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dsuyu1
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Model Details
Model Provider
dsuyu1
Model Tree
mistralai/Mistral-7B-v0.1
Input Modalities
Output Modalities
Supported Functionality
Specializes in cybersecurity tasks including incident summarization, alert triage, and threat intelligence analysis. Trained without centralizing any organization's private security data.
| Method | ROUGE-L |
|---|---|
| Zero-shot Mistral-7B | 0.367 |
| Centralized DAPT | 0.330 |
| FedDAPT (this model) | 0.707 |
FedDAPT achieved 2.1x improvement over centralized training on incident summarization.
python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torch base = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ), device_map="auto",)model = PeftModel.from_pretrained(base, "dsuyu1/FedDAPT-security-v1")tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") prompt = """### Instruction:Summarize the following security incident in one sentence. ### Input:Incident timeline: initial_access: phishing with macro attachment -> execution: PowerShell encoded command -> c2: Cobalt Strike HTTPS beacon -> lateral: SMB + PsExec lateral movement -> impact: ransomware across endpoints. ### Response:""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tokenizer.decode(out[0], skip_special_tokens=True))Villarreal, D. "Smarter SecOps: Leveraging Private, Federated Transfer Learning" BSides RGV 2026.
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigfrom peft import PeftModelimport torch base = AutoModelForCausalLM.from_pretrained( "mistralai/Mistral-7B-v0.1", quantization_config=BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, ), device_map="auto",)model = PeftModel.from_pretrained(base, "dsuyu1/FedDAPT-security-v1")tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1") prompt = """### Instruction:Summarize the following security incident in one sentence. ### Input:Incident timeline: initial_access: phishing with macro attachment -> execution: PowerShell encoded command -> c2: Cobalt Strike HTTPS beacon -> lateral: SMB + PsExec lateral movement -> impact: ransomware across endpoints. ### Response:""" inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): out = model.generate(**inputs, max_new_tokens=128, do_sample=False)print(tokenizer.decode(out[0], skip_special_tokens=True))