Finding the right embedding model for your specific data can often be guesswork, but it doesn’t have to be. While general benchmarks provide a baseline, they rarely reflect how a model will perform on your unique datasets and niche terms.
We’ve just posted a course on the freqcodecamp.org YouTube channel that offers a comprehensive, beginner-friendly roadmap to mastering the art of custom benchmarking. Moving beyond standard metrics, you’ll learn how to leverage vision language models for precise text extraction, use LLM to generate synthetic diagnostic data, and apply rigorous statistical tests to determine which model provides the best results for your machine.
In this course, you will learn how to:
Overcome the limitations of standard Python libraries for extracting PDF text by using the Vision Language Model (VLMS).
The class breaks the text into contextual sections.
Create assessment questions for each section using Large Language Models (LLM).
Create vector representations of your data using both open source and proprietary embedding models.
Deploy local models in GGUF format to your machine using LLAMA.CPP.
Benchmark different embedding models using various metrics and statistical tests with the RANX library.
Visualize the vector representation by plotting to see the formation of clusters.
Interpret statistical results, including understanding the significance of p values.
And much more!
View the full course on the freecodecamp.org YouTube channel (4 hour clock)