Introducing Tuna - A Tool for Rapidly Generating Synthetic Fine-Tuning Datasets

Tuna is a no-code tool that enables users to generate large language model (LLM) fine-tuning datasets from scratch, making it easier for anyone to create high-quality training data for fine-tuning LLMs like LLaMAs. The tool has both a web interface and a Python script, allowing users to provide an input CSV file of text data that will be individually sent to OpenAI as context to generate prompt-completion pairs from. Fine-tuning is the technique of taking a pre-trained large language model like GPT-3.5-turbo or LLaMa-2-7b and training it further on a specific task or dataset, adapting the model to a new domain or specific purpose. Tuna can be used for tasks such as improving response formatting and reliability, using small self-hosted LLMs for simple tasks, and teaching an LLM new information through fine-tuning. However, challenges in fine-tuning include having a dataset of several hundred to one thousand examples, ensuring high quality data, and managing rate limits.