takami2022/dpo-qwen3-4b-structured-v3_SFT_SystemPrompt

dpo-qwen3-4b-structured-v3SFTSystemPrompt

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: 2
• —Learning rate: 5e-07
• —Beta: 0.05
• —Max sequence length: 2048
• —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 = "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.