---
base_model: Qwen/Qwen3-4B-Instruct-2507
adapter: ykawasaki/qwen3-4b-structured-output-lora-v12
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
---

# ＜qwen3-4b-dpo-qwen-cot-merged-v7＞

This model is a fine-tuned version of **Qwen/Qwen3-4B-Instruct-2507** using **Direct Preference Optimization (DPO)** via the **Unsloth** library.
The basemodel was merged with an adapter **ykawasaki/qwen3-4b-structured-output-lora-v12** beforehand.

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
- **Adapter**: ykawasaki/qwen3-4b-structured-output-lora-v12
- **Method**: DPO (Direct Preference Optimization)
- **Epochs**: 3
- **Learning rate**: 1e-07
- **Beta**: 0.1
- **Max sequence length**: 1024
- **LoRA Config**: r=16, alpha=32 (merged into base)

- **LoRA dropout**: 0.05
- **rsLoRA**: False
- **DoRA**: True
- **Per device batch size**: 16
- **Gradient accumulation**: 8
- **Learning rate scheduler**: SchedulerType.COSINE

## Usage
Since this is a merged model, you can use it directly with `transformers`.

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

model_id = "ykawasaki/qwen3-4b-dpo-qwen-cot-merged-v7"

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 & License (IMPORTANT)

* **Training Data**: [u-10bei/dpo-dataset-qwen-cot]
* **License**: MIT License. (As per dataset terms).
* **Compliance**: Users must follow the original base model's license terms.