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rezaduty/gemma4-e2b-privesc-windows
gemma4-e2b-privesc-windows 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 windows privilege escalation. Specialized in Windows privilege escalation: service misconfigurations, token impersonation (Potato family), UAC bypass,…
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Updated Jun 4, 2026
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From the Hugging Face model README
A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in windows privilege escalation. Specialized in Windows privilege escalation: service misconfigurations, token impersonation (Potato family), UAC bypass, registry attacks, scheduled tasks, kernel exploits, and credential hunting.
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 | Windows Privilege Escalation |
| 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-privesc-windows"
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 Windows privilege escalation techniques. Provide deep technical answers on Windows privesc methods, detection strategies, and hardening measures with specific commands, tool names, and CVE references where applicable."}]},
{"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 Windows privilege escalation techniques. Provide deep technical answers on Windows privesc methods, detection strategies, and hardening measures with specific commands, tool names, and CVE references where applicable.