LLM (Large Language Models) Training in Astana, Kazakhstan

This training offers a comprehensive introduction to large language models (LLMs), covering how they work, their capabilities, use cases, and responsible application. Through practical exercises, participants will explore the fundamentals of natural language processing (NLP), prompting techniques, fine-tuning concepts, and deployment considerations for LLM-based applications.

CONCEPTS, ARCHITECTURE, AND PROMPTING

Introduction to LLMs and NLP Evolution

  What are LLMs and how they differ from traditional NLP

  The rise of transformer architecture (BERT, GPT, T5, etc.)

  Overview of key LLMs: GPT-4, LLaMA, Claude, Mistral, etc.

LLM Internals Simplified

  Tokenization, embeddings, attention mechanism

  Pre-training vs. fine-tuning vs. in-context learning

  Model size, training data, and inference basics

Prompting Essentials

  Zero-shot, one-shot, and few-shot prompting

  Prompt formatting and temperature/top-k settings

  Chain-of-thought prompting and role prompting

APPLICATIONS, OPEN-SOURCE TOOLS, AND ETHICS

LLM Use Cases and Design Patterns

  LLMs for summarization, content generation, Q&A, and chatbots

  RAG (Retrieval-Augmented Generation) and prompt chaining concepts

  Fine-tuning vs. prompt engineering trade-offs

Working with LLM APIs and Open-Source Models

  Using Hugging Face Transformers and pre-trained models

  Calling LLM APIs using Python (OpenAI, Cohere, etc.)

  Hosting local models with tools like LangChain, LlamaIndex, or Ollama

Evaluation, Limitations, and Ethics

  Hallucinations, bias, and safety concerns

  Responsible use of LLMs in production

  Output evaluation metrics and human-in-the-loop feedback

  Summary discussion, future trends, and next learning steps