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

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

dpo-qwen3_4b-cot-merged_v260302-010243

This model is a fine-tuned version of unsloth/Qwen3-4B-Instruct-2507 using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) via the Unsloth library.

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

Training Objective

In SFT, an adapter is trained to improve structured output accuracy (JSON / YAML / XML / TOML / CSV). Loss is applied only to the final assistant output, while intermediate reasoning (Chain-of-Thought) is masked.

After SFT, the adapter is merged to the base model. 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: unsloth/Qwen3-4B-Instruct-2507
• Method: SFT(Supervised Fine-Tuning) + DPO (Direct Preference Optimization)

SFT Training Configuration

• Max sequence length: 768
• Epochs: 2
• Learning rate: 5e-06
• LoRA Config: QLoRA(4-bit), r=128, alpha=128

DPO Training Configuration

• Epochs: 5
• Learning rate: 1e-06
• Beta: 0.2
• Max sequence length: 768
• LoRA Config: r=64, alpha=64 (merged into base)
• Early Drop: threshold=1.2

Usage

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

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your_id/your-repo-name"

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.