Large Language Models
Large language models like GPT-4 or Llama 3 have state-of-the-art capabilities such as general knowledge, steerability, advanced reasoning, math/science, tool use, data analysis, multilingual translation and more. Multi-modal models can apply the same capabilites across different modalities. But how do LLM's work? What is a LLM Model? How are LLM's trained? See the slideshow below or download to learn more
LLM Models (download pdf)What makes LLMs special ?
Based on transformer architecture LLM models are giants ( For ex. LLama 3 has 405 billion parameters) and can learn to understand human knowledge without supervision & without labelled datasets. LLM models can learn/understand patterns and representation of any sequence be it language, protein, biology, chemistry, etc.
LLMs are instructable universal functions
A single LLM model can perform multiple tasks such as QA, summarization, content/code generation, data analysis, advanced reasoning, translation and more. Models can be instructed to perform tasks for which they were never trained on. LLMs are excellent few-shot learners. Using prompt engineering you can guide & steer them to fulfil your request in real-time. LLMs can be multi-modal and so can be used in endless possible applications. LLMs simplify application development by replacing the need for separate code modules and rule-based systems with natural language prompts. Tasks like text parsing, data analysis, intent classification, image reasoning, translation which once required complex code can now be achieved with instructions inside a prompt.