How to Fine Tune GPT3 | Beginner's Guide to Building Businesses w/ GPT-3
Summary
TLDRIn this instructional video, Liam Motley guides entrepreneurs through the process of fine-tuning GPT-3 using NBA player performance data. He demonstrates how to prepare data, create prompt-completion pairs, and execute the fine-tuning process to build a customized AI model. The tutorial covers technical steps, including using Python scripts and the OpenAI API, and emphasizes the importance of understanding AI model fine-tuning for business opportunities in the AI industry.
Takeaways
- 📚 The video provides a step-by-step guide on how to fine-tune GPT-3 using specific data sets, such as NBA player performance data.
- 🛠️ Fine-tuning GPT-3 is a significant opportunity for entrepreneurs in the AI industry, allowing them to build businesses and applications tailored to their needs.
- 💡 Understanding the fine-tuning process is crucial for entrepreneurs looking to leverage AI models for business advantages.
- 📈 The script walks through the process of downloading and preparing data, creating prompt and completion pairs, and using them to fine-tune a model.
- 🔍 Data is manipulated in Google Sheets and then converted into a CSV format for use in the fine-tuning process.
- 📝 The script demonstrates how to use Python scripting to automate the creation of prompt and completion pairs from a CSV file.
- 🔑 An API key from OpenAI is required to access the fine-tuning functionality and to interact with the GPT-3 model.
- 📝 The video script includes a method to generate a Python script that automates the process of creating prompt and completion pairs, making it scalable.
- 🖥️ The script details the use of Visual Studio Code for editing and running the Python script that formats the data for fine-tuning.
- 🔧 The video mentions the use of a GUI for interacting with the fine-tuned model, allowing users to input prompts and receive responses.
- 🚀 The presenter emphasizes the importance of having a deep understanding of the fine-tuning process to stay ahead in the competitive AI industry.
Q & A
What is the main topic of the video?
-The video is about the step-by-step process of fine-tuning GPT3 using a dataset, specifically NBA players' performance data, to create a fine-tuned AI model for entrepreneurial purposes.
Why is fine-tuning GPT3 considered a significant opportunity in business?
-Fine-tuning GPT3 is seen as a significant opportunity because it allows entrepreneurs to leverage the power of advanced AI models to build on top of them and create valuable businesses.
What is the first step in the fine-tuning process as described in the video?
-The first step is to find a set of data, in this case, NBA players' performance data, which will be used to train the GPT3 model.
How is the data manipulated before being used for fine-tuning?
-The data is imported into Google Sheets, where unnecessary rows are removed, filters are applied to remove blanks, and the data is formatted into a CSV file ready for processing.
What tool does the video suggest using to visualize CSV files more easily?
-The video suggests using a tool called 'Rainbow CSV' to visualize and understand the structure of CSV files more easily.
What programming language is used in the script to generate prompt and completion pairs?
-Python is used in the script to generate prompt and completion pairs from the CSV data.
What is the purpose of the script provided by the video?
-The script automates the creation of prompt and completion pairs from the CSV data, which is necessary for the fine-tuning process of GPT3.
What is the significance of the 'Max tokens' parameter in the completion creation process?
-The 'Max tokens' parameter is a safety feature to limit the amount of text generated in a response, which in turn helps to control the usage and cost associated with the API.
Why is it necessary to have a unique suffix in the completions during the fine-tuning process?
-A unique suffix helps differentiate the fine-tuned model's outputs from other texts, ensuring that the model's responses are correctly identified and processed.
What platform is used to interact with the fine-tuned GPT3 model after the process is complete?
-A graphical user interface (GUI) is used to interact with the fine-tuned GPT3 model, allowing users to input prompts and receive responses.
What is the recommended model to use for fine-tuning according to the video?
-The video suggests retraining with the 'DaVinci' model for better text recognition capabilities, after initially training with the 'Curie' model.
What advice does the video give for entrepreneurs looking to understand AI fine-tuning processes?
