Prox 7b Dpo

🚀 Prox 7B DPO

A fine - tuned model based on Mistral - 7b - v0.2, designed for hacking and coding - related natural language processing tasks.

🚀 Quick Start

This README provides detailed information about the Prox 7B DPO model, including its description, intended uses, training data, and hyperparameters.

✨ Features

• Based on the new Mistral - 7b - v0.2 base model released by MistralAI on March 23, 2024.
• Fine - tuned on a dataset related to hacking and coding to enhance performance in relevant tasks.
• Can assist with various natural language processing tasks such as code generation, code explanation, answering hacking - related questions, and providing coding project insights.

📦 Installation

No installation steps are provided in the original README.

💻 Usage Examples

No code examples are provided in the original README.

📚 Documentation

Model description

This model is based on Mistral - 7b - v0.2, a new base model released by MistralAI on March 23, 2024 but they have not yet published on HuggingFace. It has been fine - tuned on a dataset containing information related to hacking and coding, with the aim of enhancing its performance on tasks within these domains.

Intended uses & limitations

This model is designed to assist with a variety of natural language processing tasks related to hacking and coding, such as:

• Code generation
• Code explanation and documentation
• Answering questions about hacking techniques and cybersecurity
• Providing insights and suggestions for coding projects

However, it is important to note that while the model has been fine - tuned on hacking and coding data, it should not be relied upon for critical security applications or used to engage in illegal activities. The model's outputs should be carefully reviewed and verified by experts in the relevant fields before being put into practice.

Training data

The prox - 7b model was fine - tuned on a proprietary dataset curated by OpenVoid AI, which consists of a diverse range of hacking and coding - related content.

Training hyperparameters

The following hyperparameters were used during training:

• Learning rate: 2e - 05
• Train batch size: 4
• Eval batch size: 8
• Seed: 42
• Distributed type: multi - GPU
• Number of devices: 2
• Gradient accumulation steps: 4
• Total train batch size: 32
• Total eval batch size: 16
• Optimizer: Adam with betas=(0.9,0.95) and epsilon = 1e - 08
• LR scheduler type: cosine
• LR scheduler warmup steps: 100
• Training Steps: 414

The training was performed using a distributed multi - GPU setup to accelerate the process and handle the large model size.

🔧 Technical Details

The model is fine - tuned on a dataset related to hacking and coding. The training process uses a distributed multi - GPU setup with specific hyperparameters to optimize the performance of the model on relevant tasks.

📄 License

The model is licensed under the Apache 2.0 license.

Additional Information

• By OpenVoid AI
• Discord: https://discord.gg/CBDAbKkgNV
• 
• Tags: code, cybersecurity, penetration testing, hacking
• Datasets:
  • CyberNative/Code_Vulnerability_Security_DPO
  • diffnamehard/toxic - dpo - v0.1 - NoWarning - alpaca
  • mlabonne/chatml - OpenHermes2.5 - dpo - binarized - alpha