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 specific inputs or contexts. Trained on vast volumes of textual data from diverse domains, these models capture the syntactic and semantic structures of human language. LLMs have demonstrated remarkable performance across a wide array of natural language processing tasks, including text summarization, question answering, and content generation.
This instructor-led, live training—available both online and onsite—is designed for developers at beginner to intermediate levels who aim to leverage Large Language Models for various natural language applications.
Upon completion of this training, participants will be equipped to:
- Configure a development environment utilising a widely adopted LLM.
- Construct a foundational LLM and fine-tune it using custom datasets.
- Apply LLMs to diverse natural language tasks, such as summarizing text, answering questions, and generating new content.
- Debug and assess LLM performance using industry-standard tools like TensorBoard, PyTorch Lightning, and Hugging Face Datasets.
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical drills.
- Live laboratory sessions for hands-on implementation.
Customization Options
- For bespoke training solutions tailored to your organization, please reach out to us to arrange.
Course Outline
Introduction
- What are Large Language Models (LLMs)?
- Distinguishing LLMs from traditional NLP models
- Key features and architectural overview of LLMs
- Challenges and limitations associated with LLMs
Understanding LLMs
- The lifecycle of an LLM
- Operational mechanisms of LLMs
- Core components: encoders, decoders, attention mechanisms, embeddings, and more
Getting Started
- Setting up the development environment
- Installing an LLM as a development tool, such as Google Colab or via 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 the task of text summarization and its practical applications
- Utilizing LLMs for extractive and abstractive summarization
- Evaluating summary quality using metrics such as ROUGE and BLEU
Question Answering
- Understanding the task of question answering and its applications
- Implementing LLMs for open-domain and closed-domain QA
- Assessing answer accuracy using metrics like F1 and EM
Text Generation
- Understanding text generation tasks and their use cases
- Employing LLMs for conditional and unconditional generation
- Controlling style, tone, and content via parameters such as temperature, top-k, and top-p
Integrating LLMs with Other Frameworks and Platforms
- Connecting LLMs with PyTorch or TensorFlow
- Integrating LLMs with Flask or Streamlit
- Leveraging LLMs on Google Cloud or AWS
Troubleshooting
- Identifying common errors and bugs in LLM implementations
- Monitoring and visualizing the training process with TensorBoard
- Simplifying training code and enhancing performance using PyTorch Lightning
- Loading and preprocessing data with Hugging Face Datasets
Summary and Next Steps
Requirements
- Familiarity with natural language processing concepts and deep learning principles.
- Proficiency in Python, along with experience in PyTorch or TensorFlow.
- Foundational programming knowledge.
Target Audience
- Software Developers
- NLP enthusiasts
- Data Scientists
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