This is an automated archive made by the Lemmit Bot.

The original was posted on /r/machinelearning by /u/Thomjazz on 2024-03-28 17:26:57.

I finally recorded this lecture I gave two weeks ago because people kept asking me for a video.

So here it is, I hope you’ll enjoy it “A Little guide to building Large Language Models in 2024”.

I tried to keep it short and comprehensive – focusing on concepts that are crucial for training good LLM but often hidden in tech reports.

In the lecture, I introduce the students to all the important concepts/tools/techniques for training good performance LLM:- finding, preparing and evaluating web scale data- understanding model parallelism and efficient training- fine-tuning/aligning models- fast inference

There is of course many things and details missing and that I should have added to it, don’t hesitate to tell me you’re most frustrating omission and I’ll add it in a future part. In particular I think I’ll add more focus on how to filter topics well and extensively and maybe more practical anecdotes and details.

Now that I recorded it I’ve been thinking this could be part 1 of a two-parts series with a 2nd fully hands-on video on how to run all these steps with some libraries and recipes we’ve released recently at HF around LLM training (and could be easily adapted to your other framework anyway):

• datatrove for all things web-scale data preparation:
• nanotron for lightweight 4D parallelism LLM training:
• lighteval for in-training fast parallel LLM evaluations:

Here is the link to watch the lecture on Youtube: And here is the link to the Google slides:

Enjoy and happy to hear feedback on it and what to add, correct, extend in a second part.