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rezaduty/gemma4-e2b-privesc-macos
gemma4-e2b-privesc-macos 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 macos privilege escalation. Specialized in macOS privilege escalation: SIP bypass, TCC bypass, LaunchDaemon misconfigurations, dylib injection/hijackin…
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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 macos privilege escalation. Specialized in macOS privilege escalation: SIP bypass, TCC bypass, LaunchDaemon misconfigurations, dylib injection/hijacking, Keychain attacks, and macOS security hardening.
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 | macOS 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-macos"
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 macOS privilege escalation and security. Provide deep technical answers on macOS privesc techniques, TCC bypass, SIP, macOS security internals, and hardening with specific commands, tool names, and CVE references."}]},
{"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 macOS privilege escalation and security. Provide deep technical answers on macOS privesc techniques, TCC bypass, SIP, macOS security internals, and hardening with specific commands, tool names, and CVE references.