Fine-tuning Gemini with Google AI Studio Tutorial - [Customize a model for your application]
Summary
TLDRIn this tutorial, viewers learn to fine-tune Google AI models within Google AI Studio for specific outputs. The host demonstrates creating a structured prompt for a social media caption generator tailored to a brand's style, emphasizing the importance of providing ample examples for training. They guide through the process of adding user inputs and expected outputs, choosing the right model, and adjusting settings like token count and temperature. The video also covers saving prompts, using the tuned model, and importing data via CSV or Google Sheets for a more efficient fine-tuning experience.
Takeaways
- 😀 The video teaches how to fine-tune Google AI models within Google AI Studio for specific outputs.
- 📝 It demonstrates creating a structured prompt for a social media caption generator tailored to a brand.
- 🆓 Google AI Studio is free to start, making it accessible for users to experiment with fine-tuning models.
- 🔧 The process involves adding potential user inputs and expected outputs within the platform's user interface.
- 📈 The video provides an example of training the model to generate captions with one emoji and two hashtags for different company types.
- 📚 Google recommends providing 500 examples for effective fine-tuning, though the video uses 20 for demonstration.
- 🔑 The model type selection is crucial, with different versions available, similar to the versions of models from OpenAI.
- 🌡 The concept of 'temperature' in AI is introduced as a creativity metric, affecting the output's variability.
- 📊 The video shows how to save structured prompts and use them to create a tuned model within Google AI Studio.
- 🔄 The process includes testing the fine-tuned model to ensure consistency in outputs like emojis and hashtags.
- 📋 The video also covers uploading a CSV file to create a fine-tuned model based on imported data.
Q & A
What is the main topic of the video?
-The main topic of the video is how to start fine-tuning Google AI models within Google AI Studio using structured prompts and import files.
What is fine-tuning a model in the context of the video?
-Fine-tuning a model in this context means training a model to produce very specific types of outputs when accessed through an API endpoint or a user interface.
What is the first example given in the video for fine-tuning a model?
-The first example is a business wanting to create a social media caption generator tailored to their brand.
How does Google AI Studio differ from using an API like open AI's Chad GPT for fine-tuning?
-Google AI Studio allows fine-tuning to be done within its user interface without the need to create a JSON file in VS Code, making the process easier.
What is a structured prompt in the context of Google AI Studio?
-A structured prompt in Google AI Studio is a way to define potential user inputs and the expected outputs for fine-tuning a model.
How many examples does Google recommend providing for fine-tuning a model?
-Google recommends providing at least 500 examples for fine-tuning a model.
What is the purpose of providing multiple examples in the fine-tuning process?
-Providing multiple examples helps the model understand the expected pattern or structure of the outputs, such as including one emoji and two hashtags in the captions.
What is the role of 'temperature' in the fine-tuning process?
-Temperature is a creativity metric; a higher temperature results in more creative and varied outputs, while a lower temperature leads to more consistent and predictable outputs.
How can the fine-tuned model be saved or referenced for future use?
-The fine-tuned model can be saved as a copy by using the ellipses menu and then referenced in a new tune model within Google AI Studio.
Can the fine-tuned model be used in an API endpoint?
-Yes, the fine-tuned model can be used in an API endpoint by utilizing the model ID provided in Google AI Studio.
What is an alternative method to manually adding examples for fine-tuning shown in the video?
-An alternative method shown in the video is uploading a CSV file with the input and output examples already formatted.
How many examples are needed to save a structured prompt according to the video?
-At least 20 examples are needed to save a structured prompt in Google AI Studio.
What is the workaround suggested in the video if the save button doesn't work?
-The workaround suggested is to use the ellipses menu and select 'save as copy' to save the structured prompt.
How does the video demonstrate the effectiveness of the fine-tuning process?
-The video demonstrates the effectiveness by showing consistent outputs with one emoji and two hashtags when testing the fine-tuned model with different inputs.
What is the final step shown in the video for using the fine-tuned model?
-The final step is uploading a CSV file with examples and following the same steps as before to create a fine-tuned model based on the imported data.
Outlines
🔧 Fine-Tuning Google AI Models for Business Use
The video script introduces the process of fine-tuning Google AI models within Google AI Studio. It aims to guide viewers on how to create structured prompts and utilize the platform's capabilities to train models for specific outputs. The primary example given is for a business wanting to create a social media caption generator tailored to their brand. The script explains the difference between fine-tuning and using a model as-is, and emphasizes the ease of fine-tuning within Google AI Studio's user interface compared to other methods like using JSON files. The tutorial continues with instructions on how to add user inputs and expected outputs, the importance of providing multiple examples for training, and touches on model types and settings such as token count and temperature, which affects the creativity of the AI's responses. The video concludes with a demonstration of how to save and use the fine-tuned model, showcasing its effectiveness in generating consistent social media captions with an emoji and two hashtags.
