Fine-tuning Gemini with Google AI Studio Tutorial - [Customize a model for your application]

Summary

In 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

00:00

🔧 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.

05:01

📈 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.

Keywords

💡Fine-tuning

Fine-tuning refers to the process of training a machine learning model on a specific dataset to improve its performance on a particular task. In the context of the video, fine-tuning is used to tailor a model to generate social media captions that are specific to a brand's style. The script mentions fine-tuning a model called Gemini, which is analogous to adjusting a model to produce outputs that are consistent with the brand's tone and format.

💡Google AI Studio

Google AI Studio is a platform that allows users to work with AI models and perform tasks such as fine-tuning. The video script highlights its user-friendly interface and the ability to start for free. It is the main environment where the fine-tuning process takes place in the video, and it is where the structured prompts for training the model are created.

💡Structured Prompt

A structured prompt is a type of input designed to guide an AI model to produce a specific type of output. In the video, the creator uses structured prompts to teach the model how to generate captions with a particular format, such as one emoji and two hashtags. The script provides an example of creating a structured prompt for a 'caption generator' tailored to a business's brand.

💡Model

In the context of AI, a model refers to the algorithmic representation of a system that can be trained to perform tasks. The video script discusses tuning a model named Gemini, which is a specific instance of an AI model that can be fine-tuned for specialized tasks like generating social media captions.

💡Social Media Caption Generator

A social media caption generator is a tool designed to create text for social media posts automatically. In the video, the creator demonstrates how to fine-tune a model to act as a caption generator that is tailored to a specific brand, ensuring that the generated captions match the brand's style and tone.

💡Emoji

An emoji is a small digital image or icon used to express an idea, emotion, or concept in digital communication. The video script specifies that the fine-tuned model should include one emoji in each generated caption, which is part of the structured prompt's output format.

💡Hashtags

Hashtags are words or phrases preceded by a hash symbol (#), used to categorize and find content on social media platforms. In the video, the fine-tuned model is expected to generate captions that include two hashtags, which helps in categorizing the social media posts and increasing their discoverability.

💡CSV

CSV stands for Comma-Separated Values, a file format used to store and organize data in a tabular structure. The video script mentions the use of a CSV file to import examples for fine-tuning the model, which simplifies the process of providing a large number of training examples.

💡Google Sheets

Google Sheets is a web-based spreadsheet application that allows users to create, edit, and collaborate on spreadsheets. The video script suggests using Google Sheets as an alternative to manually adding inputs and outputs for fine-tuning the model, which can be more efficient for handling large datasets.

💡Temperature

In the context of AI, temperature is a hyperparameter that controls the randomness of the model's output. A higher temperature results in more creative but unpredictable outputs, while a lower temperature leads to more consistent but less varied responses. The video script discusses the concept of temperature in relation to the creativity of the model's responses.

💡API Endpoint

An API endpoint is a specific location in a networked system that can be used to access a web resource or service. In the video, the creator explains that once the model is fine-tuned, it can be accessed via an API endpoint, allowing it to be integrated into other software or automation workflows.

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