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gemma4-e2b-kubernetes-security

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metadata 
base_model: google/gemma-4-e2b-it
tags:
  - text-generation-inference
  - transformers
  - gemma4
  - peft
  - lora
  - cybersecurity
  - kubernetes
  - k8s
  - container-security
  - devsecops
  - cybersecurity
license: apache-2.0
language:
  - en

Gemma 4 E2B — Kubernetes Security Expert

A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in kubernetes security. Specialized in Kubernetes security: RBAC design, Pod Security Standards, network policies, admission controllers, etcd encryption, and multi-tenant cluster hardening.

Part of the rezaduty cybersecurity model family.

Expertise

  • RBAC least-privilege design and audit logging
  • Pod Security Standards (Restricted, Baseline, Privileged)
  • Network policies and service mesh security (Istio/Linkerd)
  • Admission controllers: OPA Gatekeeper, Kyverno, Falco
  • Secrets encryption at rest and external secrets management
  • Supply-chain security: image signing, SBOM, admission control

Model Details

Property Value
Base model google/gemma-4-e2b-it (2B parameters)
Fine-tuning method QLoRA (rank 16, α 16)
Domain Kubernetes 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-kubernetes-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 Kubernetes security. You provide deep answers on RBAC, Pod Security, network policies, admission controllers, secrets management, and cluster 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 Kubernetes security. You provide deep answers on RBAC, Pod Security, network policies, admission controllers, secrets management, and cluster hardening.

See Also