---
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
- structured-output
---

# ＜qwen3-4b-nako13-dpo-qwen-cot-merged＞

This model is a high-performance variant of **Qwen/Qwen3-4B-Instruct-2507**, optimized for precise structured data generation.
It was developed through a **two-stage fine-tuning process** to ensure both high knowledge density and strict output formatting.

## Training Process
1. **Stage 1: SFT (Supervised Fine-Tuning)**
   - **Base Model**: Qwen/Qwen3-4B-Instruct-2507
   - **Adapter**: [nakotsuko13/qwen3-4b-nako13-structured-output-lora](https://huggingface.co/nakotsuko13/qwen3-4b-nako13-structured-output-lora)
   - **Focus**: Trained on 16,500+ samples to master JSON, XML, CSV, and YAML structures.

2. **Stage 2: DPO (Direct Preference Optimization)**
   - **Dataset**: u-10bei/dpo-dataset-qwen-cot
   - **Focus**: Optimized to eliminate conversational filler and provide direct, raw structured outputs.

## Training Configuration (DPO)
- **Method**: DPO (Direct Preference Optimization)
- **Epochs**: 1
- **Learning rate**: 5e-07
- **Beta**: 0.01
- **Max sequence length**: 1024
- **LoRA Config**: r=64, alpha=128 (Merged into final weights)

## Usage
This is a **full-merged 16-bit model**. It can be used directly with standard `transformers` or `vLLM`.

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

model_id = nakotsuko13/qwen3-4b-nako13-dpo-qwen-cot-merged

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    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.