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1from transformers import AutoTokenizer, AutoModelForCausalLM
2import torch
3
4# Load model and tokenizer
5model = AutoModelForCausalLM.from_pretrained(
6 "./mistral-sigma-full-model",
7 torch_dtype=torch.float16,
8 device_map="auto"
9)
10tokenizer = AutoTokenizer.from_pretrained("./mistral-sigma-full-model")
11
12# Prepare input
13system_prompt = "You are a cybersecurity expert specializing in creating Sigma detection rules from CAPEv2 behavioral analysis reports."
14instruction = "Given this CAPEv2 behavioral report, produce Sigma rules that a SOC analyst could deploy in a SIEM."
15
16# Your CAPEv2 report data
17cape_report = {
18 "signatures": ["process_injection", "registry_modification"],
19 "processes": [...]
20}
21
22prompt = f'''<<SYS>>
23{system_prompt}
24<</SYS>>
25
26{instruction}
27
28Input Data:
29{cape_report}
30
31Response:'''
32
33# Generate
34inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
35outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.7)
36sigma_rules = tokenizer.decode(outputs[0], skip_special_tokens=True)
37print(sigma_rules)