NVIDIA-Orchestrator-Cybersecurity-8B-Merged
Fine-tuned nvidia/Orchestrator-8B specialized for cybersecurity tasks. This is a merged model (LoRA weights merged into base) for easy deployment.
Trained using NVIDIA NeMo Orchestrator.
Model Description
This model was trained on ~50,000 cybersecurity instruction-response pairs from:
- Trendyol Cybersecurity Dataset (35K samples)
- Fenrir v2.0 Dataset (12K samples)
- Primus-Instruct (3x upsampled)
Training Details
| Parameter | Value |
|---|---|
| Base Model | nvidia/Orchestrator-8B |
| Training Framework | NVIDIA NeMo Orchestrator |
| Training Samples | ~50,000 |
| Epochs | 2 |
| LoRA Rank | 16 |
| LoRA Alpha | 32 |
| Learning Rate | 2e-4 |
| Max Sequence Length | 1024 |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"sainikhiljuluri2015/NVIDIA-Orchestrator-Cybersecurity-8B-Merged",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/NVIDIA-Orchestrator-Cybersecurity-8B-Merged", trust_remote_code=True)
prompt = "What are the indicators of a ransomware attack?"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
API Usage
import requests
API_URL = "https://YOUR_ENDPOINT_URL/v1/chat/completions"
response = requests.post(API_URL, json={
"model": "sainikhiljuluri2015/NVIDIA-Orchestrator-Cybersecurity-8B-Merged",
"messages": [{"role": "user", "content": "What is SQL injection?"}],
"max_tokens": 300
})
print(response.json()["choices"][0]["message"]["content"])
Cybersecurity Capabilities
- 🔍 Threat analysis and classification
- 🚨 Security alert triage
- 📋 Incident response guidance
- 🦠 Malware analysis
- 📊 MITRE ATT&CK mapping
- 🔐 Vulnerability assessment
- 💉 SQL injection detection
- 🎣 Phishing analysis