GPT-OSS-Cybersecurity-20B-Merged-heretic-ara/README.md

license, base_model, tags, datasets, language, pipeline_tag, library_name, inference

license	base_model	tags	datasets	language	pipeline_tag	library_name	inference
apache-2.0	sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged	cybersecurity security gpt-oss openai fine-tuned merged text-generation moe heretic uncensored decensored abliterated ara	Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.0 trendmicro-ailab/Primus-Instruct	en	text-generation	transformers	true

This is a decensored version of sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method

Abliteration parameters

Parameter	Value
start_layer_index	10
end_layer_index	22
preserve_good_behavior_weight	0.9307
steer_bad_behavior_weight	0.0066
overcorrect_relative_weight	1.1973
neighbor_count	2

Performance

Metric	This model	Original model (sainikhiljuluri2015/GPT-OSS-Cybersecurity-20B-Merged)
PIQA acc_norm	0.7802	Unknown
Refusals	3/100	88/100

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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

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

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