For the uninitiated, this is the same author as the many other "The Illustrated..." blog posts.
A particularly popular one: https://jalammar.github.io/illustrated-transformer/
Always very high quality.
A particularly popular one: https://jalammar.github.io/illustrated-transformer/
Always very high quality.
Looks interesting, but I'm skeptical that a book can feasibly stay up to date with the speed of development.
The basic structure of the base models has not really changed since the first GPT launched in 2018. You still have to understand gradient descent, tokenization, embeddings, self-attention, MLPs, supervised fine tuning, RLHF etc for the foreseeable future.
Adding RL based CoT training would be a relatively straightforward addendum to a new edition, and it's an application of long established methods like PPO.
All "generations" of models are presented as revolutionary -- and results-wise they maybe are -- but technically they are usually quite incremental "tweaks" to the previous architecture.
Even more "radical" departures like state space models are closely related to same basic techniques and architectures.
funny mentioning the math but not the Transformer encoders..
In any case, self-attention and MLP is the crux of Transformer blocks, be they in the decoder or the encoder.
references, please