Knowledgebase

Introduction

Fine-tuning involves updating an AI model with new data to adapt the model to a specific task. You can fine-tune a model using:

1. The retrieval augmented generation (RAG) technique. This method involves tapping real-time data sources like MySQL/PostgreSQL database to generate factual results.
2. The labeled dataset technique: This method uses a smaller labeled task-specific dataset to optimize the model's performance.

Consider the Llama 2 language Models (LLMs) with over 70 billion parameters. The model is trained on vast volumes of data to perform different tasks. However, with time, the model's data can become outdated.

To make the LLama 2 model generate relevant results for queries, you can fine-tune the model with either the RAG or labeled dataset techniques. Each method has its pros and cons, as discussed in this article.

In this guide, you'll auto-tune the LLama 2 model with a new ad generation labeled dataset. You'll then push the new model to your Hugging Face account and use it to perform ad-generation tasks using the Rcs cloud GPU server.

Prerequisites

Before you begin:

• Deploy a new Ubuntu 22.04 A100 Rcs Cloud GPU Server with at least:

  • 80 GB GPU RAM
  • 12 vCPUs
  • 120 GB Memory

• Establish an SSH connection to the server.

• Create a non-root user with sudo rights and switch to the account.

• Create a HuggingFace account.

• Create a Hugging Face user access token with WRITE permissions.

Compare RAG With Labelled Datasets

The RAG technique is more applicable when using structured data while fine-tuning a model using labeled datasets works well with unstructured data. The following table highlights the key differences between the two.

Install the Required Dependencies

To fine-tune and use the LLama 2 model with Python, follow the steps below to install the required libraries:

1. Install the huggingface_hub and autotrain-advanced libraries.

   console Copy

   $ pip install huggingface_hub autotrain-advanced
   

2. Add the libraries to the system path.

   console Copy

   $ echo "export PATH="`python3 -m site --user-base`/bin:\$PATH"" >> ~/.bashrc
   source ~/.bashrc
   

Implement the AutoTrain Tool

The Hugging Face AutoTrain tool allows you to train natural language processing (NLP) models like Llama 2 for specific tasks. The tool accepts different custom parameters in a user-friendly format without spending too much time on the technical details.

In this section, you'll feed an ad copy generation dataset to the AutoTrain tool to fine-tune the meta-llama/Llama-2-7b-chat-hf model. Then, you'll upload the newly trained model to your Hugging Face account and later run the model using a Docker container to retrieve fine-tuned results.

Prepare the New Model's Dataset

The AutoTrain tool accepts the following specific data format for training the Llama 2 model:

<s>[INST] <<SYS>>
 {{system message}}
<</SYS>>
{{message}} [/INST] {{answer}} </s>

Hugging Face provides a hub for various datasets that adhere to the above format. You can use these datasets to fine-tune your model. For instance, for this guide, you'll use the ad copy generation labeled dataset to fine-tune your model. Here are sample rows from the dataset.

<s>[INST] <<SYS>> Create a text ad given the following product and description. <</SYS>> Product: Wide-leg trousers Description: Pants with a loose and flowing fit, creating an elongated and sophisticated silhouette. [/INST] Ad: Introducing our Wide-Leg Trousers: Effortlessly chic and endlessly versatile. Discover a flattering, comfortable fit that creates an elegant, elongated silhouette. Elevate your style today! </s>

...

<s>[INST] <<SYS>> Create a text ad given the following product and description. <</SYS>> Product: Electric Food Chopper Description: Chop ingredients effortlessly with an Electric Food Chopper - designed for quick and efficient meal prep. [/INST] Ad: Chop, prep, and conquer your kitchen tasks!