Training Hands-On: Generating AI with Large Language Models

Training Hands-On: Generating AI with Large Language Models

Introduction:

Large language models (LLMs) like GPT-4 are revolutionizing the world of data science. In this hands-on training, you will explore the breakthroughs in deep learning that power LLMs, with a focus on transformer architectures. You will witness the incredible capabilities of LLMs and how they are transforming the game for developing machine learning models and successful data products. Through code demonstrations using Hugging Face and PyTorch Lightning, you will gain practical skills for working with LLMs, from training to deployment. By the end of this session, you will have a solid understanding of LLMs and valuable experience with GPT-4. Join us on this journey to unlock the power of LLMs and accelerate your data science career.

Full Article: Training Hands-On: Generating AI with Large Language Models

Large Language Models (LLMs) such as GPT-4 are revolutionizing the field of data science by empowering capabilities that were once considered science fiction. The Generative AI with Large Language Models: Hands-On Training is a comprehensive program that explores the breakthroughs in deep learning that are driving this transformation. By focusing on transformer architectures, participants will gain a firsthand understanding of the vast potential of LLMs like GPT-4.

The training offers a deep dive into how LLMs are reshaping the landscape of machine learning model development and commercial data products. Attendees will witness how LLMs can enhance the creative abilities of data scientists, propelling them towards becoming proficient data product managers.

Through interactive code demonstrations utilizing Hugging Face and PyTorch Lightning, participants will gain practical knowledge of the entire lifecycle of working with LLMs. From efficient training techniques to optimized deployment in production, attendees will acquire directly applicable skills for harnessing the power of LLMs.

By the end of this dynamic training session, participants will have a solid foundation in LLMs and hands-on experience with GPT-4. This program equips data scientists and engineers with the tools to unlock the potential of LLMs and leverage them for their projects.

### Introduction to Large Language Models (LLMs)
The training comprises four modules that elucidate the concept of Large Language Models and guide participants in training their own models. It also sheds light on the commercial value that LLMs offer. Some topics covered in this section include:
– A Brief History of Natural Language Processing
– Transformers
– Subword Tokenization
– Autoregressive vs. Autoencoding Models
– ELMo, BERT, and T5
– The GPT Family
– LLM Application Areas

### The Breadth of LLM Capabilities
Participants will explore the immense capabilities of LLMs, including progress made by the GPT Family and key updates with GPT-4. The training also covers how to access OpenAI APIs, such as GPT-4, and leverage LLM Playgrounds.

### Training and Deploying LLMs
This section delves into the technical aspects of training and deploying LLMs. Participants will learn about hardware acceleration options, such as CPU, GPU, TPU, IPU, and AWS chips. The training provides insights into the Hugging Face Transformers Library and best practices for efficient LLM training. Topics in this module include:
– Parameter-efficient fine-tuning (PEFT) with low-rank adaptation (LoRA)
– Open-Source Pre-Trained LLMs
– LLM Training with PyTorch Lightning
– Multi-GPU Training
– LLM Deployment Considerations
– Monitoring LLMs in Production

### Getting Commercial Value from LLMs
In this module, participants will discover how LLMs can enhance the overall Machine Learning (ML) process. They will learn about automatable and augmentable tasks and gain insights into best practices for successful A.I. teams and projects. Moreover, participants will get a glimpse of what the future holds for A.I.

The training offers valuable external resources, including source code, presentation slides, and a Google Colab notebook. These resources enable engineers and data scientists to have an interactive and enriching experience while implementing Generative AI in their workspaces.

To build and deploy LLM models using Huggingface and PyTorch Lighting, the following resources are essential:

– Source code
– Presentation slides
– Google Colab notebook

In summary, the Generative AI with Large Language Models: Hands-On Training provides a comprehensive and hands-on approach to understanding and leveraging LLMs. It equips participants with the knowledge and skills required to excel in the rapidly evolving field of data science, empowering them to create cutting-edge machine learning models and data products. Don’t miss out on this opportunity to unlock the potential of LLMs and pave the way for innovation in your projects.

Summary: Training Hands-On: Generating AI with Large Language Models

The “Generative AI with Large Language Models: Hands-On Training” is a comprehensive and interactive training program that explores the capabilities and applications of Large Language Models (LLMs) like GPT-4. The training covers key topics such as the history of Natural Language Processing, transformers, subword tokenization, autoregressive vs. autoencoding models, and more. Participants will also learn about hardware acceleration, efficient LLM training techniques, parameter-efficient fine-tuning, and LLM deployment considerations. The training provides valuable resources like source code, slides, and a Google Colab notebook. Led by certified data scientist professional Abid Ali Awan, this training offers practical insights and skills for harnessing the power of LLMs in machine learning and data science projects.

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Q1: What is data science and why is it important?

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