Transformers
Safetensors
PEFT
English
text-generation-inference
gemma4
lora
cybersecurity
kubernetes
k8s
container-security
devsecops
Instructions to use rezaduty/gemma4-e2b-kubernetes-security with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rezaduty/gemma4-e2b-kubernetes-security with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("rezaduty/gemma4-e2b-kubernetes-security", dtype="auto") - PEFT
How to use rezaduty/gemma4-e2b-kubernetes-security with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Gemma 4 E2B โ 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, ฮฑ 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
- General cybersecurity model โ full 646-example dataset
- Docker & Container Security
- All rezaduty models
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