So why did we put together this course?
Well the state of the art in generative AI is very rapidly advancing and there's a huge
amount of interest in creating tailored models for specific use cases and one of the the main
challenges is that there isn't really a single model that's ideal for everything. You need quite
a bit of work to find the best model to use in practice or perhaps to build your own if you
want to balance dimensions like privacy, quality, cost, and latency of serving and using that model.
And as a result, there's a huge amount of work on developing uh new uh large language models.
There are many really successful proprietary LLMs that are being offered as a service from
various companies like Anthropic and ChatGPT and PaLM-2 and there are also quite a few
open source LLMs being built by researchers and companies such as Dolly from Databricks,
Mosaic MPT, StabilityAI's models, lots of models on Hugging Face and so on. And it
can seem a little bit overwhelming. There are so many ways to get these models so we want
to have a course on the basics of what goes into them and what goes into applying them
to help you eventually you know build your own very high-quality applications uh and models.
So one of the things that we're very excited by is that uh you know while while building high-quality
large language model was uh you know kind of a mysterious uh something that very few people knew
about a few years ago. Today with open source um the the quality of uh freely available models is
rapidly advancing and also the amount of knowledge out there on how to do this well um is uh is
really going fast. So we're really excited to be able to use some of that in this course and tell
you a little bit about what's gone into recent open source models and some of the techniques that
the research community as a whole is finding uh to build great language models and applications.
So just as a history of some of the recent open source models, a lot of this started with models
that were not usable commercially at first when Meta or Facebook released their Llama model
at the beginning of 2023 and that got a lot of researchers to start working on it. For example a
group at Stanford produced a model called Alpaca, which is a version of Llama that's tuned to be
good at chat and instruction following, which makes it much more useful in many applications.
And then other groups started creating models that are fully commercially licensed including
Dolly from Databricks, where we produced uh you know a commercially usable data set to get a
model to talk with you and follow instructions; MPT from MosaicML where they did both the the
pre-training and instruction-following using open data sets; Falcon from TII,
which is a larger open source model and many others that have come out since then so as part
and and in fact in you know recently just a few weeks ago from when we're recording the course,
uh Facebook even released Llama2, which is now commercially usable by many companies, basically
companies below a certain size. Um so definitely the the amount of knowledge about how to build
LLMs uh and the amount of people experimenting with them and trying out new techniques has been
rapidly growing and this means that more and more people can get state of the art results
with these or you can make meaningful changes to them to make them better for their application.
The other exciting thing is that people are also developing common ways to evaluate these models.
So for example, Hugging Face has this uh open LLM leaderboard where they look at different
tasks like if you want your model uh to be good at programming in Python or answering questions based
on a passage or just have general world knowledge or things like that and so you can also get these
evaluations out there and this is just one of many uh you know that exists that can help with this
and these are helping everyone in the community better understand how their models are doing and
build uh you know build even better ones over time and compare them with what's available out there.
So again as I said um you know at the beginning it might seem that
um you know all the activity happening in LLMs today is a little bit overwhelming but the
main thing we want to get at in this course is the foundations and we think if you understand
the foundation of what how these models actually run, uh what goes into pretraining data for them,
what goes into inference, what are the available options at each stage um you everything else
that's out there, it's sort of small variations on these basics so you don't need to understand you
know the exact differences between say MPT and Falcon or you know the latest version of Llama
or something like that. If you understand the basics you'll be in a great spot um to uh to build
powerful applications and models based on them. So we have a course here on the fundamentals of LLMs
which really haven't changed much since 2018 when the first um you know kind of modern transformer
models came out and some of the key variations on the original that are letting people do really
exciting things today and basically, even though there are now thousands of um you know machine
learning research papers published per month, many of them about LLMs, you don't have to read
all of them uh to do something meaningful with these models and to build exciting applications.
So we hope you enjoy this course we've curated together you know
what we thought were the most important topics in this field
as well as lots of hands-on material to let you try these things out yourself