The Ollama Course: Intro to Ollama
Summary
TLDRThis introductory video course on Olama guides viewers through the basics of setting up and using the AI tool. Starting with the installation and verification of Olama, the tutorial covers downloading models, experimenting with prompts, and navigating the platform's interface. It also delves into the concept of models, their components, and the significance of quantization in reducing memory requirements. The video promises more in-depth content in upcoming lessons and encourages users to join the Discord community for support and discussions.
Takeaways
- π The video is an introductory course to Olama, a tool with various capabilities that the course will explore.
- π Olama's official website is ama.com, accessible also via the short URL ol.a, which is a hub for community, documentation, and model downloads.
- π¬ The Discord link on the website is a place for users to ask questions and get support for Olama.
- π§ GitHub houses the source code and documentation for Olama, but for support issues, Discord is preferred over GitHub issues.
- π The search feature on the website helps users find both official and community-contributed models.
- π₯ Downloading Olama is straightforward, with options for Mac, Linux, and Windows platforms.
- π οΈ After installation, users can verify Olama is running by using the 'olama run' command, which also downloads necessary model layers.
- π§ A model in Olama consists of weights and biases, representing connections between nodes that form the basis of its knowledge base.
- π Model parameters can be quantized to reduce the size of the model file, making it more accessible with less VRAM required.
- π The REPL (Read Eval Print Loop) in Olama allows for interactive coding and immediate responses to entered questions.
- π Olama models can be switched and managed using commands like 'olama LS', 'olama PS', and 'olama RM'.
- π Third-party UIs like open web UI and Misty offer enhanced ways to interact with Olama, including better memory management for longer conversations.
Q & A
What is the purpose of the free course mentioned in the video?
-The purpose of the free course is to help users get up to speed on what Olama is all about, covering various aspects of Olama and what can be done with it.
What is the first step in getting started with Olama?
-The first step is to visit the ama.com web page or ol.a, which are the URLs for Olama.
What are the different resources available on the ama.com web page?
-The ama.com web page provides links to Discord for community support, GitHub for source code and documentation, a search box for finding models, community models, and links to documentation and meetups.
Why is it recommended to ask questions on Discord instead of GitHub issues?
-Discord is for general questions and support, while GitHub issues are meant for reporting actual problems in the project. It's best to start with Discord and escalate to GitHub if necessary.
How can you download Olama?
-You can download Olama by clicking the download link on the ama.com website, which provides options for Mac, Linux, and Windows.
What is the significance of the model '53' in the video?
-The model '53' is chosen because it is short, easy to spell, and small, allowing for quick setup and running of Olama.
What is a model in the context of Olama?
-A model in Olama is made up of various components, primarily a weights file, which contains nodes and their connections (weights and biases). These parameters connect different concepts together as the model is trained.
What is the concept of 'quantization' in the context of Olama models?
-Quantization refers to the process of representing each parameter in a model with fewer bits, such as 4-bit quantization, which reduces the size of the model and makes it more accessible in terms of memory requirements.
What is the 'reppel' and how is it used in Olama?
-The 'reppel' is a read-eval-print loop, an interactive coding concept where users can enter code or questions and get immediate responses from the Olama model.
How can users continue conversations with Olama models beyond the default context window?
-Users can work with Olama through third-party UIs like Open Web UI or Misty, which may offer better ways of leveraging memory to continue conversations for longer periods.
How can users manage different models in Olama?
-Users can manage models using commands like 'olama LS' to list models, 'olama PS' to show loaded models, and 'olama RM' to remove a model.
Outlines
π Getting Started with Olama: Installation and Basics
Matt introduces the Olama platform, outlining the course's goal to familiarize users with its capabilities. The first video focuses on basic setup, including visiting the official website, installing the software, and verifying its operation. It explains how to access the Discord for support, GitHub for source code and documentation, and the search feature for models. The process of downloading a model, starting the Olama service, and understanding the concept of 'layers' in the model is also covered. The paragraph concludes with an explanation of what a model is, its components, and the significance of parameter quantization in reducing the memory footprint of AI models.
