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