---
base_model: Qwen/Qwen3-4B-Instruct-2507
datasets:
- u-10bei/dpo-dataset-qwen-cot
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 Configuration
- Base model: Qwen/Qwen3-4B-Instruct-2507
- Method: DPO (Direct Preference Optimization)
- Epochs: 1
- Learning rate: 1e-07
- Beta: 0.1
- Max sequence length: 1024
- LoRA Config: r=8, alpha=16 (merged into base)

## Usage

    from transformers import AutoModelForCausalLM, AutoTokenizer
    import torch

    model_id = "your_id/your-repo-name"

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

    prompt = "Your question here"

    messages = [
        {"role": "user", "content": prompt}
    ]

    input_ids = tokenizer.apply_chat_template(
        messages,
        tokenize=True,
        add_generation_prompt=True,
        return_tensors="pt",
    )

    input_ids = input_ids.to(model.device)

    outputs = model.generate(
        input_ids=input_ids,
        max_new_tokens=512,
    )

    print(tokenizer.decode(outputs[0], skip_special_tokens=True))

## Sources & License

* Training Data: [u-10bei/dpo-dataset-qwen-cot]
* License: MIT License (as per dataset terms)
* Compliance: Follow base model license terms