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 (SFT Tuned)
Method: DPO (Direct Preference Optimization)
Epochs: 1
Learning rate: 1e-7
Beta: 0.1
Max sequence length: 2048
Hardware: RTX 4070 Super (BF16 enabled)

Usage

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

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "takenoko888/dpo-qwen-cot-merged"

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

Dataset: [u-10bei/dpo-dataset-qwen-cot]
License: Apache 2.0 (Derived from Qwen)