|
|
--- |
|
|
license: apache-2.0 |
|
|
base_model: ykarout/RPT-DeepSeek-R1-0528-Qwen3-8B |
|
|
tags: |
|
|
- cybersecurity |
|
|
- fine-tuned |
|
|
- deepseek |
|
|
- qwen3 |
|
|
- lora |
|
|
- cyber |
|
|
- nist |
|
|
- csf |
|
|
- pentest |
|
|
language: |
|
|
- en |
|
|
- ar |
|
|
- es |
|
|
- ru |
|
|
- it |
|
|
- de |
|
|
pipeline_tag: text-generation |
|
|
datasets: |
|
|
- Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset |
|
|
library_name: transformers |
|
|
--- |
|
|
|
|
|
# CyberSec-Qwen3-DeepSeekv1 |
|
|
|
|
|
This is a cybersecurity-specialized fine-tuned model based on DeepSeek-R1-Qwen3-8B. |
|
|
|
|
|
## Model Details |
|
|
|
|
|
- **Base Model**: ykarout/RPT-DeepSeek-R1-0528-Qwen3-8B |
|
|
- **Fine-tuning Method**: LoRA (Low-Rank Adaptation) |
|
|
- **Dataset**: Trendyol Cybersecurity Instruction Tuning Dataset |
|
|
- **Specialization**: Cybersecurity expertise and guidance |
|
|
|
|
|
## Usage |
|
|
|
|
|
```python |
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
import torch |
|
|
|
|
|
model_name = "ykarout/CyberSec-Qwen3-DeepSeekv1" |
|
|
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
|
model_name, |
|
|
torch_dtype=torch.bfloat16, |
|
|
device_map="auto", |
|
|
trust_remote_code=True |
|
|
) |
|
|
|
|
|
# Example usage |
|
|
messages = [ |
|
|
{"role": "system", "content": "You are a cybersecurity expert."}, |
|
|
{"role": "user", "content": "What is a DDoS attack and how can it be mitigated?"} |
|
|
] |
|
|
|
|
|
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
|
|
inputs = tokenizer(input_text, return_tensors="pt").to(model.device) |
|
|
|
|
|
with torch.no_grad(): |
|
|
outputs = model.generate( |
|
|
**inputs, |
|
|
max_new_tokens=512, |
|
|
temperature=0.7, |
|
|
do_sample=True, |
|
|
pad_token_id=tokenizer.eos_token_id |
|
|
) |
|
|
|
|
|
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True) |
|
|
print(response) |
|
|
``` |
|
|
|
|
|
## Training Details |
|
|
|
|
|
- **Framework**: TRL (Transformers Reinforcement Learning) |
|
|
- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA |
|
|
- **Assistant-only Loss**: Custom data collator for training only on assistant responses |
|
|
- **Hardware**: NVIDIA H100 |
|
|
- **Precision**: bfloat16 |
|
|
|
|
|
## Ethical Use |
|
|
|
|
|
This model is designed for educational and defensive cybersecurity purposes only. Please use responsibly and in accordance with applicable laws and ethical guidelines. |
|
|
|
|
|
## License |
|
|
|
|
|
Apache 2.0 |
