base_model, datasets, language, license, library_name, pipeline_tag, tags

base_model	datasets	language	license	library_name	pipeline_tag	tags
Qwen/Qwen3-4B-Instruct-2507	u-10bei/dpo-dataset-qwen-cot	en	apache-2.0	transformers	text-generation	dpo unsloth qwen alignment two-stage-training

qwen3-4b-2507-dpo-cot-merged

This model is the result of two-stage fine-tuning:

1. SFT (Supervised Fine-Tuning): Training on structured output datasets
2. DPO (Direct Preference Optimization): Alignment using preference data

This repository contains the full-merged 16-bit weights. No adapter loading is required.

Training Pipeline

Stage 1: SFT

• Base model: Qwen/Qwen3-4B-Instruct-2507
• SFT adapter: masachika/qwen3-4b-Instruct-2507-structured-output-lora
• Objective: Learn to generate structured outputs (JSON, YAML, XML, TOML, CSV)

Stage 2: DPO (This model)

• Starting point: SFT-trained model (merged)
• Method: Direct Preference Optimization
• Dataset: u-10bei/dpo-dataset-qwen-cot
• Objective: Align responses with preferred outputs, improve reasoning quality

Training Configuration (DPO Stage)

• Epochs: 2
• Learning rate: 3e-07
• Beta: 0.2
• Max sequence length: 2048
• LoRA Config: r=64, alpha=128 (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 = "masachika/qwen3-4b-dpo-cot-merged"

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)

• SFT Base: masachika/qwen3-4b-Instruct-2507-structured-output-lora
• DPO 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