---
library_name: transformers
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B-Instruct-2507
- TakaTaka3/qwen3-4b-lora-adapter_V4
pipeline_tag: text-generation
datasets:
- u-10bei/structured_data_with_cot_dataset_512_v2
---

# TakaTaka3/Qwen3-4B-Instruct-2507-sft-merged_V2

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

This repository contains the merged model that merged **base model (Qwen/Qwen3-4B-Instruct-2507)** and **LoRA adapter weights (TakaTaka3/qwen3-4b-lora-adapter_V4)**
 .

## 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: 2048
- Epochs: 1
- Learning rate: 2e-06
- LoRA: r=64, alpha=128

## Usage

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

model_id = "TakaTaka3/Qwen3-4B-Instruct-2507-sft-merged_V2"

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

# Test inference
prompt = "Your question here"
inputs = tokenizer.apply_chat_template([{"role": "user", "content": prompt}], tokenize=True, add_generation_prompt=True, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))
```

## 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.