π Exploring Olama Models and Advanced Features
This paragraph delves into the interactive aspect of Olama through its read-eval-print loop (REPL), where users can ask questions and receive immediate answers. It discusses the concept of 'tokens' in AI, the context window limitations, and the use of third-party UIs to enhance user experience. The video script then guides viewers on how to find and download additional models from the Olama website, highlighting the features and variants of the 'Intern LM' model, which is designed to be proficient in math reasoning. The paragraph also explains the process of running a model, the importance of quantization levels, and provides a brief overview of commands and keyboard shortcuts available in the REPL. Lastly, it covers how to list, load, and remove models within Olama, and teases upcoming course content.
Mindmap
Keywords
π‘Olama
π‘Model
π‘Discord
π‘GitHub
π‘Weights and Biases
π‘Quantization
π‘REPPLE
π‘Token
π‘Context Window
π‘CLI (Command Line Interface)
π‘VRAM
Highlights
Introduction to Olama and its capabilities in the free course.
Basic steps to get started with Olama: installation, verification, downloading models, and using prompts.
Visiting the ama.com webpage for resources and community engagement.
Accessing Discord for questions and GitHub for source code and documentation.
Using the search box on ama.com to find official and user-contributed models.
Downloading Olama and choosing the appropriate platform (Mac, Linux, Windows).
Running Olama with the 'olama run' command and handling model downloads.
Understanding the concept of models in Olama, including weights, biases, and parameters.
Explaining the role of nodes and their connections in a model's learning process.
Discussing model quantization and its impact on VRAM requirements.
Entering the REPL (Read Eval Print Loop) for interactive code processing.
Asking questions in the REPL and receiving immediate model-generated answers.
Exploring third-party UIs for better memory leverage and extended conversations.
Navigating to the ama.com website to explore and download different models.
Understanding model variants and their quantization levels.
Running a new model command in the terminal to experience different model responses.
Using 'olama LS' to list models and 'olama PS' to see loaded models.
Removing models using 'olama RM' and understanding model memory management.
Anticipation for the next video in the course and invitation to join the Discord community.
Transcripts
hi I'm Matt I want to help you get up to
speed on what olama is all about in this
free course you're going to learn all
the different aspects of olama and what
you can do with it this first video will
just get you started in the most basic
way we'll install a llama verify it's
running download a model try out a
prompt and find and download another
model it's not going to be everything
but that'll come as the rest of the
course is released so let's get started
first first thing we want to do is visit
the ama.com web page you can also get to
this by going to ol. a because all the
cool kids have a URLs okay let's take a
quick look at what's on this page at the
top we have a link to the Discord you
can join and ask any questions you have
and you'll probably get a decent answer
GitHub has the source code and
documentation for olama which you can
review but if you have an issue with
olama it's best to keep the question
questions on the Discord and not the
GitHub issues GitHub issues are for
actual problems in the project and not
really support issues start out the
question in the Discord and if you need
to escalate GitHub is a great place to
go the search box will let you search
for both official models and user
contributed models we'll talk more about
models soon next to that is the link to
the community models since I'm logged in
you can see my username on AMA this is
is all model related and we'll come back
to
that down at the bottom we can see a
link to the docs which is just a folder
on the GitHub one other interesting link
is to meetups and these are events held
around the world with the AMA team keep
an eye out there may be one close to you
at some point in the future so right in
the middle is a link to download olama
click that to get three choices Mac
Linux and windows I'll go into more
detail on this in another video but just
choose your platform and follow the
instructions I'm on a Mac right now so
I'll click the download button and then
run the installer so once it's installed
there are a few different things you can
do to ensure that olama is running the
easiest is just to run olama run 53
that's f as in Phi and the number three
the reason I chose that model is that
it's short and easy to spell and small
so we can be up and running quickly you
probably don't have the model so you'll
see it download the various layers of
the model you'll learn more about layers
later in this course if you're on a Mac
or Windows and the olama service wasn't
running just running olama run will
start up that service if however you're
on Linux and the olama Run command fails
you may not have the service running you
can refer to this page to get a little
bit more information about how to get it
started it's always best to let the
service run that piece rather than
running it locally in a command prompt
that you start so at this point you may
still have to wait a little bit longer
for that model to download so let's talk
for a moment about what a model is a
model is made up of a number of pieces
the biggest of which is the weights file
this is a collection of nodes and they
have connections between them called
weights and biases those weights and
biases combined are referred to as
