Hello folks! Wanted to share a new blog post with ya'll. Below are the details:

​An Open Course on LLMs, Led by Practitioners​

Published on July 29, 2024

Today, we are releasing Mastering LLMs, a set of workshops and talks from practitioners on topics like evals, retrieval-augmented-generation (RAG), fine-tuning and more. This course is unique because it is:

• Taught by 25+ industry veterans who are experts in information retrieval, machine learning, recommendation systems, MLOps and data science. We discuss how this prior art can be applied to LLMs to give you a meaningful advantage.
• Focused on applied topics that are relevant to people building AI products.
• Free and open to everyone .

We have organized and annotated the talks from our popular paid course.1 This is a survey course for technical ICs (including engineers and data scientists) who have some experience with LLMs and need guidance on how to improve AI products.

Speakers include Jeremy Howard, Sophia Yang, Simon Willison, JJ Allaire, Wing Lian, Mark Saroufim, Jane Xu, Jason Liu, Emmanuel Ameisen, Hailey Schoelkopf, Johno Whitaker, Zach Mueller, John Berryman, Ben Clavié, Abhishek Thakur, Kyle Corbitt, Ankur Goyal, Freddy Boulton, Jo Bergum, Eugene Yan, Shreya Shankar, Charles Frye, Hamel Husain, Dan Becker and more

Getting The Most Value From The Course

Prerequisites

The course assumes basic familiarity with LLMs. If you do not have any experience, we recommend watching A Hacker’s Guide to LLMs. We also recommend the tutorial Instruction Tuning llama2 if you are interested in fine-tuning 2.

Navigating The Material

The course has over 40 hours of content. To help you navigate this, we provide:

• Organization by subject area: evals, RAG, fine-tuning, building applications and prompt engineering.
• Chapter summaries: quickly peruse topics in each talk and skip ahead
• Notes, slides, and resources: these are resources used in the talk, as well as resources to learn more. Many times we have detailed notes as well!

To get started, navigate to this page and explore topics that interest you. Feel free to skip sections that aren’t relevant to you. We’ve organized the talks within each subject to enhance your learning experience. Be sure to review the chapter summaries, notes, and resources, which are designed to help you focus on the most relevant content and dive deeper when needed. This is a survey course, which means we focus on introducing topics rather than diving deeply into code. To solidify your understanding, we recommend applying what you learn to a personal project.
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​Course Website​

Footnotes

1. https://maven.com/parlance-labs/fine-tuning. We had more than 2,000 students in our first cohort. The students who paid for the original course had early access to the material, office hours, generous compute credits, and a lively Discord community.↩︎
2. We find that instruction tuning a model to be a very useful educational experience even if you never intend to fine-tune, because it familiarizes you with topics such as (1) working with open weights models (2) generating synthetic data (3) managing prompts (4) fine-tuning (5) and generating predictions.↩︎
3. These testimonials are taken from https://maven.com/parlance-labs/fine-tuning.↩︎

​Read more...​