📈 Uploading Data to Fine-Tune Models in Google AI Studio
This paragraph of the script focuses on the advanced steps of fine-tuning models in Google AI Studio using uploaded data. It begins with the process of naming and setting up a fine-tuned model, highlighting the platform's limitations to one model type at a time. The script then details how to upload a CSV file to create a fine-tuned model, explaining the formatting requirements and the process of mapping input and output columns. The example provided involves generating captions with emojis and hashtags based on state names. The video also mentions the option to use existing Google Sheets or other files from the user's drive. After uploading and formatting the data, the script guides the viewer through the steps of importing examples and fine-tuning the model based on the provided data. The paragraph concludes with a mention of other features available on the platform and an invitation to explore additional content on the channel.
Mindmap
Keywords
💡Fine-tuning
💡Google AI Studio
💡Structured Prompt
💡Model
💡Social Media Caption Generator
💡Emoji
💡Hashtags
💡CSV
💡Google Sheets
💡Temperature
💡API Endpoint
Highlights
Introduction to fine-tuning Google AI models within Google AI Studio.
Explanation of the concept of fine-tuning a model for specific outputs.
Demonstration of creating a structured prompt for a business use case.
Advantages of Google AI Studio's free-to-start policy and user interface.
Tutorial on adding user inputs and expected outputs for model training.
Comparison with fine-tuning a GPT model using JSON files in VS Code.
Importance of providing multiple examples for model understanding.
Google's recommendation of 500 examples for optimal training.
Explanation of model types and choosing the appropriate one for fine-tuning.
Details on token count and temperature settings for model fine-tuning.
The impact of temperature on AI creativity and output consistency.
Process of adding data points and examples for the model to learn from.
Testing the fine-tuned model with a real-time example.
Instructions on saving a structured prompt with at least 20 examples.
Workaround for saving issues by using 'save as copy' feature.
How to reference a saved structured prompt in a new tune model.
Overview of uploading a CSV file to create a fine-tuned model.
Steps for formatting and importing examples from a CSV file.
Final steps to complete the fine-tuning process and use the model.
Additional resources and further exploration of Google AI Studio features.
Transcripts
in today's video we're going to learn
how to start fine-tuning Google AI
models within Google AI Studio we have
the ability to create prompts templates
and a ton of other stuff in this video
though we're going to learn how to tune
a model therefore in today's video I'm
going to show you how to tune a model
with a structured prompt and a import
file sound good let's go and jump into
today's video Welcome Back y'all in this
video I'm going to show you how to start
fine-tuning Google AI models now your
first question might be Corbin what do
you mean tune a model or fine tune what
this does in this context is that we
train a model so when we access the AP
endpoint or alternatively use it in a
user interface like this we expect very
specific types of outputs so in this
video we're going to go over two
different examples here and it's going
to make a lot more sense the first
example here is going to be a business
that wants to create a social media
caption generator but be tailored to
their brand now I've done a video like
this in the past but with open Ai and
Chad gbt and you can check it out right
there to see how to finetune the Chad
gbt model but in this video we're going
to fine-tune Gemini no not the Star Sign
Gemini are you a Leo are you a Virgo are
you a Scorpio let's go ahead and get
going here now what's great about Google
AI studio is it's free to start so I'm
going to leave a link to it in the
description below go and check it out
but let's go and create a tune model
here let's start off by creating a
structured prompt we're going to go
ahead and simply click create a
structured prompt I'm also going to
leave a link to their FAQ and guide so
if you have any other questions that I
may not address in this video you can
check it out as well create structured
prompt we are in let's go edit our title
here we're going to goad and see caption
generator boom now comparative to
fine-tuning a kgpt model which actually
required us to make a Json file within
vs code we can actually do the
fine-tuning within the UI of Google AI
studio so it's a little bit easier all
it requests us to do is simply add a
potential user input and the expected
output now we have a couple different
options of how to add this kind of
information the second option you're
going to see in this video is just
simply doing it through a Google sheet
or CSV this option though where it's
going to manually add our inputs and
outputs here's an example of a possible
input and a possible output notice the
input is K company and the output is
Sweet Moments cake emoji # cake love #
Sweet Treats therefore the purpose of
this fine tune model for this use case
is I'm going to provide a company type
so for example cake company and then the
actual output should consist of a
caption one emoji and two hashtags and
how we're going to train it to
understand to only do one emoji and only
do two hashtags is we're going to
provide 20 different examples here now
Google has identify the best way to go
about this is to provide 500 examples
but for that context you're better off
uploading a while then you know manually
inputting each one now before I add the
rest of these data points cuz I don't
think you want to be there for that
we're going to go ahead and look at our
settings here now we looking at our
