metadata
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.
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:
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
- License: MIT License (as per dataset terms)
- Compliance: Users must follow the original base model's license terms.