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| Property | Value |
|---|---|
| Base model | google/gemma-4-e2b-it (2B parameters) |
| Fine-tuning method | QLoRA (rank 16, α 16) |
| Domain | Active Directory & Red Team |
| License | Apache 2.0 |
1from transformers import AutoTokenizer, AutoModelForCausalLM
2from peft import PeftModel
3import torch
4
5base_model = "google/gemma-4-e2b-it"
6adapter = "rezaduty/gemma4-e2b-redteam-activedirectory"
7
8tokenizer = AutoTokenizer.from_pretrained(adapter)
9model = AutoModelForCausalLM.from_pretrained(
10 base_model, torch_dtype=torch.bfloat16, device_map="auto"
11)
12model = PeftModel.from_pretrained(model, adapter)
13
14messages = [
15 {"role": "system", "content": [{"type": "text", "text": "You are an expert in Active Directory security and red team operations. You provide deep answers on AD attack paths, lateral movement, credential theft, and defensive hardening."}]},
16 {"role": "user", "content": [{"type": "text", "text": "Your question here"}]},
17]
18inputs = tokenizer.apply_chat_template(
19 messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
20).to(model.device)
21output = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
22print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))You are an expert in Active Directory security and red team operations. You provide deep answers on AD attack paths, lateral movement, credential theft, and defensive hardening.