-The video advises entrepreneurs to invest time in understanding the fine-tuning process to gain a competitive edge, identify data sources, and integrate them into AI models like GPT3 or the upcoming GPT4.
Outlines
📚 Introduction to Fine-Tuning GPT3 for Entrepreneurs
This paragraph introduces a video tutorial focused on fine-tuning GPT3 for beginners, especially entrepreneurs interested in AI. The speaker plans to demonstrate the step-by-step process of fine-tuning a model using NBA player performance data. The tutorial aims to provide a basic understanding of the fine-tuning process, enabling viewers to apply this knowledge to their AI ventures. The speaker emphasizes the importance of understanding these processes to identify opportunities and build valuable businesses in the 'AI Gold Rush'.
🔍 Step-by-Step Guide to Fine-Tuning with NBA Data
The speaker provides a detailed walkthrough of the fine-tuning process using NBA players' performance data. The process begins with downloading the dataset and manipulating it in Google Sheets to prepare it for fine-tuning. The data is then formatted into prompt and completion pairs, which are necessary for training GPT3. The speaker also discusses the creation of a Python script to automate the generation of these pairs, ensuring scalability. The paragraph concludes with the execution of the script to produce a JSON file containing the prompt and completion pairs.
🤖 Fine-Tuning Execution and Model Interaction
This paragraph details the execution of the fine-tuning process, starting with obtaining an API key from OpenAI and installing necessary libraries. The speaker explains how to prepare the data in JSONL format and fine-tune the model using a specific base model. After the fine-tuning process, a unique model name is provided, which can be used to interact with the model through a graphical user interface (GUI). The speaker encounters an issue with the completion output but identifies a fix by adjusting the 'Max tokens' parameter. The paragraph concludes with the speaker retraining the model with a different model for improved results and demonstrating the interaction with the fine-tuned model.
🚀 Conclusion and Encouragement for Entrepreneurs
The final paragraph wraps up the video by summarizing the process of fine-tuning a GPT3 model with a dataset and emphasizes the importance of understanding this process for entrepreneurs. The speaker encourages viewers to explore the provided resources, such as the Google doc with prompts and code, and to engage with the community for further learning. The speaker also highlights the potential of fine-tuned models to answer specific questions about the dataset and suggests that with more data, the model's understanding can become more flexible. The video concludes with an invitation to subscribe for more content on AI entrepreneurship and a reminder of the ongoing opportunities in the AI industry.
Mindmap
Keywords
💡Fine-tuning
💡GPT-3
💡Prompting Completion Pairs
💡NBA Players Performance Data
💡CSV Format
💡JSON
💡API Key
💡Entrepreneur
💡AI Gold Rush
💡Data Manipulation
💡Python Scripting
Highlights
Introduction to a step-by-step video on fine-tuning GPT3 and building an AI startup.
Explanation of the necessity for entrepreneurs to understand fine-tuning processes in the AI gold rush.
Starting with NBA players' performance data set for the fine-tuning example.
Importing and manipulating data in Google Sheets to prepare for fine-tuning.
Downloading the data in CSV format for further processing in Visual Studio Code.
Using Chat GPT to generate a script for creating prompt and completion pairs.
The process of string interpolation and F-strings in Python for automating data processing.
Installing and using the rainbow CSV extension for better CSV visualization.
Running the Python script to generate prompt and completion pairs in JSON format.
Using OpenAI's API key for accessing fine-tuning capabilities on beta.openai.com.
Preparing data in JSON L format for the fine-tuning process.
Initiating the fine-tuning process with a specified base model and data file.
Creating a GUI for interacting with the fine-tuned GPT3 model.
Troubleshooting the GUI for displaying the entire completion from the fine-tuned model.
The importance of training with a DaVinci model for better text recognition.
The potential of fine-tuned models to answer specific and complex questions with enough training data.
Final thoughts on the importance of understanding fine-tuning for entrepreneurs in the AI industry.
