Introduction to Large Language Models (LLMs) - Plan Szkolenia

Large Language Models (LLMs) are deep neural network models that can generate natural language texts based on a given input or context. They are trained on large amounts of text data from various domains and sources, and they can capture the syntactic and semantic patterns of natural language. LLMs have achieved impressive results on various natural language tasks such as text summarization, question answering, text generation, and more.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to use Large Language Models for various natural language tasks.

By the end of this training, participants will be able to:

Set up a development environment that includes a popular LLM.
Create a basic LLM and fine-tune it on a custom dataset.
Use LLMs for different natural language tasks such as text summarization, question answering, text generation, and more.
Debug and evaluate LLMs using tools such as TensorBoard, PyTorch Lightning, and Hugging Face Datasets.

Format of the Course

Interactive lecture and discussion.
Lots of exercises and practice.
Hands-on implementation in a live-lab environment.

Course Customization Options

To request a customized training for this course, please contact us to arrange.

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Plan Szkolenia

Introduction

What are Large Language Models (LLMs)?
LLMs vs traditional NLP models
Overview of LLMs features and architecture
Challenges and limitations of LLMs

Understanding LLMs

The lifecycle of an LLM
How LLMs work
The main components of an LLM: encoder, decoder, attention, embeddings, etc.

Getting Started

Setting up the Development Environment
Installing an LLM as a development tool, e.g. Google Colab, Hugging Face

Working with LLMs

Exploring available LLM options
Creating and using an LLM
Fine-tuning an LLM on a custom dataset

Text Summarization

Understanding the task of text summarization and its applications
Using an LLM for extractive and abstractive text summarization
Evaluating the quality of the generated summaries using metrics such as ROUGE, BLEU, etc.

Question Answering

Understanding the task of question answering and its applications
Using an LLM for open-domain and closed-domain question answering
Evaluating the accuracy of the generated answers using metrics such as F1, EM, etc.

Text Generation

Understanding the task of text generation and its applications
Using an LLM for conditional and unconditional text generation
Controlling the style, tone, and content of the generated texts using parameters such as temperature, top-k, top-p, etc.

Integrating LLMs with Other Frameworks and Platforms

Using LLMs with PyTorch or TensorFlow
Using LLMs with Flask or Streamlit
Using LLMs with Google Cloud or AWS

Troubleshooting

Understanding the common errors and bugs in LLMs
Using TensorBoard to monitor and visualize the training process
Using PyTorch Lightning to simplify the training code and improve the performance
Using Hugging Face Datasets to load and preprocess the data

Summary and Next Steps