---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
  - reinforcement-learning
  - ppo
  - lora
  - code-generation
  - clarification
license: apache-2.0
---

# rl-clarify-orig-prompt-d1-1p5

LoRA fine-tune of [Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
trained with PPO-Lagrangian constrained RL on [HumanEvalComm](https://huggingface.co/datasets/jie-jw-wu/HumanEvalComm).

## Training setup
- **Algorithm:** PPO with Lagrangian constraint on avg questions per episode
- **LoRA rank:** 16, alpha 32
- **Question budget (d1):** 1.5
- **Iterations:** 80
- **Checkpoint dir:** checkpoints/orig_prompt/d1_1.5

## Eval results (selected checkpoint: `iter_0049`)
- **Final eval pass@1:** 0.759 (417 problems, greedy decoding)
- **Final eval avg questions:** 0.8

## Checkpoints
Each `iter_XXXX/` folder contains LoRA adapter weights and a `log.json`
with per-iteration training metrics (avg_reward, avg_questions, lambda1, lambda2, kl_per_seq).

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

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base, "acv1229/rl-clarify-orig-prompt-d1-1p5", subfolder="iter_0049")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
```