metadata
license: apache-2.0
base_model: deepseek-ai/DeepSeek-R1-0528-Qwen3-8B
tags:
- cybersecurity
- security
- deepseek
- fine-tuned
- merged
- text-generation
datasets:
- Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset
- AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
inference: true
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
- 🔍 Threat analysis and classification
- 🚨 Security alert triage
- 📋 Incident response guidance
- 🦠 Malware analysis
- 📊 MITRE ATT&CK mapping
- 🔐 Vulnerability assessment
- 💉 SQL injection detection
- 🎣 Phishing analysis