What are large language models, and how to deploy them?
In recent years, the world of artificial intelligence (AI) has been revolutionized by the advent of large language models. These models, such as OpenAI’s GPT-3, have showcased the immense potential of AI in understanding and generating human-like text. This article will delve into what exactly large language models are and how to deploy them for various applications.
Large language models are a class of artificial intelligence models that have been trained on vast amounts of text data to understand, generate and manipulate human language.
These models utilize deep learning techniques, specifically a type of neural network called a transformer, to process and learn patterns from text data. The result is a model capable of comprehending context, semantics and syntax in human language, allowing it to generate coherent and contextually relevant text.
OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) is one of the most prominent examples of a large language model. With 175 billion parameters (learnable weights), GPT-3 can perform a wide range of tasks, from language translation and text generation to code completion and conversation.
Related: What is prompt engineering and how does it work
In addition to prompting LLMs, many developers are now also experimenting with fine-tuning. I describe in The Batch how to choose from the growing menu of options for building applications with LLMs: Prompting, few-shot, fine-tuning, pre-training. https://t.co/NgPg0snzNt
Deploying a large language model involves making it accessible to users, whether through web applications, chatbots or other interfaces. Here’s a step-by-step guide on how to deploy a large language model:
The versatility of large language models enables their deployment
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