settings we have a couple things to take
note of here first off choosing our type
of model if you're familiar with open AI
think of Chad gbt 3.5 Chad gbg4 they
have their own versions of models as
well now if you want a more in-depth
video on the models the pricing and
everything above the board there check
out my Google AI studio video that's
like 9 Minutes of everything you need to
know about this platform for now though
we're just going to St with 1.5 flash
token count as the amount of tokens that
are currently being expended through the
input and the output now temperature is
important but it seems within our
structured prompts as of now we can't
actually adjust it I want you to think
of temperature as a creativity metric
basically the higher temperature we have
the more creative the AI gets the more
crazy it gets you get some pretty crazy
answers here that AR really structur it
or maybe not a line of what you want the
lower temperature it is the more
consistent it is at scale on outputs
what's good here though is since we're
doing a structure prompt anyways we
don't have to worry too much about this
because we're basically telling you how
to interact with inputs now we have some
other options here but for now it's not
too important for what we're trying to
do today let's add the rest of our data
points or examples in this context I
want ahead and added 10 different
examples here of ways I want the input
to be and the output to look at a few
fitness trainer no paying no gain
fitness goals work out dog groming
service positively perfect # dog groming
# petare notice how each one has an
emoji and two hashtags that's the whole
use case and purpose of this specific
finetune we want to make sure and
ensures that these show up on every
single caption we do when we put the
input of a company type once we put in
enough examples and we're satisfied
let's see if it works I'm going to
Simply put in real estate agency and
let's check out the output here we
should see one emoji and two hashtags
generate response there we go we got
home swe home an emoji and two hashtags
this shows you the power of fine-tuning
a model I'm going to go ahead and hit
run down here as well and we can run it
as many times as possible so it worked
again another one another one another
one no I'm not dj calid but there we go
it shows consistency and it shows it's
working every single time therefore we
don't have to worry too much about
temperature in this context now in order
for us to actually save that structured
prompt we need at least 20 examples so
let me go and add those I have gone
ahead and saved 20 different examples
here let's go and save it now what I've
noticed in the short term is that the
actual button to save doesn't really
seem to work so a work around for this
is hit these ellipses and save as copy
now that I've saved it as a copy I can
actually reference it in my new tune
model here come in right here copy of
capent generator here we go showing me
the first example that we saw earlier
proceed to choose your tuned model name
I'm just going to say my business
caption I know very creative short term
we can only find tune one type of model
for advanced settings we're just going
to go a and leave this default once
that's all set up all we need to do is
hit tune we are loading in what you'll
notice is that the way that it will save
it in your library is as a tune model
comparative to a structured prompt or a
chat prompt once it's done it's going to
simply be ready to go and updated just
now click it you'll get a summary of how
it tuned the model and from here to
start using it hit use a structured
prompt and we can start using our train
model Now using the structured prompt is
to use it within Google AI Studio to do
a little bit more testing see if it
works well if you want to use it in the
context of an API endpoint within your
software or automation you're going to
use the model ID here this right here is
going to allow us to access this model
at an API endpoint on top of that notice
how the actual name for the model ID has
the name we added earlier so keep that
in mind there you go now you
successfully know how to find two models
Within Google AI Studio make sure you
leave a like it's completely free and
let's go ahead and see our last step
here which is uploading a CSV to create
one for this example I went ahead and
just created a CSV here with the idea
being the input is a state and the
output will be a little caption three
hashtags and Emoji associated with it
now you also have the ability to use a
currently existing Google sheet or any
file within your drive I'm going to
simply just drag and drop my CSV once
I've added my CSV here it's going to ask
for a little bit of formatting pretty
simple because we actually formatted it
in the CSV all we to do is this this is
the input column so I'm going to say new
input column and then this is the output
column new output column once we do that
we can input our 30 current examples now
optimally this is how you're going to
upload 500 as you would not want to
manually put in 500 entries import
examples currently loading once we've
imported the data we just follow the
same steps I showed earlier and you got
your fine tune model but based off
imported data now that covers everything
we can do when it comes to fine tuning
models and Google AI studio now there is
other features on this platform that I
cover in that other video I referenced
earlier so go ahead and check that out
also do a ton of other stuff on this
channel other than just this so make
sure to check that out and I'll see you
in the next video I went ahead and let
YouTube do its thing and choose the
videos to see next I have no clue what
they are probably pretty good that's my
face and I'll see you in the next video
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