Resources provided in the video description for further learning and experimentation.
Encouragement for viewers to subscribe and engage with the channel for AI entrepreneurship content.
Transcripts
my recent video about how to fine-tune
gpt3 and build an AI startup in a few
minutes got a lot of questions about the
Nitty Gritty details on how you can
actually do this fine tuning process
rather than try to answer you all in the
comments I thought I'd pop back on here
and make a quick video showing you the
step-by-step process of how to go from
data to prompting completions to a fine
tune model that you can interact with
I'm going to be breaking it down a lot
more granularly in this video and going
A to Z on how any beginner can come in
and use data to fine tune it and get a
fine-tuned version of gpt3 to use for
their own purposes if you're an
entrepreneur and are looking to start
making money and building businesses in
the AI Gold Rush you need to be aware of
how these fine-tuning processes work so
this video is exactly what you're going
to need to learn to get to a basic level
of understanding so that you can move
forward and start hiring people and know
what the process is and be alert and
aware of what's going on so we're going
to jump back in here where we left off
with the NBA players performance data
set I'm going to do this all again so
we're starting off by downloading the
data set links to this will be in the
description below fine-tuning these
models is one of the biggest
opportunities in business of 2023 and
I'd say for the next few years because
of the power of these models and being
able to build on top of them one of the
most powerful skills that you can have
as an entrepreneur right now is
understanding how this process works so
that you can start to see opportunities
way you can apply data to these models
and build a valuable business so don't
go anywhere I'm going to be going step
by step explaining this whole process
and you're not going to want to miss any
of it enough talk let's get stuck into
it a quick overview of this process from
start to finish to start off we need to
find a set of data which in this case
we're using the NBA players performance
started from my previous video then
we're going to take that data and create
prompt and completion pairs which is the
format that we need to provide to gpt3
in order to fine tune it and finally
we're going to be able to run a basic
app on our computer that is a fine-tuned
version of gpt3 that understands the
starter that we fit to it to start off
we're going to download the NBA player
performance data from kaggle the dataset
link will be in the description next
we're going to import the data to Google
Sheets so that we can manipulate it a
bit we have an index row here on the
left that we can remove
now I'm just going to put a filter on
this so that we can remove the blanks as
I did in the previous video
copy this
create a new sheet and paste it in now
we have it all formatted nicely and
Compact and ready to go we're just going
to download this data in a CSV format
and then open it in Visual Studio code
so we can have a look at it now what you
need to do is you don't have to visual
studio code or your favorite code editor
what I'm going to do here it's already
opened up but just to help you guys out
I'm going to actually close that and
then show how you open it so I'm going
to go open folder
and here is the folder that I've created
you need to create a new folder anywhere
on your desktop will work and then you
need to open that folder by clicking
open here and then inside it I've got
the I've dragged in the CSV file that we
just downloaded and in here's also some
of the uh all the data that we download
from the kaggle set which we don't
really need at the moment because we've
got the one that we need which is the
scoring then you're going to want to
copy the header row and take it over to
chat GPT
and then we're going to give it just an
example row of data so it kind of knows
what format works I'll give it a couple
actually let's give it four rows of data
to have a look at so this is the prompt
I'm ready to get started I have a
spreadsheet of basketball data these are
the column headings in CSV format and
I've pasted in the column headings and
I've gone here are the rows of data in
CSV format and pasted in a few example
rows for it to use finally I'm asking it
do you understand and I'm hoping after
this prompt is sent it's going to say
okay I understand what you're trying to
do here I've read the CSV and I
understand the format of the file great
tattoo BT is understood what we've put
into it and now we need to ask it for
some prompting completion pairs the
method I'm going to show you here is
actually different to the one I did in
my previous video in my last video the
way I showed you how to generate these
promptly completion pairs using chat GPT
is actually not that scalable so I've
done a little bit of research and
figured out how to get chat GPT to write
us a script to generate these pairs and
so we can do that with like hundreds of
thousands of different pairs so we're
going to get stuck into that now I'm
telling chat GPT that I want to
fine-tune gpt3 using the starter write
