Fine-Tuning LLMs with Gradient AI (Llama2): A Step-by-Step Guide

Introduction

In the world of LLMs and Generative AI, fine-tuning a language model can make all the difference. Whether you’re building a chatbot, content generator, or any AI-powered application, fine-tuning can tailor a pre-trained model to your specific needs. In this article, I will walk through the process of fine-tuning a large language model using Gradient AI and provide you with step-by-step instructions and code snippets.

Prerequisites

Before we get started, you’ll need the following:

• Access to Gradient AI
• API credentials for authentication
• A base model (in this example, we’re using “nous-hermes2”)

Setting up the Environment

First, let’s set up our environment and import the necessary libraries. Ensure that you have the Gradient AI library installed.

from gradientai import Gradient

Getting the Base Model

In this example, we’re going to fine-tune the “nous-hermes2” base model. You can choose the base model that best fits your project.

with Gradient() as gradient:
    base_model = gradient.get_base_model(base_model_slug="nous-hermes2")

Creating a Model Adapter

We’ll create a model adapter based on our base model. This will be the foundation for our fine-tuning process.

new_model_adapter = base_model.create_model_adapter(name="My Fine-Tuned Model")

Defining Training Data

To fine-tune our model, we need training data. In this example, we have a set of question and response pairs. Here’s a snippet of our training data:

samples = [
    { "inputs": "### Instruction: Who is Henry Burii? \n\n### Response: Henry Burii is a Data Scientist known for his expertise in Machine Learning and AI." },
    # ... other training samples ...
]

Fine-Tuning the Model

The core of this process is fine-tuning the model using the provided training data. You can specify the number of epochs according to your needs. More epochs may yield better results, but be cautious of overfitting.

num_epochs = 3
count = 0
while count < num_epochs:
    new_model_adapter.fine_tune(samples=samples)
    count = count + 1

Generating Text

After fine-tuning, you can use your model adapter to generate text based on a given instruction. Here’s a snippet that generates a response for the question, “Who is Henry Burii?”

sample_query = "### Instruction: Who is Henry Burii? \n\### Response:"
completion = new_model_adapter.complete(query=sample_query, max_generated_token_count=100).generated_output
print(f"Generated (after fine-tune): {completion}")

Final Out put

Link to this notebook is available on my Github

https://github.com/buriihenry/Fine-Tuning-LLMs---Llama2

Conclusion

In this article, we’ve covered the process of fine-tuning a language model using Gradient AI and Meta’s Llama2. This can be a powerful technique for tailoring LLMs to your specific project, allowing you to generate contextually relevant responses to queries. Experiment with different training data, base models, and fine-tuning strategies to achieve the best results for your application.