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URL: https://huggingface.co/sainikhiljuluri2015/DeepSeek-R1-Cybersecurity-8B-Merged

โ‡ฑ sainikhiljuluri2015/DeepSeek-R1-Cybersecurity-8B-Merged ยท Hugging Face


DeepSeek-R1-Cybersecurity-8B-Merged

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

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

  • ๐Ÿ” Threat analysis and classification
  • ๐Ÿšจ Security alert triage
  • ๐Ÿ“‹ Incident response guidance
  • ๐Ÿฆ  Malware analysis
  • ๐Ÿ“Š MITRE ATT&CK mapping
  • ๐Ÿ” Vulnerability assessment
  • ๐Ÿ’‰ SQL injection detection
  • ๐ŸŽฃ Phishing analysis
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