me a script to create prompted
completion pairs within this format and
then pasting some examples of the format
that I want it back in I'm actually
going to insert python in here just to
be extra clear okay now after a bunch of
missing around and tweaking this just to
get it just right so you only have to do
it once this is the exact prop that you
guys will need to put in I had to really
coach it in order to give the right
script out the first time so that you
guys didn't have to mess around with it
so if you're following along head down
below to the description I'm going to be
pasting these prompts into a Google doc
and leaving the share link for you guys
to check out down below so if you want
to get this entire prompt here that I've
had to tweak quite a lot and you can
follow along then just kid down below
and you can get it off Google doc after
submitting that prompt it gave me back
the script that I need to get this data
into the promptly completion pairs so
once you have this code all you need to
do is copy it and head back over to vs
code right click in here and create a
new file and we're going to call it
main.pi
paste this in save the file and then
you're going to want to make sure up
here this basketball data.csv is the
same name as this so you can just click
on here press enter paste it in there
and save the file so now we have the
name thing all ready to go and then we
have it the script all ready to go as
well if you're not too familiar with
programming and and this basic sort of
python scripting I'm going to give you a
quick run through of what's Happening
Here so that you're not completely blind
here we have our CSV data and this is
comma separated value so each one of
these at the top you have the header and
a comma separates it to the next column
so this is basically a condensed version
of a spreadsheet that a computer can
read we have all of the headings up here
and they're separated by commas so the
computer can read along and say okay
this is a column and I've got a uh
extension installed on this computer
that allows me to see these a lot more
easily I'm actually just going to show
you that now
and here it is it's called rainbow CSV
if you just install this quickly then
it's going to help you visualize the CSV
a lot more easily like I am here so it's
pretty straightforward we have the
header row and then underneath it is
each of the data points for that same as
a spreadsheet but it's just not
formatted as nicely so by using a method
called string interpolation and F
strings as you can see here this f means
that anything that you put inside of
these curly brackets here is going to be
uh the value of this player here is
index player so this is referencing the
player column here right at the end so
this player one when you see that key
player it's referencing that in the
script here so for every row of data
that we have coming along here it's
going to take this player column which
is the green thing here and it's going
to say okay I'm gonna because we're on
this row and it's going to work down
every single row through the whole sheet
it's going to take the player value
there
and write the prompt with it write a
summary of player values statistics and
then it's going to start building the
completion for that row and once again
it inserts the player's name and it says
player name played games played games
starting game started of them so it
writes a big long sentence and summary
of what the player's data is and every
time it comes to one of these blocks
here which has got the curly brackets
it's reaching into this file and
plucking out the right value as the
script has made it then all it needs to
do is append these prompting completion
pairs together into the format that we
asked it for back in chat GPT here which
is this format
and then it just saves it to a Json and
dumps it out for us to look at if you're
following along I'm assuming you've got
python installed on your machine if not
head to their website and you can
download the latest release of python
now that you understand the script let's
actually run it and then see what we get
the command to run a script and platform
is Python and then the name of the
script so this case is main.pi and hit
enter and then just like that we have a
prompt and completion peers.json which
looks like a complete mess but it is a
ton of data all formatted exactly how we
wanted it if you'd like to see it a
little bit more pretty what we can do
here is type our pritia
and this one here if you just install
this quickly and then you head back to
your uh prompt and completion pairs you
can press option shift and if and then
it will format it all up nicely like
this it looks really good and actually
color codes and understand it as Json so
you're actually probably going to need
to do this so head over grab that
extension and come back and press option
shift and F now we can see the result of
our hard work so now if every player on
that spreadsheet that we started with we
have the prompt which is write a summary
of Luca don't shoot your statistics and
in the completion which uses check TPT
summary structure and then it's simply
done it programmatically and use string
installation which is a python feature
