---
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 StructEval DPO v1 (Base + DPO)

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 structured output quality (JSON, YAML, XML, TOML, CSV).

## Training Configuration

- **Base model**: Qwen/Qwen3-4B-Instruct-2507
- **SFT Adapter**: None (direct DPO from base)
- **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

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

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

model_id = "sonodd/qwen3-4b-structeval-dpo-v1-base"

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

## Inference with Standard Code 2

For inference using the competition's standard code 2, set:
```python
MODEL_SOURCE = "merged"
MERGED_MODEL_ID_OR_PATH = "sonodd/qwen3-4b-structeval-dpo-v1-base"
```

## Sources & License (IMPORTANT)

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