README.md

---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
# model-card-metadata
language: [en]
tags: [AI, NLP, Cybersecurity, Ethical Hacking, Pentesting]
license: mit
pipeline_tag: text-generation
metrics:
  - accuracy
  - perplexity
  - response_time
model_type: causal-lm
---

# Model Card for Pentest AI

<!-- Provide a quick summary of what the model is/does. -->

This model card provides an overview of **Pentest AI**, a generative language model designed to assist in the domain of penetration testing and cybersecurity. It generates informative responses related to ethical hacking practices and techniques, helping users enhance their knowledge and skills in the field.

## Model Details

### Model Description

**Pentest AI** is a causal language model fine-tuned specifically for generating relevant and contextual information about penetration testing methodologies, tools, and best practices. It serves as an educational resource for security professionals and enthusiasts.

- **Developed by:** Esteban Cara de Sexo
- **Funded by [optional]:** No funding received
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Causal Language Model (CLM)
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

- **Repository:** [Your GitHub Repository Link]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

### Direct Use

**Pentest AI** is intended for direct interaction, allowing users to generate and explore text-based scenarios related to penetration testing and cybersecurity techniques.

### Downstream Use [optional]

This model can be incorporated into cybersecurity training platforms, interactive learning environments, or tools aimed at improving security practices.

### Out-of-Scope Use

The model is not intended for use in malicious activities, unauthorized access, or any illegal operations related to penetration testing.

## Bias, Risks, and Limitations

While **Pentest AI** aims to produce accurate information, it may generate biased or misleading content. Users are encouraged to critically evaluate the outputs.

### Recommendations

Users should be aware of the model's limitations and verify generated content before application in real-world scenarios, especially concerning ethical and legal implications.

## How to Get Started with the Model

To start using **Pentest AI**, you can implement the following code snippet:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "Canstralian/pentest_ai"
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)

input_text = "Describe the steps involved in a penetration test."
inputs = tokenizer.encode(input_text, return_tensors='pt')
outputs = model.generate(inputs)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(output_text)
```

## Training Details

### Training Data

The model was trained on a diverse dataset encompassing articles, guides, and documentation related to penetration testing and cybersecurity. Refer to the associated Dataset Card for more details.

### Training Procedure

#### Preprocessing [optional]

Training data was filtered to remove any sensitive or personally identifiable information, ensuring adherence to ethical standards.

#### Training Hyperparameters

- **Training regime:** fp16 mixed precision

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

- **Training Duration:** Approximately 10 hours
- **Checkpoint Size:** 500MB

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated on a distinct dataset of penetration testing scenarios and inquiries.

#### Factors

Evaluation metrics are disaggregated by user demographics and application contexts, including educational versus professional uses.

#### Metrics

- **Accuracy:** Measures the correctness of the model's generated responses.
- **Perplexity:** Assesses the model's confidence in its predictions.
- **Response Time:** Measures how quickly the model provides outputs.

### Results

The model demonstrated an accuracy of 85% in generating appropriate responses during evaluation.

#### Summary

**Pentest AI** proves to be a valuable resource for generating information on penetration testing, but users should remain cautious and validate the generated information.

## Model Examination [optional]

Further research is required to assess the interpretability and decision-making processes of the model.

## Environmental Impact

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:** NVIDIA Tesla V100
- **Hours used:** 10
- **Cloud Provider:** Google Cloud Platform
- **Compute Region:** us-central1
- **Carbon Emitted:** Estimated 120 kg CO2

## Technical Specifications [optional]

### Model Architecture and Objective

**Pentest AI** employs a transformer architecture optimized for generating coherent and contextually relevant text in the realm of penetration testing.

### Compute Infrastructure

The model was trained on high-performance GPU instances within a cloud infrastructure.

#### Hardware

- **Type:** NVIDIA Tesla V100
- **Count:** 4 GPUs

#### Software

The model is developed using PyTorch and the Hugging Face Transformers library.

## Citation [optional]

For citations related to this model, please refer to the following information:

**BibTeX:**

```bibtex
@article{deJager2024,
  title={Pentest AI: A Generative Model for Penetration Testing Text Generation},
  author={Esteban Cara de Sexo},
  journal={arXiv preprint arXiv:2401.00000},
  year={2024}
}
```

**APA:**

Cara de Sexo, E. (2024). *Pentest AI: A Generative Model for Penetration Testing Text Generation*. arXiv preprint arXiv:2401.00000.

## Glossary [optional]

- **Causal Language Model (CLM):** A model that predicts the next word in a sequence based on the previous words.

## More Information [optional]

For further inquiries and updates, please refer to [Your GitHub Repository Link].

## Model Card Authors [optional]

- Esteban Cara de Sexo

## Model Card Contact

For questions, please contact Esteban Cara de Sexo at [distortedprojection@gmail.com].
```

### Next Steps

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2. **Update metrics** and results based on your model's specific performance and findings.
3. **Review and edit sections** to ensure they accurately represent your model and its capabilities.