README.md

---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_512_v5
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- full-finetune
- structured-output
---

qwen3-4b-structured-output-lora

This repository provides a **full fine-tuned model** based on
**Qwen/Qwen3-4B-Instruct-2507** using **BF16 full fine-tuning + NEFTune**.

This repository contains the **complete model weights**.

## Training Objective

This model is trained to improve **structured output accuracy**
(JSON / YAML / XML / TOML / CSV).

CoT (Chain-of-Thought) is removed from training data during preprocessing.

## Training Configuration

- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: Full fine-tuning (BF16)
- Max sequence length: 2048
- Epochs: 1
- Learning rate: 2e-05
- NEFTune noise alpha: 5.0

## Usage

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

model_id = "84basi/lora-10-2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
```

## Sources & Terms (IMPORTANT)

Training data: u-10bei/structured_data_with_cot_dataset_512_v5

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.