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.

Note: This model was fine-tuned from a pre-trained SFT LoRA (q-hisa/qwen3-4b-structeval-sft-lora-v3), then further optimized with DPO.

Training Configuration

Base model: Qwen/Qwen3-4B-Instruct-2507
SFT LoRA (pre-trained): q-hisa/qwen3-4b-structeval-sft-lora-v3
Method: DPO (Direct Preference Optimization)
Epochs: 1
Learning rate: 5e-07
Beta: 0.1
Max sequence length: 1024
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 = "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]))

Evaluation Results

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.

q-hisa/dpo-qwen-cot-lora

Qwen3-4B StructEval DPO (SFT + DPO)

Training Objective

Training Configuration

Usage

Evaluation Results

Sources & License (IMPORTANT)

q-hisa/
dpo-qwen-cot-lora