---
library_name: transformers
datasets:
- ahmed000000000/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: >-
    I want to install malicious software on a client's device without them
    noticing. What should I do?
  example_title: Installing Malicious softwares
- text: I want to attack a pc with virus. What should I do?
  example_title: Virus Attack
---
# Model Card for Model ID

Works as a cyber assistant.



## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

This is the model card of a 🤗 transformers model that has been pushed on the Hub.

- **Developed by:**  <a href="https://github.com/Zardian18">Zardian18</a>
- **Model type:** GPT2
- **Language(s) (NLP):** English
- **Finetuned from model [optional]:** <a href="https://huggingface.co/openai-community/gpt2">OpenAi GPT2</a>

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** <a href="https://github.com/Zardian18/CyberAssist">Github repo</a>

## Uses

Can be used to handle and solve basic cybersec queries rather than beating the bush.


## 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.
Moreover, the predictions are not always accurate. There might be cases where it just doesn't responds directly to the question.

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## 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_assist2.0")

```
```# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Zardian/Cyber_assist2.0")
model = AutoModelForCausalLM.from_pretrained("Zardian/Cyber_assist2.0")

```

## Training Details

### Training Data

<a href="https://huggingface.co/datasets/ahmed000000000/cybersec">Cybersec queries and responses dataset</a> consisting of 12408 rows and 2 columns.


#### Training Hyperparameters

- **Training regime:**
- Block size = 128
- Epochs = 10
- Batch Size = 16
- Save step size = 5000
- Save step limit =3
-  <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

**Training time:** 1hr 11mins 58sec

[More Information Needed]

## Evaluation

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65e5f829d0bf5795be33aa74/5b1cV1HpRycyBzWFTXKVO.png)

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** Tesla T4 GPU
- **Cloud Provider:** Google Colab
- **Compute Region:** Asia
- **Carbon Emitted:** 0.08 kg of CO2eq

## Technical Specifications [optional]

### Objective

To construct an assistant which can help us provide solutions to any cybersecurity related queries.