Present case studies of student aI students making projects to build an AI LLM language model with PYTORCH and TensorFlow

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https://www.linkedin.com/advice/3/what-unique-features-tensorflow-pytorch-make-them-urkoe

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Here are some case studies of AI students building LLM (Large Language Model) projects using PyTorch and TensorFlow:

#### College Students in AI Application Development Courses and AI LLM Model Building

When it comes to AI application development courses, college students engage in various work practices to build AI LLM (Large Language Model) models.

These practices often involve coding and model building to create conversational capabilities.

**Work Practices:**

- **Code Generation:** College students use LLMs to generate full programs or boilerplate code snippets on the fly for faster development.

- **Explaining Concepts:** LLMs are utilized to paste complex passages about coding topics and have the LLM explain them in simpler terms.

- **API Design:** LLMs can suggest intuitive APIs and standard library interfaces, which can be useful for building conversational capabilities. - **Model Building:** Students engage in model building to train LLMs on vast amounts of text data and learn the statistical properties of language, enabling them to excel at predicting what comes next in a given sequence of words or generating text based on a prompt. - **Conversational Capabilities:** LLMs are used to create conversational capabilities, allowing for AI tutors to answer student questions and explain concepts.

**Examples of Coding:**

- **Python:** College students often use Python for coding when building AI LLM models. Python is a popular programming language for AI and machine learning due to its simplicity and versatility.

- **TensorFlow and PyTorch:** These are commonly used frameworks for building AI models, including LLMs. College students may use these frameworks for training and deploying LLMs.

- **Natural Language Processing Libraries:** Libraries such as NLTK (Natural Language Toolkit) and spaCy are frequently used for natural language processing tasks, which are essential for LLM model building.

In summary, college students in AI application development courses engage in various work practices, including model building and coding using languages like Python and frameworks like TensorFlow and PyTorch to build AI LLM models with conversational capabilities.

Case Study 1: Building a Conversational AI Chatbot with PyTorch

Case Study 2: Developing a Summarization Model with TensorFlow

Case Study 3: Exploring Generative AI with PyTorch and TensorFlow

** Here's a hypothetical case study, based on realistic scenarios, of how a team of four college students might structure and construct their Large Language Model (LLM) AI project using

Google Colab

PyTorch

TensorFlow: (Get your TensorFlow Certification Exam)

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https://coda.io/@peter-sigurdson/google-tensorflow-certified-developer-exam

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https://coda.io/@peter-sigurdson/starter-python-lab-to-make-the-minimal-viable-product-ai-model-b

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https://coda.io/@peter-sigurdson/building-the-mvp-using-pytorch-and-tensorflow-to-demonstrate-tra

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Team Composition and Roles

Alice (Project Manager): Oversees project timeline, coordinates team meetings, and ensures deliverables are met.

She has a strong background in project management and a basic understanding of AI and ML.

Bob (Data Scientist): Responsible for organizing the data acquisition, cleaning, and preprocessing. Bob has experience in Python and data manipulation techniques.

Charlie (MLOps Engineer): Focuses on the model architecture, training, and optimization. Charlie has proficiency in PyTorch and TensorFlow.

Diana (Application Developer): Works on integrating the model into a user-friendly interface. Diana is skilled in web development and API integrations.

Project Plan

Objective Setting:

Develop a text-based LLM AI capable of performing specific tasks, such as:

summarization

translation

question-answering.

The model should be accessible via a simple web interface: You could use PYFLASK

For your project it is fine to interact via the Google Collab console

Research and Resource Allocation:

Extensive research on existing LLMs like GPT and BERT.

Allocating cloud resources on Google Colab for model training and testing.

Data Acquisition and Preprocessing:

Collecting large datasets from publicly available sources like Common Crawl or academic datasets.

Cleaning and preprocessing the data to make it suitable for training.

This includes tokenization, removing non-relevant data, and data formatting. We have observed the use of PyTorch methods to do this.

Model Selection and Training:

Choosing a base model architecture suitable for LLMs, such as Transformer models.

via the method calls in PyTorch

Customizing and scaling the model architecture in PyTorch or TensorFlow to fit their needs. You could RunPod.io

Training the model on the processed data, using PYTORCH methods → leveraging Google Colab's GPU resources.

Testing and Evaluation:

Evaluating the model's performance on various NLP tasks.

Fine-tuning model parameters based on evaluation results. Hyper-parameter optiimization is a very specialized skill in ML Model Engineering.

Application Development:

Building a web interface using HTML, CSS, and JavaScript.

Integrating the trained model with the web interface using Flask or a similar framework for Python.

Deployment and Iteration:

Deploying the application on a cloud platform: For the sake of our class project here: Your way of submitting your project is to provide to me the Share LINK to your Google Collab Notebook → And make

psigurd@gmail.com

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an Editor of the Notebook.

Gathering user feedback and iterating on the model and application based on this feedback.

Documentation and Presentation:

Documenting the entire process, challenges, and learnings.

Preparing a presentation to showcase their project, covering the technical aspects and the user experience.

Diagrams and Illustrations.

Learning Outcomes

Understanding the complexities and challenges of building a LLM AI: Both building the technology (Code Plus Compute), and the Project Management.

Gaining practical experience in data preprocessing, model training, and application development.

Learning to collaborate effectively in a diverse team with different skill sets.

Developing problem-solving and project management skills.

This case study gives students an insight into the real-world application of AI technologies and encourages them to approach their projects systematically and collaboratively.

Creating a Google Colab notebook for Alice and her team involves setting up an environment with the necessary libraries and starter code for their Large Language Model (LLM) AI project. Here's a basic outline of what the notebook and resources might look like:

Google Colab Notebook Setup

Create a New Notebook:

Go to

Google Colab

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Click on New Notebook to create a fresh notebook.

Install Required Libraries:

In the first cell of the notebook, install PyTorch, TensorFlow, and any other required libraries.

pythonCopy code

!pip install torch torchvision !pip install tensorflow

Import Libraries:

In the next cell, import the installed libraries.

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import torch import tensorflow as tf

Starter Code for Data Preprocessing:

Include code snippets for basic data preprocessing.

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# Example code for data loading and preprocessing def load_data(filepath): # Code to load data from the filepath return data def preprocess_data(data): # Code to preprocess data return processed_data

Model Setup:

Starter code for setting up a basic model architecture.

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# Example PyTorch model setup class MyModel(torch.nn.Module): def __init__(self): super(MyModel, self).__init__() # Initialize model layers def forward(self, x): # Define forward pass return x

Training Loop:

Basic structure of a training loop.

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# Example training loop for epoch in range(num_epochs): for batch in data_loader: # Training steps

Resource Citations

PyTorch Documentation:

For in-depth understanding and advanced functionalities:

PyTorch Docs

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TensorFlow Documentation:

Comprehensive guide and API reference:

TensorFlow Docs

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Google Colab Tutorials:

Getting started with Google Colab:

Welcome to Colaboratory

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Transformer Models:

Original Transformer paper for understanding the architecture:

"Attention Is All You Need"

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Dataset Resources:

Common datasets for NLP:

Hugging Face Datasets

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Large Language Models:

Overview and tutorials:

Hugging Face Course

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By utilizing these resources, Alice and her team can start with the basics and gradually expand their project. They should continuously refer to the documentation and resources for advanced functionalities and troubleshooting.