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base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/structured_data_with_cot_dataset_512
- u-10bei/structured_data_with_cot_dataset_512_v2
- u-10bei/structured_data_with_cot_dataset_512_v4
- u-10bei/structured_data_with_cot_dataset_512_v5
- u-10bei/structured_data_with_cot_dataset_v2
language:
- en
license: apache-2.0
library_name: peft
pipeline_tag: text-generation
tags:
- qlora
- lora
- structured-output
---
# Qwen3-4B_R_64_ALPHA_128 for Structured Output
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.
## Training Objective
This adapter is trained to improve **structured output accuracy**
(JSON / YAML / XML / TOML / CSV).
Loss is applied only to the final assistant output,
while intermediate reasoning (Chain-of-Thought) is masked.
## Training Configuration
- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: QLoRA (4-bit)
- Max sequence length: 512
- Epochs: 1
- Learning rate: 1e-06
- LoRA: r=80, alpha=160
## 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)
```
## Sources & Terms (IMPORTANT)
Training data: ['u-10bei/structured_data_with_cot_dataset_512', 'u-10bei/structured_data_with_cot_dataset_512_v2', 'u-10bei/structured_data_with_cot_dataset_512_v4', 'u-10bei/structured_data_with_cot_dataset_512_v5', 'u-10bei/structured_data_with_cot_dataset_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.
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