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rezaduty/gemma4-e2b-active-directory-ttps
gemma4-e2b-active-directory-ttps is a machine learning model from rezaduty. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in active directory attack ttps. Specialized in Active Directory attack techniques: BloodHound attack path analysis, Kerberos delegation abuses, RBCD, GPO…
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Updated Jun 4, 2026
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.safetensors124 MB · 79%
From the Hugging Face model README
A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in active directory attack ttps. Specialized in Active Directory attack techniques: BloodHound attack path analysis, Kerberos delegation abuses, RBCD, GPO abuse, ACL attacks, trust attacks, and domain persistence.
Part of the rezaduty cybersecurity model family.
| Property | Value |
|---|---|
| Base model | google/gemma-4-e2b-it (2B parameters) |
| Fine-tuning method | QLoRA (rank 16, α 16) |
| Domain | Active Directory Attack TTPs |
| Dataset | rezaduty/cybersecurity-qa-v2 |
| License | Apache 2.0 |
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
base_model = "google/gemma-4-e2b-it"
adapter = "rezaduty/gemma4-e2b-active-directory-ttps"
tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(
base_model, torch_dtype=torch.bfloat16, device_map="auto"
)
model = PeftModel.from_pretrained(model, adapter)
messages = [
{"role": "system", "content": [{"type": "text", "text": "You are an expert in Active Directory attack techniques and red team operations. Provide deep, technical answers on AD exploitation, attack paths, lateral movement, and domain dominance techniques with tool references and MITRE ATT&CK mappings."}]},
{"role": "user", "content": [{"type": "text", "text": "Your question here"}]},
]
inputs = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
output = model.generate(inputs, max_new_tokens=512, temperature=0.7, top_p=0.9)
print(tokenizer.decode(output[0][inputs.shape[-1]:], skip_special_tokens=True))
You are an expert in Active Directory attack techniques and red team operations. Provide deep, technical answers on AD exploitation, attack paths, lateral movement, and domain dominance techniques with tool references and MITRE ATT&CK mappings.