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 + DPO (Direct Preference Optimization)
SFT Epochs: 1
SFT LORA_R: 16
SFT LORA_ALPHA: 32
SFT LORA_DROPOUT: 0.05
SFT Learning Rate: 2e-5
SFT use_dora: True
DPO Epochs: 1
DPO Learning rate: 1e-06
DPO Beta: 0.1
DPO Max sequence length: 2048
DPO LoRA Config: r=8, alpha=16 (merged into base)

Usage

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

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "migaku4649/dpo-lora-model"

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 Training Data: [u-10bei/structured_data_with_cot_dataset_512_v2]
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.

migaku4649/dpo_lora_model

unsloth-4b-dpo-v1

Training Objective

Training Configuration

Usage

Sources & License (IMPORTANT)

migaku4649/
dpo_lora_model