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gemma4-e2b-cloud-iam-terraform

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metadata 
base_model: google/gemma-4-e2b-it
tags:
  - text-generation-inference
  - transformers
  - gemma4
  - peft
  - lora
  - cybersecurity
  - cloud-security
  - aws
  - iam
  - terraform
  - devsecops
  - cybersecurity
license: apache-2.0
language:
  - en

Gemma 4 E2B — Cloud IAM & Terraform Security Expert

A QLoRA fine-tuned version of Gemma 4 E2B Instruct specialized in cloud iam & terraform security. Specialized in cloud IAM and Terraform security: least-privilege IAM policy design, ECR image scanning, Terraform state security, and cloud privilege escalation paths.

Part of the rezaduty cybersecurity model family.

Expertise

  • AWS IAM least-privilege design and permission boundaries
  • IAM role assumption, OIDC federation, and cross-account access
  • ECR image scanning, lifecycle policies, and pull-through cache security
  • Terraform state file security, remote backends, and drift detection
  • Cloud privilege escalation paths and detection
  • IaC security scanning: Checkov, tfsec, Terrascan

Model Details

Property Value
Base model google/gemma-4-e2b-it (2B parameters)
Fine-tuning method QLoRA (rank 16, α 16)
Domain Cloud IAM & Terraform 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-cloud-iam-terraform"

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 cloud IAM and infrastructure-as-code security. You provide deep answers on AWS IAM, ECR hardening, Terraform security, and cloud privilege escalation paths."}]},
    {"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 cloud IAM and infrastructure-as-code security. You provide deep answers on AWS IAM, ECR hardening, Terraform security, and cloud privilege escalation paths.

See Also