to pluck data out of that CSV file and
put it into the correct place to create
this completion I've just taken all of
the code out of the main.pi and put it
into a new file called generate.pi and
saved it so that we can play around in
the main.pi file for the next step
another fun part of taking all of these
pairs and funneling them into gpt3 and
fine-tuning it begins to get started
you're going to want to head to
beta.openai.com and you're going to want
to head over to your personal section
and view your API key to create an
account if you haven't already it's free
and then you're going to want to create
a new API key here I am just creating a
new one now you can see all that on
screen but I'm going to delete it you
cheeky guys in the in the comments I'm
just going to put that there to save it
for later now we're on the documentation
page for fine tuning bio open AI so
we're going to head down to the
installation we're going to copy this
and head over to our terminal and paste
it in
I've already got everything downloaded
of course so the requirements are
already satisfied for me but it should
start installing and show a progress bar
for you now you need to copy this export
string and just bring it back over to
your main.pi file to copy this entire
API key and then paste it within these
quotations
now we need to copy this entire thing
hit back and paste this in and this
means that it's worked we've exported
the open AI key so now it's ready to use
for later next we're going to have to
prepare our data so you can copy this
head over to your terminal and delete
all this and this local file means we
need to reference our content completion
file and what we can do is come over
here copy all of this head back paste it
in there and hit enter I've just taught
it to prepare my data and it says here
your file appears to be in Json will be
converted to Json L which is the format
it needs your file contains 250 prompted
completion pairs which is a pretty good
starting little batch of data we have
here now it gives you me a whole bunch
of tips and tricks on how it can make
the data better and get better results
out of it so you probably want to have a
read through this whenever you do this
again and uh follow all of the
instructions here because it's going to
make your model a whole lot better for
all of these pairs we should really have
a suffix on it that is really unique and
like a bunch of slashes and hashtags and
stuff too there's a few things here like
starting all of your completions with
the white space character using a unique
ending like pound signs on the end of
your completions all of these are really
important to do we don't have time in
the scope of this video if you'd like a
little bit more on that I can shoot a
quick loom for you guys and put it in
the in the comment section below but for
all purposes of this video we're good to
go and we can hit in on this and it's
going to convert it from Json into Json
l
and again add a white space character to
the beginning of the completion it's
going to do it for us which is great
and yes
and just like that we have it all made
up into Json L format ready to put into
the fine tuning process now we can
actually fine tune our model uh you need
to head over here and copy this and note
that you can change the name of the base
model that you're starting from so I've
got this put in here open AI fine
tunes.create I've put Curie at the end
to specify the model that we want to use
and now I also need to put in the path
to the file which is all the data that
it's going to use to fine-tune so I've
got to go over here copy this entire
thing including the suffix and then
paste this in here and then hit enter so
what this is now going to do is upload
all of that data and put you in the
queue to fine tune and then it's going
to put all that data through their fine
tuning method and then the result is
going to be a fine-tuned version of gpk3
that is familiar with all of this
basketball information and in just a few
minutes we've got our fine tune model
complete and here down on the bottom of
the screen you can see what the name of
the model is called you're going to want
to copy that and save it for later now
we need to head back to chat gbt so that
we can get a graphical user interface or
GUI so that we can interact with this
now to make things super easy I'm
actually just going to grab the GUI
script from the previous video that I
did so I'm going to copy this this is
going to be available in the Google talk
down in the description so head over
there and grab that this is going to
have all this code that you need in
order to run a basic GUI so that you can
interact with your fine tune model and
we're going to head back and here we
have the name of our model that we want
to cut that out of there and replace it
within these uh quotations here now
before you try to start this app up make
sure you save your main.pi file before
you start running it python main.pi
function ran and just like that we have
a fine tune gpt3 window up and we can
start giving it prompts if I paste in
one of these prompt here Jason Tatum
statistics and paste them in it's going
to give me out what is the beginning of
