Fraud Call Detection - Qwen 0.5B LoRA
This is a LoRA fine-tuned model for detecting fraudulent phone calls based on dialogue transcripts.
Base Model
- Base: Qwen/Qwen2.5-0.5B-Instruct
- Method: LoRA (Low-Rank Adaptation)
- Task: Binary Classification (Fraud vs Legitimate)
Training Details
- LoRA Rank: 16
- LoRA Alpha: 32
- Target Modules: q_proj, k_proj, v_proj, o_proj
- Training: Instruction-tuned format
Usage
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Load model and tokenizer
base_model = "Qwen/Qwen2.5-0.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.float16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
# Load LoRA adapters
model = PeftModel.from_pretrained(model, "shakeleoatmeal/Fraud-call-detection-Qwen-0.5B-Lora")
# Prepare input
dialogue = "Your phone call dialogue here..."
instruction = f'''Analyze the following phone call dialogue and determine if it is fraudulent or legitimate.
Respond with only "1" if the call is fraudulent/scam, or "0" if it is legitimate.
Phone Call Dialogue:
{dialogue}
Classification:'''
inputs = tokenizer(instruction, return_tensors="pt").to(model.device)
# Generate prediction
outputs = model.generate(**inputs, max_new_tokens=5)
response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response) # "1" for fraud, "0" for legitimate
Model Output
- 0: Legitimate call
- 1: Fraudulent/Scam call
Limitations
- Designed for English language phone call dialogues
- Performance may vary with dialogue length and format
- Should be used as part of a broader fraud detection system
Citation
If you use this model, please cite:
@misc{fraud-call-detection-qwen,
author = {shakeleoatmeal},
title = {Fraud Call Detection - Qwen 0.5B LoRA},
year = {2025},
publisher = {HuggingFace},
howpublished = {\url{https://huggingface.co/shakeleoatmeal/Fraud-call-detection-Qwen-0.5B-Lora}}
}
- Downloads last month
- 2
Inference Providers
NEW
This model isn't deployed by any Inference Provider.
๐
Ask for provider support