---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_512_v2
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- structured-output
- qwen
- qlora
- lora
---

# qwen3-4b-struct-exp77

This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.

This repository contains the **full merged 16-bit weights**.
No adapter loading is required.

## Training Objective

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

## Training Configuration

- Base model: Qwen/Qwen3-4B-Instruct-2507
- Dataset: u-10bei/structured_data_with_cot_dataset_512_v2
- Method: QLoRA (4-bit)
- Max sequence length: 512
- Epochs: 3
- Learning rate: 1e-06
- LoRA: r=128, alpha=256

## Usage

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

model_id = "curio184/qwen3-4b-struct-exp77"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)
```

## Sources & Terms (IMPORTANT)

Training data: u-10bei/structured_data_with_cot_dataset_512_v2

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.