this completion now it seems to be
having an issue where it's not writing
out the entire completion I'm not sure
if that's an issue with my API key or a
limit on the API request or it's an
issue within the GUI itself but I'm
going to have a dig into that over the
next couple hours and get back to you
guys in a bit hey guys I just took a
little look into why it's not completing
the rest of it and turns out it's a
pretty simple fix in the completion line
here
openai.completion.create there's
actually a parameter that is a Max
tokens that is usually set so 10 or
something just so that it limits how
much you're charged through the API so
it's a built-in safety feature to stop
you spending too much money all you need
to do is come in and change us here by
adding in this comma Max tokens equals
I've done 150 and that about that's
about right for what we need so if I
just run the Python program again here
bring it over another thing you need to
note as well is that in the preparing
process I didn't notice it at the time
but what it did is trim out the writer
summary part so as you can see here it
says write a summary of look Advantage
statistics but what it did is because it
was shared with all of the different
prompts it actually cut it out in the
preparation process so all our prompts
are really is just their name and their
statistics afterwards so if I take that
over to our app here and put it in load
for a bit now I've actually retrained
this on a DaVinci model off camera just
because DaVinci is actually a lot better
for recognizing what text you want so if
you train with Curie make sure you go
back and retrain with DaVinci it cost me
about three bucks in order to get it
retrained but definitely worth it and as
you can see here we've got the entire
prompt here which we expected and it's
actually started giving us information
on Karl Anthony towns and statistics so
I'm not 100 sure why it's continuing
down the list there but we got our
result we got our entire print out of
janus's Statistics so I've been a result
the last episode thanks for sticking
around and we'll get back to the video
for the purpose of this video I've shown
you how to go from start to finish how
you can take your data you can prepare
that data you can get it put into a
promptly completion Pairs and then
finally fine-tuned your version of gpt3
so that you can start interacting with
it of course this is an extremely basic
example and the understanding that this
gpt3 fine-tuned model has of this
basketball topic is very limited and you
need to give it uh probably thousands
and thousands more variations of these
prompts once you've given it enough data
its understanding will be flexible of
the topic and you'd actually be able to
ask us basically any question and start
being sort of specific about it and
saying hey what are the three players
playing for the XYZ team who have the
highest field goal percentage and that's
the kind of stuff that you'd eventually
be able to get to given enough training
data so that's all for the video guys
I've shown you how to go from start to
finish and fine tuning a model with a
bit of data so if you have any questions
about this or you've got stuck or
something's not working on your computer
Drop it Down Below in the comments
either I'll help you or someone else in
the community will the important thing
about learning this process is that as
an entrepreneur you need to understand
that this is what is going on behind the
scenes for a lot of the startups that
you're seeing just spending half an hour
or an hour trying to understand this
process is going to put you leagues
ahead of other entrepreneurs and other
people trying to make money in this AR
gold rush because you understand the
underlying buying technicalities of how
these models are being created and
fine-tuned but the understanding of this
process you are going to be keeping a
close eye out for data sources and
understanding how you can get that data
source and integrate it into a gpt3
model or a gpt4 model which is coming up
very soon so I hope you got something
out of this remember that down in the
description I'm going to have a Google
doc having all of the prompts that I
sent to church EBT and also the code so
it should be pretty straightforward for
you guys to have a play around with us I
really hope you enjoyed and got
something out of it if you like content
like this my name is Liam Motley and I'm
a self-made serial entrepreneur from New
Zealand but now I'm living in Dubai I
make AI entrepreneurship focused content
at least three times a week for aspiring
and established entrepreneurs looking to
get into the AI industry and make money
in this hour Gold Rush it's happening
right in front of us so if that kind of
stuff sounds interesting to you hit down
below and subscribe to the channel hit
the Bell so you don't miss my next one
if you've got something out of this
video please drop a like it really helps
my channel a lot and of course leave
your comments down below and I'll be
answering as many as I can that's all
for today thank you so much for watching
and the best of luck to you as you
navigate This Hour Gold Rush I'll see
you next time
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