---
base_model: Qwen/Qwen2.5-Coder-1.5B
tags:
  - lora
  - sft
  - code
  - python
  - instruction-tuning
license: apache-2.0
---

# Track B SFT – Qwen2.5-Coder-1.5B + LoRA

Fine-tuned on ~250 synthetic coding instruction pairs generated from the [verl](https://github.com/volcengine/verl) corpus.

## Results

| Metric | Baseline | Post-SFT | Δ |
|--------|----------|----------|---|
| pass@1 | 0.565 | **0.804** | +0.239 |
| pass@3 | 0.783 | 0.848 | +0.065 |

## Training

- **Base model:** `Qwen/Qwen2.5-Coder-1.5B`
- **Method:** LoRA (r=16, alpha=32)
- **Data:** `archit11/track_b_sft` (~257 train examples)
- **Epochs:** 3, **LR:** 2e-4, **Hardware:** T4 GPU

## Usage

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

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-1.5B")
model = PeftModel.from_pretrained(base, "archit11/track_b_sft_model").merge_and_unload()
tokenizer = AutoTokenizer.from_pretrained("archit11/track_b_sft_model")
```