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}}
}