Build Anything with Llama 3 Agents, Here’s How
Summary
TLDRIn this tutorial, David Andre demonstrates how to build AI agents using the Llama 3 model without extensive programming knowledge or powerful hardware. He utilizes AMA for local model running, VS Code for coding, and Gro for enhanced performance. The video showcases the setup process, from downloading the model to creating agents for tasks like email classification and response. Andre also highlights the potential issues with running the model through crew AI and offers a solution by connecting to the Gro API for improved speed and efficiency. The video concludes with a call to action to join his community for a comprehensive workshop on AI agent development.
Takeaways
- 😀 David Andre introduces a tutorial on building AI agents using the Llama model, which is accessible even for those with limited computer resources and no programming knowledge.
- 💻 The tutorial utilizes AMA for local model running, VS Code for coding, and Gradio for achieving high performance.
- 🚀 David demonstrates the impressive speed of the Llama model, achieving 216 tokens per second, highlighting the potential of the model even on less powerful hardware.
- 📈 The script showcases a comparison between the Llama 370B open-source model and the GPT-4 model, positioning Llama as a competitive choice.
- 🔧 David provides a step-by-step guide to downloading and setting up the Llama model, including instructions for using AMA and VS Code.
- 📝 The tutorial includes a practical example of building AI agents from scratch, focusing on an email classifier and responder scenario.
- 🛠️ David encounters and troubleshoots issues with the Llama model integration in crew AI, offering insights into potential solutions and workarounds.
- 🔗 The script emphasizes the importance of connecting Gradio to a team of agents to leverage the benefits of high-speed AI processing.
- 🔑 A guide is provided on how to securely use API keys with Gradio, including the creation and application of an API key for the Llama model.
- 🌟 The video concludes with a call to action for viewers to join David's community to stay ahead in the AI revolution and learn more about building AI agents.
Q & A
What is the main topic of the video?
-The main topic of the video is teaching viewers how to build AI agents using the new llama free model, even without a powerful computer or programming knowledge.
What tools does David Andre recommend for building AI agents?
-David Andre recommends using AMA to run the models locally, Visual Studio Code (VS Code) for writing the code, and Gro for achieving super-fast performance.
What is the significance of the 'llama fre model' mentioned in the video?
-The 'llama fre model' is an open-source AI model that David Andre uses to demonstrate the creation of AI agents. It's significant because it allows for local running of AI models without needing powerful hardware or cloud-based services.
How does David Andre demonstrate the performance of the AI model?
-David Andre demonstrates the performance of the AI model by showing the number of tokens processed per second, comparing the speed of the larger and smaller versions of the llama model.
What is the 'llm arena' mentioned in the video?
-The 'llm arena' is a place where language models are ranked. In the context of the video, it's used to show that the open-source llama 370b model is better than the proprietary model GBD4.
What is the purpose of the community David Andre mentions?
-The purpose of the community is to provide a step-by-step workshop for building AI agents, even for those who are not programmers, and to connect with others who are interested in staying ahead in AI development.
What is the first step David Andre suggests for setting up the AI model?
-The first step is to download AMA from ama.com and then download Visual Studio Code from code.visualstudio.com.
How does David Andre handle the installation of the AI model in the video?
-David Andre handles the installation of the AI model by showing viewers how to copy a command from the llama models page, open a terminal in VS Code, and run the command to download the model.
What is the issue David Andre encounters when trying to use the llama model through crew AI?
-David Andre encounters an issue where the llama model works perfectly in the terminal but does not seem to work well when run as an agent through crew AI, causing unexpected results and slow performance.
How does David Andre resolve the issue with crew AI and the llama model?
-David Andre resolves the issue by adding the Gro API, which allows him to use the llama model effectively and achieve the desired performance.
What is the final outcome of David Andre's demonstration with the AI agents?
-The final outcome is the successful creation of AI agents that can classify and respond to emails, demonstrating the potential of the llama model and the crew AI framework for building AI applications.
