--- library_name: transformers datasets: - ahmed000000000/cybersec - dzakwan/cybersec language: - en widget: - text: I have a port vulnerability on my device. What should I do? example_title: Port Vulnerability - text: >- An attacker hacked my pc with ransomware and is asking for money to decrypt my files. What should I do? example_title: Ransomware - text: >- A friend of mine got a malicious software on his PC, what are the steps to avoid any data loss? example_title: Avoiding data loss - text: >- My PC got affected with virus. What should I do? example_title: Virus Attack - text: >- How to perform risk assessment on a security system? example_title: Risk Assessment --- # Model Card for Model ID Works as a cyber assistant. ## Model Details ### Model Description This is the model card of a 🤗 transformers model that has been pushed on the Hub. - **Developed by:** Zardian18 - **Model type:** GPT2 - **Language(s) (NLP):** English - **Finetuned from model [optional]:** OpenAi GPT2-medium ### Model Sources [optional] - **Repository:** Github repo ## Uses Can be used to handle and solve basic cybersec queries. ## Bias, Risks, and Limitations Currently it is fine-tuned on GPT2, which is good but not comparable to state of the art LLMs and Transformers. Moreover, the dataset is small. [More Information Needed] ## How to Get Started with the Model ```# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Zardian/Cyber_assist3.0") ``` ```# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Zardian/Cyber_assist3.0") model = AutoModelForCausalLM.from_pretrained("Zardian/Cyber_assist3.0") ``` ## Training Details ### Training Data Cybersec queries and responses dataset consisting of 12k enteries. Cybersec dataset with instructions and output consisting of 14k enteries.