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gemma4-e2b-ai-llm-security

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PEFT
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text-generation-inference
gemma4
lora
cybersecurity
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prompt-injection
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metadata 
base_model: google/gemma-4-e2b-it
tags:
  - text-generation-inference
  - transformers
  - gemma4
  - peft
  - lora
  - cybersecurity
  - ai-security
  - llm-security
  - prompt-injection
  - cybersecurity
  - machine-learning
license: apache-2.0
language:
  - en

Gemma 4 E2B — AI & LLM Security Expert

A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in ai & llm security. Specialized in AI and LLM security: prompt injection attacks, jailbreaks, model poisoning, training data extraction, adversarial examples, and guardrail design.

Part of the rezaduty cybersecurity model family.

Expertise

  • Prompt injection — direct and indirect attack vectors
  • Jailbreak techniques and system prompt extraction
  • Training data poisoning and backdoor attacks
  • Membership inference and model inversion attacks
  • LLM guardrails, content filtering, and output validation
  • Secure RAG pipelines and agentic system threat modeling

Model Details

Property Value
Base model google/gemma-4-e2b-it (2B parameters)
Fine-tuning method QLoRA (rank 16, α 16)
Domain AI & LLM Security
License Apache 2.0

Usage 

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

base_model = "google/gemma-4-e2b-it"
adapter    = "rezaduty/gemma4-e2b-ai-llm-security"

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 AI and LLM security. You provide deep answers on prompt injection, model poisoning, adversarial attacks, LLM guardrails, and secure AI deployment."}]},
    {"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 AI and LLM security. You provide deep answers on prompt injection, model poisoning, adversarial attacks, LLM guardrails, and secure AI deployment.

See Also