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Generate high-quality synthetic datasets economically using lightweight libraries

Large Language Models (LLMs) are capable and general-purpose tools, but often they lack domain-specific knowledge, which is frequently stored in enterprise repositories.

Fine-tuning a custom LLM with your own data can bridge this gap, and data preparation is the first step in this process. It is also a crucial step that can significantly influence your fine-tuned models performance.

However, manually creating datasets can be an expensive and time-consuming. Another approach is leveraging an LLM to generate synthetic datasets, often using high-performance models such as GPT-4, which can turn out to be very costly.

In this article, I aim to bring to your attention to a cost-efficient alternative for automating the creation of instruction datasets from various documents. This solution involves utilizing a lightweight open-source library called Bonito.

Before we dive into the library bonito and how it works, we need to first understand what even an instruction is.

An instruction is a text or prompt given to a LLM, such as Llama, GPT-4, etc. It directs the model to produce a specific kind of answer. Through instructions, people can guide the discussion, ensuring that the models replies are relevant, helpful, and in line with what the user wants. Creating clear and precise instructions is important to achieve the desired outcome.

Bonito is an open-source model designed for conditional task generation. It can be used to create synthetic instruction tuning datasets to adapt large language models to users specialized, private data.

Read the rest here:

How to Generate Instruction Datasets from Any Documents for LLM Fine-Tuning - Towards Data Science