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Assignment: Building the Simplest MVP AI Language Model Trained on Baby Llama
Purpose of the Assignment: Create an Embedding which means Building the Simplest MVP AI Language Model Trained on Baby Llama
{Later on: For the project we will build some ML OPS build processing with CI CD around your MVP AI Language Model embedding to evolve your embedding into a simple but fully featured AI language model that you can send queries to and get responses: A small scale Chat GPT}
Objective:
The objective of this lesson plan is to discuss constructing a Minimum Viable Product (MVP) AI language model.
Students will learn how to train a basic AI language model using the Baby Llama dataset and Google Colab as the coding tool.
Prerequisites:
- Basic understanding of Python programming language
- Familiarity with Google Colab
Materials Needed:
- Computers with internet access
- Google Colab account
Lesson Plan:
1. Introduction (10 minutes)
- Begin the lesson by introducing the concept of AI language models and their applications.
- Explain the importance of MVP in AI development and its role in creating a basic working model.
- Highlight the Baby Llama dataset and its use for training the language model.
Today, we will be diving into the fascinating world of AI language models. AI language models have become increasingly powerful and capable, revolutionizing various fields such as natural language processing, understanding, and generation. They have the potential to transform the way we interact with technology and communicate with each other.
Before we begin, let's take a moment to understand the importance of Minimum Viable Product (MVP) in AI development.
An MVP is a basic working model that focuses on delivering the core functionality of a product.
It allows developers to quickly test and validate their ideas, gather feedback, and iterate on their designs.
In the context of AI language models, an MVP serves as a starting point for further development and refinement.
Today, we will specifically explore the concept of building the simplest MVP AI language model trained on the Baby Llama dataset.
The Baby Llama dataset is a collection of text data that we will use to train our language model.
It will serve as the foundation for our model's understanding and generation of text. Your MVP assignment will be the input to building your PROJECT.
Importance of AI Language Models
AI language models are at the forefront of generative AI techniques.
They analyze bodies of text data using statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence.
Baysian Training Methods.
These models are used in various applications, including natural language processing, understanding, and generation systems.
The power of AI language models comes with ethical considerations. Issues such as bias in generated text, misinformation, and potential misuse of AI-driven language models have led to concerns about their unregulated development. As we explore AI language models, it is important to be aware of these ethical concerns and strive for responsible development and usage
.Introduction to the Baby Llama Dataset
The Baby Llama dataset is a curated collection of text data that we will use to train our AI language model.
It provides a diverse range of language patterns and structures (tokens and weightings) for our model to learn from.
By training our model on this dataset, we aim to create a language model that can generate text similar to the patterns and styles found in the Baby Llama dataset.
The Baby Llama dataset is just one example of the many datasets available for training language models.
It is important to choose a dataset that aligns with the specific goals and requirements of your project.
The dataset should be representative of the type of text you want your language model to generate. {Project goal is a general conversation chatbot.}
Now that we have a clear understanding of the concept of AI language models, the importance of MVP in AI development, and the Baby Llama dataset, we can move on to the next steps in our journey of building the simplest MVP AI language model trained on Baby Llama.
Lab Worksheets [Template for your Assignment]
2. Setting Up Google Colab (15 minutes)
- Instruct students to open Google Colab on their computers.
- Guide them through the process of creating a new Python notebook in Google Colab.
- Explain the benefits of using Google Colab, such as its cloud-based environment and access to GPU resources.
3. Importing Necessary Libraries (10 minutes)
- Instruct students to import the required libraries, such as TensorFlow and Keras, for training the AI language model.
Importing Necessary Libraries
To train an AI language model, we need to import certain libraries that provide the required functionality. The two main libraries we will be using are TensorFlow and Keras. Here's how you can import them:
import tensorflow as tf
from tensorflow import keras
Let's understand the purpose of each library:
By importing these libraries, we gain access to a rich set of functions and classes that simplify the process of training an AI language model. TensorFlow provides the underlying infrastructure, while Keras offers a high-level API for building and training neural networks.
- Provide code snippets and explain the purpose of each library.
Now that we have imported the necessary libraries, we can proceed with the next steps in training our AI language model.
4. Preparing the Dataset (15 minutes)
- Guide students through the process of downloading and preprocessing the Baby Llama dataset.
- Demonstrate how to clean and preprocess the text data to remove any unnecessary characters or symbols.
- Explain the importance of data preprocessing for training accurate language models.
- Explain the importance of data preprocessing for training accurate language models.
Preparing the Dataset
To train an AI language model, we need a dataset that contains text data. Let's go through the process of downloading and preprocessing the Baby Llama dataset as an example.
By following these steps, you can download, clean, and preprocess a dataset to prepare it for training an AI language model. Remember to adapt the code and instructions based on the specific requirements of your dataset and language model.
5. Building the Language Model (20 minutes)
- Introduce students to the concept of recurrent neural networks (RNNs) for language modeling.
- Guide them through the process of building a basic RNN-based language model using TensorFlow and Keras.
- Explain the architecture of the model and how it learns from the training data.
Explain the architecture of the model and how it learns from the training data.
Building the Language Model
To build a language model, we will use a recurrent neural network (RNN) architecture. RNNs are well-suited for language modeling tasks as they can capture the sequential nature of text data. Here's a step-by-step guide on building a basic RNN-based language model using TensorFlow and Keras:
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