Rapid Application Development Using Large Language Models (RADLLM) – Perfil

Esquema Detallado del Curso

Introduction

  • Meet the instructor.
  • Create an account at courses.nvidia.com/join

From Deep Learning to Large Language Models

  • Learn how large language models are structured and how to use them:
    • Review deep learning- and class-based reasoning, and see how language modeling falls out of it.
    • Discuss transformer architectures, interfaces, and intuitions, as well as how they scale up and alter to make state-of-the-art LLM solutions.

Specialized Encoder Models

  • Learn how to look at the different task specifications:
    • Explore cutting-edge HuggingFace encoder models.
    • Use already-tuned models for interesting tasks such as token classification, sequence classification, range prediction, and zero-shot classification.

Encoder-Decoder Models for Seq2Seq

  • Learn about forecasting LLMs for predicting unbounded sequences:
    • Introduce a decoder component for autoregressive text generation.
    • Discuss cross-attention for sequence-as-context formulations.
    • Discuss general approaches for multi-task, zero-shot reasoning.
    • Introduce multimodal formulation for sequences, and explore some examples.

Decoder Models for Text Generation

  • Learn about decoder-only GPT-style models and how they can be specified and used:
    • Explore when decoder-only is good, and talk about issues with the formation.
    • Discuss model size, special deployment techniques, and considerations.
    • Pull in some large text-generation models, and see how they work.

Stateful LLMs

  • Learn how to elevate language models above stochastic parrots via context injection:
    • Show off modern LLM composition techniques for history and state management.
    • Discuss retrieval-augmented generation (RAG) for external environment access.

Assessment and Q&A

  • Review key learnings.
  • Take a code-based assessment to earn a certificate.