Introduction to Large Language Models (LLMs) Training Course
Large Language Models (LLMs) are deep neural network architectures capable of generating natural language text based on provided input or context. Trained on extensive text data from diverse domains and sources, they capture the syntactic and semantic patterns inherent in natural language. LLMs have delivered remarkable results across a range of natural language processing tasks, including text summarization, question answering, and text generation.
This instructor-led, live training session (available online or onsite) is designed for developers at beginner to intermediate levels who aim to leverage Large Language Models for various natural language processing tasks.
Upon completion of this training, participants will be able to:
- Configure a development environment integrated with a prominent LLM.
- Construct a foundational LLM and fine-tune it using a custom dataset.
- Apply LLMs to various natural language tasks such as text summarization, question answering, and text generation.
- Debug and evaluate LLMs using tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
Format of the Course
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- To arrange a customized training session for this course, please contact us.
Course Outline
Introduction
- What are Large Language Models (LLMs)?
- LLMs compared to traditional NLP models.
- Overview of LLM features and architecture.
- Challenges and limitations of LLMs.
Understanding LLMs
- The lifecycle of an LLM.
- How LLMs function.
- Key components of an LLM: encoder, decoder, attention mechanisms, 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 deploying an LLM.
- Fine-tuning an LLM on a custom dataset.
Text Summarization
- Understanding text summarization tasks and their applications.
- Utilizing an LLM for extractive and abstractive text summarization.
- Evaluating summary quality using metrics such as ROUGE, BLEU, etc.
Question Answering
- Understanding question answering tasks and their applications.
- Utilizing an LLM for open-domain and closed-domain question answering.
- Evaluating answer accuracy using metrics such as F1, EM, etc.
Text Generation
- Understanding text generation tasks and their applications.
- Utilizing an LLM for conditional and unconditional text generation.
- Controlling the style, tone, and content of 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 common errors and bugs in LLMs.
- Monitoring and visualizing the training process using TensorBoard.
- Simplifying training code and improving performance with PyTorch Lightning.
- Loading and preprocessing data using Hugging Face Datasets.
Summary and Next Steps
Requirements
- A foundational understanding of natural language processing and deep learning.
- Practical experience with Python and either PyTorch or TensorFlow.
- Basic programming proficiency.
Audience
- Developers.
- NLP enthusiasts.
- Data scientists.
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 2600 € + VAT*
Contact us for an exact quote and to hear our latest promotions
(*The final price may vary depending on the technical specialization of the course, the level of customization, the method of delivery and the number of learners)
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