Outlines
💻 Building AI Agents with Llama Model
David Andre introduces a tutorial on constructing AI agents using the Llama Free model, which is accessible even to those with limited computer capabilities and no programming background. The video demonstrates the use of AMA for local model execution, VS Code for coding, and Gro for enhanced performance. David showcases the Llama model's speed, processing 216 tokens per second, and compares it with other models, highlighting its superiority over models like GPT-4. He emphasizes the importance of keeping up with AI advancements and encourages viewers to build AI agents, offering a step-by-step workshop through his community.
🔧 Setting Up the Development Environment
The tutorial continues with setting up the development environment. David guides viewers to download AMA from their official website and VS Code from visualstudio.com. He then instructs on acquiring the Llama model, suggesting the 8 billion parameter version for its balance of size and performance. He demonstrates how to download the model using a command in the terminal within VS Code. Afterward, he shows how to import the Llama model into a Python script using the 'crei' package and sets up a basic Python file to begin coding the AI agents.
📧 Creating Email Classifier and Responder Agents
David proceeds to define the roles and goals for two AI agents: an email classifier and an email responder. The classifier's task is to accurately categorize emails as important, casual, or spam, while the responder's role is to draft responses based on the email's importance. He sets up the agents using the 'crew' library, detailing the process of assigning each agent a model, verbosity level, and delegation settings. David then outlines the creation of tasks for the agents, which involve classifying an email and responding to it accordingly. Despite initial issues with the Llama model's performance within the 'crew' framework, he successfully demonstrates the model's capabilities in a terminal test.
🚀 Integrating with Gro for Enhanced Performance
Facing performance issues with the Llama model in 'crew', David explores an alternative approach by integrating with Gro, a platform known for its API capabilities. He guides viewers through generating an API key from Gro Cloud and setting up the necessary environment variables for API integration. After modifying the script to use Gro's API, he successfully runs the AI agents, achieving a significant improvement in speed and performance. David concludes by reflecting on the rapid advancements in AI and the importance of staying updated with the latest technologies, inviting viewers to join his community for further exploration and learning.
Mindmap
Keywords
💡AI agents
💡Llama fre model
💡AMA (Agent Management API)
💡VS Code
💡Gro
💡LLM (Large Language Model)
💡API (Application Programming Interface)
💡Email Classifier
💡Crew AI
💡Community
Highlights
Introduction to building AI agents using the llama free model without programming knowledge.
Utilization of AMA for local model running and VS Code for coding.
Achieving high performance with Gro and comparing it with the big model.
Demonstration of instant response with the smaller llama free 8 billion model.
Instruction on connecting Gro to a team of agents for enhanced speed.
Llama 370b, an open-source model, outperforming gbd4 in the llm arena.
Urging the audience to build AI agents to stay ahead in the AI revolution.
Step-by-step workshop to build AI agents from scratch.
Downloading AMA and VS Code as prerequisites for the project.
Selection of the llama 3 model for its balance between size and performance.
Downloading the model using a command in the terminal.
Quick tip on ending chats without killing the terminal in VS Code.
Importing the llama model in a Python file using the LangChain community package.
Setting up the email classifier agent with a specific goal and backstory.
Creating a responder agent to write email responses based on classification.
Defining tasks for the agents within the crew AI framework.
Troubleshooting the integration of llama 3 with crew AI and finding a solution.
Adding the Gro API for improved performance on lower-end computers.
Demonstration of the significant speed improvement with the Gro API.
Invitation to join the community for those interested in cutting-edge AI.
