Fine-tuned deepseek-ai/DeepSeek-R1-0528-Qwen3-8B 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

Parameter	Value
Base Model	deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
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/DeepSeek-R1-Cybersecurity-8B-Merged",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("sainikhiljuluri2015/DeepSeek-R1-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))

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