parameters a node is often a a concept
maybe a word or a phrase and when the
model is trained the parameters connect
each of these different concepts
together by different amounts and
sometimes they get a little closer and
other times they get a little further
away as the model is trained more and
more two nodes won't just have one
weight between them they might have many
combinations of Weights depending on the
context of what the node does although
it feels like magic this is how much of
the world's knowledge can be stuffed
into a relatively tiny little file how
big that file is depends on how the
parameters are represented when the file
is originally developed it's probably
going to use 16 or 32-bit floating Point
numbers these can be incredibly big and
precise but if we group those numbers
into smaller sets we can abstract them
down to much smaller numbers while
retaining an incredible amount of
precision the most common amount is four
and that's what's referred to as 4-bit
quantization
there'll be a more advanced video in
this course that goes into a lot more
detail about quantization in the future
when each parameter is represented by a
32-bit number llama 38b or 8 billion
parameters will take roughly 32 gigs of
vram to run because there are eight bits
in a bite four bytes perameter so 8
billion Time 4 adds up to roughly 32 GB
there's some extra overhead as well but
that's the simple way of calculating it
if we quantize to 4 bits per parameter
that gets close to four to 5 GB of vram
required which is a whole lot more
accessible there are a few other
components to the model and we'll cover
that later in this course so after all
that your model should be downloaded and
ol will have dropped you into the
repple reppel is a coding concept and
means read eval print Loop this is a
place that you can enter some code
interactively and it'll be processed
right away and in the ol reppel we can
enter a question and get it answered
immediately so try asking a question why
is the sky blue and within a few seconds
the model will spit out or generate an
answer the answer is streamed out token
by token a token is a word or common
part of a word and there are a number of
factors that go into how long that
generation will take you can continue
the conversation and the model will
remember much of what was said limited
by the size of the context window that
the models supports often this contact
size is 248 tokens by default inama
models but that's easily modifiable if
your conversation goes longer than 248
tokens the model will start to forget
the earlier parts of the
conversation and if you restart the CLI
or reppel that entire history will be
wiped often users will work with olama
through a thirdparty UI open web UI is a
common one as is Misty and so many
others one thing some of the uis offer
is better ways of leveraging memory so
you can continue those conversations for
longer we'll see that in future topics
now back at the command line type
slby to exit out of the reppel let's go
to the ama.com website and click on
models right now the list of models is
sorted by featured try sorting by newest
one of the more recent models at the
time of this recording is intern LM
which attempts to be better at math and
math reasoning that's not actually
saying that much because models tend to
be terrible at these things and aren't
the best tool to use thankfully it's
also good at all the usual things models
do so click on the link for intern LM we
have a few bits of info on this page
first there's a short description of the
model we see how popular the model is as
well as how recently it was updated then
there's a drop down with different
variants of the model it defaults to the
most common one which will be a four bit
quantize model to the right is the
command to run to get this model below
that is the hash of the model and the
overall size below that we see the
various layers of the model there's that
layer term again and there will be more
on that later in this course in the drop
down with the different tags or variants
find the one that is 7B chat B 2.5 Q2 _
K so copy the command to run this model
and paste it into the terminal if you're
still running 53 then type slby to exit
and then run that command you'll see it
download the model which is a bit larger
than the last one when it's done try
asking what is a black hole and soon
after you will get an answer describing
a black hole in a way that's a little
different from 53 style but what's most
incredible about this is that this one
has been quantized from the original
32-bit floating Point number to a TW bit
quantization you will usually see much
better answers from the 4-bit model but
it's pure magic this even works at all
while we're still in the repple type
slash question mark you'll get a list of
all the commands you can run then try
typing SL Mark shortcuts this shows us
different keyboard shortcuts you can use
in the reppel though I still prefer
exiting with slby so exit the reppel
however you prefer now type olama LS to
see a list of your two models olama PS
will show us which models if any are
currently loaded models stay in memory
for 5 minutes by default and several can
be loaded at once depending on your
Hardware we'll look at concurrence in
more detail in a future video if you
want to remove one of the models you can
use olama RM and the model
name there is so much more you can do
with AMA but this video is already long
enough watch out for the next video in
this course coming in the next few days
if you have any specific questions about
what's covered in this course join us on
a brand new Discord that you can find at
this URL thanks so much for watching
goodbye
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