Qwen3-4B-AI-Review-Detector

This model is a fine-tuned version of unsloth/qwen3-4b-base-unsloth-bnb-4bit designed to detect whether a Korean cosmetics review is Human-Written (HWR) or LLM-Generated (LGR).

It has been trained using TRL and Unsloth for efficient fine-tuning.

Model Details

• Base Model: unsloth/qwen3-4b-base-unsloth-bnb-4bit
• Task: Binary Classification (via Text Generation)
  • Class 0: HWR (Human Written Review)
  • Class 1: LGR (LLM Generated Review)
• Language: Korean
• Domain: Cosmetics / Beauty
• Training Method: LoRA (Low-Rank Adaptation)

Quick start

You can use the pipeline from the transformers library to run inference.

from transformers import pipeline

# Load the model
# Replace 'None' with your Hugging Face model ID (e.g., "username/Qwen3-4B-AI-Review-Detector")
model_id = "jedimark/Qwen3-4B-AI-Review-Detector" 
generator = pipeline("text-generation", model=model_id, device_map="auto")

# Example review
review_text = "이 제품 정말 좋아요! 배송도 빠르고 품질도 만족합니다."

# Construct the prompt
prompt = f"""다음 리뷰 텍스트가 사람이 작성한 것인지(Human Written) LLM이 생성한 것인지 판단하여 분류하세요.
{review_text}

Classify this review into one of the following:
class 0: HWR (Human Written Review)
class 1: LGR (LLM Generated Review)

SOLUTION
The correct answer is: class"""

# Run inference
output = generator(prompt, max_new_tokens=1, return_full_text=False)[0]
print(f"Predicted Class: {output['generated_text']}")
# Output: 0 (Human Written) or 1 (LLM Generated)

Training procedure

This model was trained using SFT (Supervised Fine-Tuning) with the following configuration:

• Dataset: Custom dataset of Korean cosmetics reviews labeled as Human-Written (0) or LLM-Generated (1).
• Quantization: 4-bit quantization using bitsandbytes (BnB) for memory efficiency.
• LoRA Configuration:
  • Rank (r): 16
  • Alpha: 16
  • Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj (All linear layers)
• Optimization: Trained with Unsloth for faster training and lower memory usage.

Framework versions

• Unsloth 2024.x
• PEFT 0.18.0
• TRL: 0.24.0
• Transformers: 4.57.2
• Pytorch: 2.9.0
• Datasets: 4.3.0
• Tokenizers: 0.22.1