File size: 2,399 Bytes
79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 79d6542 518ff09 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | ---
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
This is the **merged** version of [sainikhiljuluri/DeepSeek-R1-Cybersecurity-8B](https://huggingface.co/sainikhiljuluri/DeepSeek-R1-Cybersecurity-8B),
where the LoRA adapter has been merged into the base model for easier deployment.
## Model Description
Fine-tuned **deepseek-ai/DeepSeek-R1-0528-Qwen3-8B** specialized for **cybersecurity** tasks.
This merged model can be loaded directly without needing PEFT.
## 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 |
## Usage
### Direct Loading
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model = AutoModelForCausalLM.from_pretrained(
"sainikhiljuluri/DeepSeek-R1-Cybersecurity-8B-Merged",
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
"sainikhiljuluri/DeepSeek-R1-Cybersecurity-8B-Merged",
trust_remote_code=True
)
prompt = "Explain how to detect SQL injection attacks."
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))
```
### Via Inference API
```python
import requests
API_URL = "https://api-inference.huggingface.co/models/sainikhiljuluri/DeepSeek-R1-Cybersecurity-8B-Merged"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}
response = requests.post(API_URL, headers=headers, json={
"inputs": "What are the indicators of a ransomware attack?",
"parameters": {"max_new_tokens": 256, "temperature": 0.7}
})
print(response.json())
```
## Cybersecurity Capabilities
- 🔍 Threat analysis and classification
- 🚨 Security alert triage
- 📋 Incident response guidance
- 🦠 Malware analysis
- 📊 MITRE ATT&CK mapping
- 🔐 Vulnerability assessment
|