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sainikhiljuluri2015/DeepSeek-R1-Cybersecurity-8B-Merged
DeepSeek-R1-Cybersecurity-8B-Merged is a text generation model from sainikhiljuluri2015. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
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.
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.safetensors16.4 GB · 100%
From the Hugging Face model README
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.
This model was trained on ~50,000 cybersecurity instruction-response pairs from:
| 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 |
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))