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base_model: google/gemma-4-e2b-it
tags:
- text-generation-inference
- transformers
- gemma4
- peft
- lora
- cybersecurity
- active-directory
- red-team
- pentesting
- cybersecurity
- windows-security
license: apache-2.0
language:
- en
---
# Gemma 4 E2B — Active Directory & Red Team Expert
A QLoRA fine-tuned version of [Gemma 4 E2B Instruct](https://huggingface.co/google/gemma-4-e2b-it) specialized in **active directory & red team**.
Specialized in **Active Directory and red team techniques**: Kerberoasting, Pass-the-Hash, DCSync, BloodHound analysis, GPO abuse, and defensive hardening strategies.
Part of the [rezaduty cybersecurity model family](https://huggingface.co/rezaduty).
---
## Expertise
- Kerberoasting, AS-REP Roasting, and Kerberos delegation abuses
- Pass-the-Hash, Pass-the-Ticket, and credential harvesting
- DCSync, Golden/Silver Ticket attacks and detection
- BloodHound/SharpHound attack path analysis
- GPO abuse, ACL misconfigurations, and AdminSDHolder
- AD hardening: tiering model, PAW, LAPS, Protected Users group
---
## Model Details
| 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 |
---
## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
base_model = "google/gemma-4-e2b-it"
adapter = "rezaduty/gemma4-e2b-redteam-activedirectory"
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 security and red team operations. You provide deep answers on AD attack paths, lateral movement, credential theft, and defensive hardening."}]},
{"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))
```
---
## System Prompt
```
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.
```
---
## See Also
- [General cybersecurity model](https://huggingface.co/rezaduty/gemma4-e2b-cybersecurity-interview) — full 646-example dataset
- [Docker & Container Security](https://huggingface.co/rezaduty/gemma4-e2b-docker-container-security)
- [Kubernetes Security](https://huggingface.co/rezaduty/gemma4-e2b-kubernetes-security)
- [AI & LLM Security](https://huggingface.co/rezaduty/gemma4-e2b-ai-llm-security)
- [All rezaduty models](https://huggingface.co/rezaduty)
|