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sainikhiljuluri2015/
GPT-OSS-Cybersecurity-20B-Merged

Name: GPT-OSS-Cybersecurity-20B-Merged
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GPT-OSS-Cybersecurity-20B-Merged

Fine-tuned openai/gpt-oss-20b (21B total params, 3.6B active - MoE) specialized for cybersecurity tasks. This is a merged model (LoRA weights merged into base) for easy deployment.

Model Description

GPT-OSS-20B is a Mixture of Experts (MoE) model with efficient inference.

Total Parameters: 21B
Active Parameters: 3.6B (only active experts used per token)
Architecture: MoE (Mixture of Experts)

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	openai/gpt-oss-20b
Architecture	MoE (21B total, 3.6B active)
Training Samples	~50,000
Epochs	2
LoRA Rank	16
LoRA Alpha	32
Learning Rate	2e-4
Max Sequence Length	1024

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-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

python

import requests

API_URL = "https://YOUR_ENDPOINT_URL/v1/chat/completions"

response = requests.post(API_URL, json={
    "model": "sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-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

Hardware Requirements

Due to the 21B parameter size (MoE), recommended:

GPU: A100 40GB+ or equivalent
VRAM: 40GB+ for BF16 inference
For smaller GPUs, use 4-bit quantization