Streaming Custom Data
Learn how to stream custom data from the server to the client.
Summary
Embedding sizes in industry have grown from about 200-300 dimensions to commonly between 768 and 1536 or higher, driven by the scaling of models like BERT and GPT-3 and the parallelization needs of transformer architectures.
The shift from custom, in-house embeddings to accessible, API-based solutions and standardization through platforms like HuggingFace has led to greater uniformity and larger dimension sizes. Sizes now routinely reach 4096 dimensions, with further growth influenced by increased data, task complexity, and ongoing work on optimizing embedding efficiency.
