README.md

---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
library_name: peft
pipeline_tag: text-generation
tags:
- lora
- grpo
- trl
- unsloth
- quantum-error-correction
license: mit
---

# QuantumScribe (GRPO LoRA)

**LoRA adapter** fine-tuned with **GRPO** for logical quantum error correction, on top of **base** [`unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit`](https://huggingface.co/unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit).

## Adapter

- LoRA `r=16`, `lora_alpha=32`, `lora_dropout=0.1`
- Target: `q_proj`, `k_proj`, `v_proj`, `o_proj` (PEFT 0.18.1)

## Training

- **W&B:** [ronitraj/QuantumScribe-GRPO](https://wandb.ai/ronitraj/QuantumScribe-GRPO) (e.g. run `4p7eurnc`)
- ~1500 GRPO steps; SFT warm-up as in the project `scripts/train_grpo.py`

## Eval (from project `data/eval_grpo.json`)

- **Logical correction rate** high (~0.96 on the recorded run)
- **pymatching_beat** reported at 0 on the evaluated split — align narrative and metrics (continuous vs threshold) with your harness and README

## Load

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit"
adapter_id = "ronitraj/quantumscribe"
tokenizer = AutoTokenizer.from_pretrained(adapter_id)
model = AutoModelForCausalLM.from_pretrained(
    base_id, device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter_id)
```