LLMs & Foundation Models Explained
A practical guide to Large Language Models (LLMs) and foundation models, covering architectures, training concepts, fine-tuning, inference, embeddings, RAG, and real-world AI application development.
What is Large Language Model (LLM)
A Large Language Model is a sophisticated mathematical function that predicts what word comes next for any piece of text"
LLM is a type of foundation model specifically designed to understand and generate human language.
🧱 Foundation Models (FMs)
Large-scale models trained on broad data that can be adapted to a wide range of downstream tasks.
- Examples:
GPT-3,BERT,DALL-E,Stable Diffusion
Characteristics:
- Trained on massive datasets (text, images, code)
- Capable of zero-shot and few-shot learning
- Serve as a base for fine-tuning on specific tasks
🧠 Large Language Models (LLMs)
A subset of foundation models that are specifically designed to understand and generate human language.
Examples: GPT-3, BERT, T5
- Characteristics:
- Trained on vast amounts of text data
- Able to recognize and interpret human language
- Flexible: can perform tasks like text generation, translation, summarization, and question-answering