Detailed Explanation of the Technical Capabilities of the One-Person AI Startup DeepNetwork
CEO: Seokwon Jang / Contact: sayhi7@daum.net
GPT-3 Model Foundation Design Know-How
- Model Architecture: GPT-3 is a Transformer-based language model with 175 billion parameters. This model uses Transformer blocks with 96 heads.
- Training Data: GPT-3 was trained using a large-scale text dataset collected from the internet. This data includes a variety of languages and expressions.
- Training Method: GPT-3 was trained using an Auto-regressive Language Modeling method. This method aims to predict the next word.
- Training Cost: Training the GPT-3 model incurred very high costs. OpenAI invested hundreds of millions of dollars to train this model.
LoRA Model Fine-Tuning Know-How
- File Tuning: LoRA (Low-Rank Adaptation) is a method to fine-tune large-scale language models for specific tasks. This method converts the model's parameters into low-rank matrices to enhance performance for specific tasks.
- Training Data: The LoRA model is trained using a dataset tailored to specific tasks. This dataset is designed to allow the model to perform specific tasks.
- Training Method: The LoRA model converts the parameters of the existing model into low-rank matrices to enhance performance for specific tasks. This method enhances performance for specific tasks without modifying the existing model.
- Training Cost: Training the LoRA model incurs relatively lower costs. This is because it enhances performance for specific tasks without modifying the existing model.
Based on this technical know-how, the one-person AI startup DeepNetwork can leverage GPT-3 and LoRA models to provide various AI services. This enables better performance and efficiency.