qwen3-4b-dpo-qwen-cot-_2-3_05_DPO/README.md

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	peft	text-generation	dpo unsloth qwen alignment lora

qwen3-4b-dpo-qwen-cot-_2-3_05_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 reasoning (Chain-of-Thought) and structured response quality based on the provided preference dataset.

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: DPO (Direct Preference Optimization)
• Epochs: 1
• Learning rate: 1e-07
• Beta: 0.1
• Max sequence length: 1024
• LoRA Config: r=8, alpha=16

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer import torch

model_id = "your_username/qwen3-4b-dpo-qwen-cot-_2-3_05_DPO" # Replace with your username/repo

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

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.