| run | greedy | mean reward | patch applied |
|---|---:|---:|---:|
| KL02 + prompt hint, step 0 baseline | 9/35 | 0.4563 | 0.8571 |
| GRPO continuation, step 25 | 8/35 | 0.4234 | 0.8286 |
The continuation was stable but negative on the held-out greedy metric. It regressed from the inherited step-0 baseline, so no pass@8 evaluation was promoted for this checkpoint.
The stronger artifact for normal use is:
`imdatta0/qwen3-4b-swegym-moto-kl02-adapter`
Use that adapter with the structural multi-file prompt hint at runtime for the best measured deterministic behavior from this branch.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base = "unsloth/Qwen3-4B-Instruct-2507"
adapter = "imdatta0/qwen3-4b-swegym-moto-kl02-multihint-grpo-b02-lr2e6-s25-adapter"
tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(base)
model = PeftModel.from_pretrained(model, adapter)
```
## Limitations
- This adapter requires the base model and is not a merged full model.
- This is a research checkpoint for SWE-Gym style code repair, not a general coding assistant release.
- It was evaluated only on the local SWE-Gym moto held-out split used in this investigation.
- The step-25 continuation is not a frontier checkpoint; it regressed relative to the inherited KL02 prompt-hint baseline.
- Metrics depend on the repository's retrieval, prompt, search/replace extraction, patch application, and sandbox scoring code.
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