Foundation-Sec-Cybersecurity-8B-Merged

Foundation-Sec-Cybersecurity-8B-Merged

Fine-tuned fdtn-ai/Foundation-Sec-8B specialized for cybersecurity tasks. This is a merged model (LoRA weights merged into base) for easy deployment.

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

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "sainikhiljuluri2015/Foundation-Sec-Cybersecurity-8B-Merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/Foundation-Sec-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/Foundation-Sec-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
• 🔑 CVE knowledge
• 🛡️ Security best practices