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Table of Contents

1. Introduction
2. What is Fine-Tuning?
3. Benefits of Fine-Tuning
4. Getting Started with Fine-Tuning
   • Signing up for a Gradient account
   • Creating a new workspace
   • Obtaining the workspace ID and API token
   • Setting up Google Colab
5. Installing the Gradient AI module
6. Setting environment variables
7. Loading the base model
8. Creating the model adapter
9. Defining the prompt and response
10. Running sample queries
11. Fine-Tuning the Model
    • Choosing the number of epochs
    • Providing training samples
    • Running the fine-tuning process
12. Generating responses after fine-tuning
13. Managing the model adapter
14. Tips for Creating the Training Dataset
    • Using chat GPT
    • Requesting data variations
15. Conclusion

Introduction

In this guide, we will explore the process of fine-tuning a language model using Gradient's platform. Fine-tuning is a technique that allows us to provide additional information to a pre-trained model to improve its performance on specific tasks or domains.

By following the step-by-step instructions provided, You will learn how to fine-tune an AI model using Gradient and Google Colab. We will cover everything from setting up your account to running the fine-tuning process and generating responses.

Let's dive in and get started!

What is Fine-Tuning?

Fine-tuning is the process of taking a pre-existing AI model and adjusting it to better suit specific use cases or business requirements. By providing additional data or refining the model's parameters, we can enhance its performance and tailor it to address specific tasks.

Benefits of Fine-Tuning

Fine-tuning offers several benefits, including:

1. Improved performance: Fine-tuning allows for better performance on domain-specific tasks by incorporating additional knowledge or data.
2. Customization: By fine-tuning a pre-trained model, you can mold it to better suit your business needs or target audience.
3. Cost-effectiveness: Fine-tuning a model is often faster and more cost-effective than training a new model from scratch.
4. Ease of implementation: With platforms like Gradient, fine-tuning models has become more accessible, requiring minimal coding experience.

Getting Started with Fine-Tuning

To begin the fine-tuning process, follow these steps:

Signing up for a Gradient account

To get started, sign up for a Gradient account by visiting the Gradient homepage and clicking on the "Sign Up" button. Complete the registration process and log in to your new account.

Creating a new workspace

Once you log in, you will be directed to the Gradient interface. Click on "Create New Workspace" and give it a name of your choice. You will need the workspace ID later in the process.

Obtaining the workspace ID and API token

To access the API, you will need both the workspace ID and an API token. You can find the workspace ID within your workspace settings. To obtain the API token, click on your Avatar in the top-right corner, go to "Access Tokens", and generate a new token. Make sure to copy the token for later use.

Setting up Google Colab

To perform the fine-tuning process, we will be using Google Colab, a free integrated development environment. If you don't have an account already, sign up for one. Open Google Colab, create a new notebook, and make sure to paste the API token in the appropriate location within the notebook.

Installing the Gradient AI module

To install the Gradient AI module, run the following command in Google Colab:

!pip install gradientai --upgrade

This will ensure that you have the latest version of the Gradient AI module installed.

Setting environment variables

To set the required environment variables, import the OS module and run the following code in Google Colab:

import os

os.environ['GRADIENT_ACCESS_TOKEN'] = <your_token>
os.environ['GRADIENT_WORKSPACE'] = <your_workspace_id>

Replace <your_token> with the API token you obtained earlier, and <your_workspace_id> with the workspace ID.

Loading the base model

To load the base model, use the Gradient library and specify the desired model. For example, to use the "nous Hermes 2" model, use the following code:

from gradientai import load

base_model = load('nous Hermes 2')

You can choose from various available models, such as "bloom 560" or "llama 2".

Creating the model adapter

To create the model adapter, which is a copy of the base model for fine-tuning, use the following code:

test_model = base_model.create_adapter(adapter_name='test_model')

You can choose your own name for the adapter, such as "test_model".

Defining the prompt and response

To define the prompt and response, use the following code:

prompt = """
Who is Matthew Berman?
""" 

response = """
Matthew Berman is a popular YouTuber who creates content about AI and its impact on society.
"""

You can modify the prompt and response to suit your own fine-tuning objectives.

Running sample queries

To test the model before fine-tuning, run the following code:

print("Before fine-tuning:")
print(test_model.complete(prompt))

print("\nAfter fine-tuning:")
print(test_model.complete(prompt))

Observe the output and note any differences between the before and after fine-tuning results.

Fine-Tuning the Model

Fine-tuning is the key step in improving the model's performance. Follow these steps to fine-tune your model:

Choosing the number of epochs

Decide on the number of fine-tuning iterations, also known as epochs. For example, if you choose three epochs, the model will be fine-tuned three separate times.

Providing training samples

Create a set of training samples that the model will learn from. Provide both positive and negative examples to ensure a well-rounded understanding. Refer to the future videos for tips and tricks on creating effective training datasets.

Running the fine-tuning process

Run the fine-tuning process using the following code:

num_epochs = 3
count = 0

while count < num_epochs:
    print(f"Iteration: {count+1}")
    test_model.fine_tune(prompt, response)
    count += 1

print("\nAfter fine-tuning:")
print(test_model.complete(prompt))

Make sure to adjust the num_epochs variable according to your chosen number of epochs.

Generating responses after fine-tuning

After fine-tuning, it's essential to generate responses once again to see if the model has learned from the training samples. Use the following code:

print("\nAfter fine-tuning:")
print(test_model.complete(prompt))

Observe the output and compare it to the results obtained before fine-tuning.

Managing the model adapter

Once you have completed the fine-tuning process, you can delete the model adapter. Use the following code to do so:

test_model.delete_adapter()

Make sure to comment out this line if you intend to use the fine-tuned model for further API use.

Tips for Creating the Training Dataset

To create an effective training dataset, consider using chat GPT to generate additional variations. Follow these steps:

1. Copy the existing samples you created.
2. Open chat GPT and provide these samples, requesting more variations in the same format.
3. Copy the newly generated variations and paste them into your dataset.

This allows you to quickly expand your training dataset and improve the model's understanding of the prompt and response Patterns.

Conclusion

Congratulations! You have successfully learned how to fine-tune a language model using Gradient and Google Colab. Fine-tuning allows you to augment pre-existing models to better suit your specific needs. Remember to experiment with different training datasets and variations to achieve the best results.

Thank you for using Gradient, and we hope you enjoy exploring the possibilities of fine-tuning models!

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