The Ollama Course: Intro to Ollama

Summary

This 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

• 😀 The video is an introductory course to Olama, a tool with various capabilities that the course will explore.
• 🔗 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.
• 💬 The Discord link on the website is a place for users to ask questions and get support for Olama.
• 🔧 GitHub houses the source code and documentation for Olama, but for support issues, Discord is preferred over GitHub issues.
• 🔍 The search feature on the website helps users find both official and community-contributed models.
• 📥 Downloading Olama is straightforward, with options for Mac, Linux, and Windows platforms.
• 🛠️ After installation, users can verify Olama is running by using the 'olama run' command, which also downloads necessary model layers.
• 🧠 A model in Olama consists of weights and biases, representing connections between nodes that form the basis of its knowledge base.
• 📊 Model parameters can be quantized to reduce the size of the model file, making it more accessible with less VRAM required.
• 📝 The REPL (Read Eval Print Loop) in Olama allows for interactive coding and immediate responses to entered questions.
• 🔄 Olama models can be switched and managed using commands like 'olama LS', 'olama PS', and 'olama RM'.
• 🌐 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

00:00

🚀 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.

05:03

📚 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.

Keywords

💡Olama

Olama is the central subject of the video, which appears to be a software or platform being introduced in the course. It is implied to be a complex system with various functionalities and models that users can interact with. The script guides viewers through the basic steps of getting started with Olama, including installation and model interaction.

💡Model

In the context of the video, a 'model' refers to a component within the Olama system, likely an AI model with specific capabilities. Models are made up of various layers and parameters, and they can be downloaded and interacted with through the Olama interface. The script mentions different models like '53' and 'Intern LM', each with unique characteristics.

💡Discord

Discord is mentioned as a platform where users can join to ask questions and receive support related to Olama. It serves as a community hub for users to engage with one another and the developers, highlighting the collaborative aspect of the Olama ecosystem.

💡GitHub

GitHub is referenced as the location for the source code and documentation of Olama. It is a platform where developers can contribute to the project and report issues. However, the script differentiates between using GitHub for actual project issues and Discord for support queries.

💡Weights and Biases

Weights and biases are fundamental to the concept of a model in the video. They represent the connections and fine-tuning within an AI model, influencing how different concepts or nodes within the model relate to one another. The script explains that these parameters are crucial for the model's training and functionality.

💡Quantization

Quantization is a process mentioned in the script that involves reducing the precision of the numbers used to represent the model's parameters. It is used to make models more accessible by reducing the amount of VRAM (Video RAM) required to run them, trading off some precision for efficiency.

💡REPPLE

REPPLE, standing for Read Eval Print Loop, is an interactive coding environment within Olama where users can input commands or questions and receive immediate responses. It is showcased in the script as a way to interact with the models and demonstrates the system's interactivity.

💡Token

A 'token' in the script refers to a unit of text, such as a word or part of a word, that the model generates in response to a query. The generation process is described as streaming out token by token, highlighting the incremental nature of the model's responses.

💡Context Window

The context window is the amount of conversation history that the model can remember during an interaction. It is limited in size, with the script mentioning 248 tokens as the default for Olama models, affecting the continuity and depth of the conversation that can be maintained.

💡CLI (Command Line Interface)

The CLI is the textual interface through which users interact with Olama, as demonstrated in the script when the user types commands to exit the REPPLE or to list models. It is a common way to interact with software, providing a direct and flexible means of control.

💡VRAM

VRAM, or Video RAM, is the memory used by the graphics processing unit (GPU) to store image data. In the context of the video, it refers to the amount of memory required to run the AI models within Olama, with quantization affecting the VRAM usage.

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