Tag: synthetic-data

Robust, maintainable, expressive and composable pipelines are critical for scaling synthetic data generation. This post advocates for abstractions that reduce boilerplate, avoiding ad-hoc scripts, and leveraging dataframe APIs with structured document representations. The concrete example involves fine-tuning a smaller model using synthetic QA pairs generated from SEC corporate reports by a frontier model, maintaining quality while reducing inference costs.

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In this post, I explore how synthetic data is used to train and fine-tune large language models. I'll focus on Meta's open-source **synthetic-data-kit**, a tool built for exactly this purpose. LLMs owe their success to two factors: human ingenuity and the vast, annotated text of the internet.

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Meta's **synthetic-data-kit** is a toolkit designed to generate high-quality synthetic datasets for fine-tuning Large Language Models. The tool streamlines the process of creating training data through an ETL-like pipeline with four key operations. The toolkit exposes a simple CLI interface with these commands:

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