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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](https://huggingface.co/google/gemma-4-e2b-it) 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](https://huggingface.co/rezaduty).
---
## 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
```python
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](https://huggingface.co/rezaduty/gemma4-e2b-cybersecurity-interview) — full 646-example dataset
- [Docker & Container Security](https://huggingface.co/rezaduty/gemma4-e2b-docker-container-security)
- [All rezaduty models](https://huggingface.co/rezaduty)
|