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: 1e-06
Beta: 0.05
Max sequence length: 4096
LoRA Config: r=8, alpha=16 (merged into base)

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Hi-Satoh/qwen3-test2-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

Sources & License (IMPORTANT)

Training Data: [Hi-Satoh/test_dpo_dataset]
License: MIT License. (As per dataset terms).
Compliance: Users must follow the original base model's license terms.