If your weekend plans include catching up on AI developments and understanding Large Language Models (LLMs), I've prepared a 1-hour presentation on the development cycle of LLMs, covering everything from architectural implementation to the finetuning stages.
The presentation also includes an overview and discussion of the different ways LLMs are evaluated, along with the caveats of each method.
Below, you'll find a table of contents to get an idea of what this video covers (the video itself has clickable chapter marks, allowing you to jump directly to topics of interest):
00:00 – Using LLMs
02:50 – The stages of developing an LLM
05:26 – The dataset
10:15 – Generating multi-word outputs
12:30 – Tokenization
15:35 – Pretraining datasets
21:53 – LLM architecture
27:20 – Pretraining
35:21 – Classification finetuning
39:48 – Instruction finetuning
43:06 – Preference finetuning
46:04 – Evaluating LLMs
53:59 – Pretraining & finetuning rules of thumb
It's a slight departure from my usual text-based content, but if you find this format useful and informative, I might occasionally create and share more of them in the future.
Happy viewing!
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