Large Language Model Fundamentals

KEY CONCEPTS

• →Transformer architecture basics
• →Tokens and tokenization
• →Context windows and limitations
• →Temperature and sampling parameters
• →Pre-training vs fine-tuning
• →Embeddings and vector representations

COMMON TRAPS

• !Confusing pre-training with fine-tuning scope
• !Assuming larger context = always better
• !Mixing up temperature effects on output

OFFICIAL DOCUMENTATION

• [DOC]Introduction to Large Language Models

STUDY Q&A

• What is Artificial Intelligence (AI) in general terms?
  Artificial Intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect. AI encompasses a wide range of technologies, including rule-based systems, machine learning, and deep learning.
  Official source
• What is the main difference between traditional AI and Generative AI?
  Traditional AI focuses on recognizing patterns and making predictions or classifications based on existing data, while Generative AI creates new content such as text, images, or code by learning from large datasets. Generative AI models, like LLMs, can generate novel outputs rather than just analyze or classify data.
  Official source
• What is Natural Language Processing (NLP) and why is it fundamental for LLMs?
  Natural Language Processing (NLP) is a field of AI focused on enabling machines to understand, interpret, and generate human language. NLP is fundamental for LLMs because it allows them to process and generate text in a way that is meaningful and contextually appropriate for users.
  Official source
• What is the difference between supervised and unsupervised learning?
  Supervised learning uses labeled data to train models to predict outcomes, while unsupervised learning finds patterns or groupings in data without explicit labels. Supervised learning is common for classification and regression, whereas unsupervised is used for clustering and dimensionality reduction.
  Official source
• What is a machine learning model?
  A machine learning model is a mathematical representation trained on data to recognize patterns and make predictions or decisions without being explicitly programmed for each task.
  Official source
• What is a feature in an ML model?
  A feature is an individual measurable property or characteristic used as input to a machine learning model. Features are critical for the model to learn and make accurate predictions.
  Official source
• What does inference mean in an ML or LLM context?
  Inference is the process of using a trained model to make predictions or generate outputs based on new, unseen data. In LLMs, inference refers to generating text or answers from a prompt.
  Official source
• What is a foundation model and why is it fundamental to GenAI?
  A foundation model is a large, pre-trained model that serves as a base for a wide range of downstream tasks. It is fundamental to GenAI because it provides general capabilities that can be adapted to specific applications through fine-tuning or prompting.
  Official source
• What is a Large Language Model and what is it trained on?
  A Large Language Model (LLM) is a machine learning model trained on vast amounts of textual data to learn linguistic patterns, semantic relationships, and syntax. LLMs are designed to understand and generate natural language, adapting to multiple tasks without domain-specific training.
  Official source
• What do tokens represent and why is token capacity important?
  Tokens are the minimal units of text (words, subwords, or characters) processed by the model. Token capacity defines the maximum amount of text the model can handle in input and output; exceeding this limit leads to context loss or generation errors.
  Official source
• What is prompt tuning and how does it differ from fine-tuning?
  Prompt tuning involves optimizing the input prompts to guide the model's output, while fine-tuning adjusts the model's internal parameters using additional training data. Prompt tuning is less resource-intensive and does not require retraining the model.
  Official source

PRACTICE QUESTIONS

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