May 08, 2024

Exploring Synthetic Data Creation for Model Training

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Exploring Synthetic Data Creation for Model Training

Source: youtube.com

Toran Billups presented an extensive exploration on creating and using synthetic data to train a language model that can mimic his texting style. Billups' personal project aimed to train a model that could respond in his likeness based on about two years' worth of his text messaging data. The process involved extracting, cleaning, and merging the data, which was crucial to avoid amplifying any quality issues later on. Using tools like 'IMazing' for data extraction and leaning on UNS sloth for quantizing models, he iterated through fine-tuning, prompting, and evaluating. A significant insight was discovering the importance of prompt engineering and the impact that well-structured prompts have on the language model's output. One noteworthy tactic was using a larger model like Mixol to generate high-quality synthetic data, which was then used to guide a smaller model's training, improving its text generation quality. The smaller model was then served using Elixir with NX, accentuating integration with Elixir's ecosystem. Billups highlighted the importance of focusing on data quality over quantity and considering the operational costs associated with model size. Finally, he encouraged the community to innovate by building, measuring, learning, and truly considering the end user's needs when developing AI-powered solutions.

Guide to Deploying Nx in Production for Elixir Applications

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May 07, 2024

Switching Paths: Peter Ullrich from Psychology to Elixir and Founding Indie Courses

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Apr 30, 2024

In-Depth Exploration and Q&A of the Ash Framework for Elixir

In this talk, Zach Daniel — creator of the Ash Framework — explores the key aspects of the Ash Framework and answers various questions. He discusses how Ash simplifies and enhances writing Elixir applications by providing a resource-oriented, declarative approach to application development.

Apr 29, 2024