👉 The sampling fluid, also known as the sampling mechanism or generator in language models, is a crucial component that enables the creation of diverse and contextually relevant outputs from the model. It operates by taking input prompts and generating responses based on a learned distribution of possible sequences, effectively simulating the process of human language generation. This fluid is trained on vast amounts of text data, allowing it to understand and mimic the nuances of human language. During inference, when a user provides input, the sampling fluid dynamically selects and combines elements from its internal memory or probability distributions to produce a response that is both coherent and contextually appropriate, making it an essential part of how language models generate text.
