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Ilieg
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Model Details
Model Provider
Ilieg
Model Tree
Qwen/Qwen2.5-7B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Evaluated on a held-out, deduplicated unseen test split (N=500 emails) consisting of real-world corporate communications, spear-phishing lures, and false-positive traps:
| Model Variant | Tuned Target Modules | Accuracy | Phishing F1-Score | ROC-AUC | Avg. Latency | VRAM Footprint |
|---|---|---|---|---|---|---|
| Qwen2.5-7B (Base Zero-Shot) | None | 82.40% | 80.10% | 0.8310 | ~240 ms | 14.2 GB (16-bit) |
| Standard LoRA | q, k, v, o | 97.00% | 96.50% | 0.9681 | ~250 ms | 5.8 GB (4-bit AWQ) |
| Comprehensive LoRA (Ours - Best) | q, k, v, o, gate, up, down | 97.80% | 97.46% | 0.9773 | ~255 ms | 5.9 GB (4-bit AWQ) |
transformerspython
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_id = "Ilieg/qwen2.5-7b-phishing-standard-merged-16bit"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto")system_prompt = "You are an expert cybersecurity email threat analyzer. Classify the email as either 'Phishing' or 'Benign'."user_message = "Subject: URGENT: Direct Deposit Update Required\n\nPlease update my account routing number immediately prior to Friday payroll."messages = [{"role": "system", "content": system_prompt},{"role": "user", "content": user_message}]prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=10, do_sample=False)prediction = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()print(f"Classification: {prediction}")