dpo-qwen-cot-lora-v7

Qwen3-4B StructEval DPO (SFT + DPO)v4

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.

Note: This model was fine-tuned from a pre-trained SFT LoRA (q-hisa/qwen3-4b-structeval-sft-lora-v5), 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-v5
• 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.

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

<!-- 【任意】StructEval などのベンチマークスコアがあれば記載してください --> <!-- 例: - **StructEval-T**: 0.XX -->

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.