Transcripts
my name is David Andre and I'll show you
how to build AI agents using the new
llama fre model even if you have a bad
computer and know nothing about
programming now we're going to be using
AMA to run the models locally vs code to
write the code and Gro to achieve super
fast performance and look at this and
now boom 216 tokens per second now this
is the big model let's try the smaller
one Lama free 8 billion and let's do
tell me a long joke
like that was instant so at the end of
this video I'll show you how to connect
Gro to your team of agents that way you
can get the benefits of this insane
speed now check this out this is the llm
arena where language models are ranked
llama 370b an open source model is
better than gbd4 the future is here if
you aren't doing anything about it if
you aren't building AI agents you're
falling behind so I urge you to take the
first step and build something to make
this as easy as possible for you I've
built an entire Workshop that will teach
you step by step how to build AI agents
even if you're not a programmer you can
get it inside of my community the link
will be in the description so let's get
to building agents I was planning on
building on top of a previous project
but then I realized I think you'll get
more value if I build everything from
scratch so the basics right go to
ama.com and hit download then you need
vs code go to code. visual studio.com
and download it obviously the next thing
you need is the model so so if we go to
all Lama click on models and select
llama 3 obviously I would recommend
going with 8 billion model which is
already really good so copy this command
just click on here copy it go to your
terminal and if you don't have u k
that's okay in vs code just click on
Terminal and do new terminal and you
should be able to run the AMA right here
right yeah exactly the first time you
run this obviously you won't be able to
run the LM you cannot just be like hey
llama because you haven't downloaded the
model yet so the first time do this it
will start downloading the model now 4.7
GB the smaller one so it will take like
20 minutes the bigger one though if you
have a really good PC this one is 40 GB
so this will take like 3 hours probably
either way you have to download it the
first time you use it right which is
fine because you only have to do this
once by the way quick tip whenever
you're in a chat and you want to end it
just go SL bu and you don't have to kill
the terminal each time so we run this
and this is just to chat with the llm
locally and that's already pretty good I
mean most people can't do that but
honestly we're not interested in that
this is kind of a basic we want to build
AI agents with Lama fre so let's go into
vs Studio by the way if you're not
subscribed Please Subscribe and I
created just an empty file you know
clicking right clicking new file naming
it main.py just an empty python file
literally as simple as that so what we
need to do is we need to import AMA so
go from Lang
chain community. llms import or Lama and
as you can see it's highlighted meaning
we have not installed it I have to be in
the correct um environment okay so yeah
if you have not installed this package
yet don't worry just go in here go into
your terminal by the way again you can
do this straight in vs code just go into
the terminal make sure you're in the
right environment I mean you can just
use the base environment if you want and
just type in PIP install crew AI
literally just type that and it will
install everything and as you can see I
already have it if you don't have it it
will take like 20 seconds so don't worry
inside of crei um you'll get the L chain
Community package which is what we'll
use to import o Lama next we need crew
AI so from crew AI we need to import
some things we need to import agent task
crew and process that's it those are the
two Imports we need next thing we need
to uh decide the model so I'm just going
to use a variable and O Lama we have to
do model
equals and put the name of the model as
it named in ama if you're using the
default version this the 8 billion just
type in Lama 3 if you want to use the
70b version um then do llama free 70b so
I'm just going to do llama free because
um faster it's faster so yeah and now I
need to decide what type of Agents I
want to build and I'll just keep this
simple that way all of you can follow
along and what I came up with is a
simple email classifier so we'll go the
first agent will receive receive a
random email and classify it and based
on the classification the second agent
will write a response so let's create a
new variable named email and this is
just going to be a simple string and I'm
going to do a random one uh let's do the
classic Nigerian PS sending some gold
let's see if the llms are smart enough
to recognize this is a spam by the way
all of the code and prompts from this
video will be linked below the module so
another reason to join the community
anyways let's create our first agent and
this is going to be
classifier and we're going to do agent
like this beautiful and first we need to
give it a role which is basically just a
second name right and this is going to
be email
classifier next we need to give it a
goal so what is what will this agent do
what's his main goal goal equals
accurately classify emails based on
their importance give every email one of
these ratings
important casual SP or spam and then we
need to give it a backstory and I'm just
going to keep it super simple that way
this is not a 2hour video the next thing
I need to do is set verbos so ver both
equals true this will let us see the
