---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- daichira/structured-hard-sft-4k
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- structured-output
- supervised-finetuning
- assistant-only-loss
---

This repository provides a **LoRA adapter** fine-tuned from  
**Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.

This repository contains **LoRA adapter weights only**.  
The base model must be loaded separately.

---

## Overview

This adapter is designed to improve **structured output reliability**  
across formats such as:

- JSON
- YAML
- XML
- TOML
- CSV

The focus is on increasing formatting stability, bracket correctness,
key-value alignment, and structural consistency.

---

## Training Objective

The model is trained via **Supervised Fine-Tuning (SFT)** with the following design:

- Loss is applied only to the **final assistant output**
- Intermediate reasoning (Chain-of-Thought) is masked
- Training emphasizes output formatting precision

This setup prioritizes structured output correctness over verbose reasoning.

---

## Training Configuration

- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: QLoRA (4-bit)
- Max sequence length: 1024
- Epochs: 3
- Learning rate: 2e-05
- LoRA configuration:
  - r = 128
  - alpha = 256

---

## Usage

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

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "your_id/your-repo"

tokenizer = AutoTokenizer.from_pretrained(base)

model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
)

model = PeftModel.from_pretrained(model, adapter)
model.eval()
```

## Sources & Terms (IMPORTANT)

Training data: daichira/structured-hard-sft-4k

Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.