---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- Hi-Satoh/test_dpo_dataset
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- dpo
- unsloth
- qwen
- alignment
---

# ＜【課題】ここは自分で記入して下さい＞

This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.

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

## Training Objective
This model has been optimized using DPO to align its responses with preferred outputs, focusing on improving reasoning (Chain-of-Thought) and structured response quality based on the provided preference dataset.

## Training Configuration
- **Base model**: Qwen/Qwen3-4B-Instruct-2507
- **Method**: DPO (Direct Preference Optimization)
- **Epochs**: 1
- **Learning rate**: 2e-07
- **Beta**: 0.1
- **Max sequence length**: 4096
- **LoRA Config**: r=8, alpha=16 (merged into base)

## Usage

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

model_id = "Hi-Satoh/adv_sft_dpo_final_12_merged"

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

## Sources & License (IMPORTANT)

* **Training Data**: [Hi-Satoh/test_dpo_dataset]
* **License**: MIT License. (As per dataset terms).
* **Compliance**: Users must follow the original base model's license terms.