flow of the messages inside of the
terminal next we do allow delegation
equals false because we don't want this
agent delegating this task to somebody
else else and lastly and this is super
important we need to assign the llm
which is model if we don't do this it
will automatically default to gp4 which
will not work because we haven't
provided the API for open AI but we
don't want gbd4 we want llama free and
that's our agent done crew AI makes this
super simple next agent is going to be
responder so responder equals agent and
then we basically do the same but for an
email responder and again kab copal just
Sav me some time based on the importance
of the email write a concise and simple
response all right we're both true
beautiful all of this is the same so now
we have our agents created super simple
now we need to define the task so first
task I'm just going to name classify
email equals task and creating tasks
inside of crei is even simpler than
creating agents first we do a
description description equals and then
I'm going to do an F string and the
reason for that is because I want to
enter the variable we've created right
for the email so F strings in Python
just allow you to insert the variable
inside of a string agent equals
classifier and then expected output one
of these three options import on casual
or spam yes beautiful so that's our
first task created then we're going to
create a second task respond to email
okay
exactly beautiful description respond to
the email now we have created both of
the tasks next step is to define the
crew crew equals crew and first we need
to assign the agents exactly a list of
Agents so just create a list and enter
all of the agents you have can be two
can be three can be 10 it's up to you
then we do tasks and be careful because
I used to put task here and this will
not cause an error but it will cause
your crew not to work verbos is
different here it's either one or two
I'm going to do two and then process
equals process. sequential and the last
thing we need to do is output equals
crew. kickoff okay and then obviously I
need to print it out to see it and now
this should work fingers crossed let's
see it do we have a team of llama free
agents working or
not okay this is promising let's look at
the activity
monitor so we have memory used 36 it
went up from like 30 which is means that
the llamas 3 8 billion uses like 3 to 6
GB of RAM which is very good news
because most of you can run it but this
is bad news
because it's probably going to include a
bunch of Chunk it should not be this
slow it should be much faster and I'm
suspecting that we're going to see a
bunch of hallucinations oh my God what
is this so I don't know why this is
happening but inside of crew AI
llama 3 does not seem to be working well
it works perfectly in the terminal but
some like somehow through crew AI it's
uh not really working well I mean I can
try verbose false it's not the issue of
the model clearly like look at this this
is perfect right look at this the model
said a classic rating spam the Nigerian
pen is a welln example of a fishing scam
yeah so perfect I mean it gave it the
rating exactly as I wanted but somehow
running it as a agent through crew AI is
causing troubles I have no clue why so
we tested this now I'm going to show you
how to add the gro API so if you have a
bad computer and you want to use the API
I'm going to show you how so okay let's
go Gro and let's go Gro cloud and let's
go API Keys create API key I'm just
going to do llama 3 submit and I'm going
to delete this before posting the video
you know never share API keys with
anybody
I'm going to do Gro API key equals let's
go into documentation see how we connect
it let me duplicate this rename cuz we
want to do Gro so now this is for the
gro right so we delete AMA and okay
let's follow along with this we can just
probably delete this all together
honestly following the GitHub official
GitHub from crei might be the move right
so we need to assign these three
variables and just make a default right
so open a API
base always that environment yes so we
need to import OS we copy this we need
these three lines and we actually need
them not commented out beautiful let's
go to Gro
documentation and open a compatibility
and the base URL copy this API base
equals model name let's copy this
again and the API key test Moment of
Truth oh my god it works it works and
look at the speed though let's try the
different email and let's do it look at
the
speed okay what am I doing okay so I'm
just going to explain what I did so I'm
I was completely trolling the answer was
right in front of my eyes right here
inside of the main GitHub from crew AI I
just needed to assign these three
variables right and the reason why I
deleted the llm is because crew AI is
automatically made to use the default
one so like this is like basically
hidden right something like this is
hidden and you don't have to do that if
you're using the open endo and guys
let's just appreciate the speed one more
time like this is an API look at this
insane this is the AI Revolution we are
watching the the AI Revolution happened
right in front of our eyes unfortunately
99% of people will miss out on the AI
Revolution and I don't want you to be
one of them the only way to stay ahead
is to surround yourself with people who
are at The Cutting Edge of AI which is
exactly why I created the community I'm
looking for people who want to take AI
seriously so if that sounds like you
make sure to join for this link in the
description
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