Magnitude (Plasticity) is a fast, memory-efficient Python library and file format for working with word and vector embeddings in machine learning. With about 1,700 stars on GitHub, it was developed by Plasticity to make loading and querying embeddings such as word2vec, GloVe and fastText dramatically simpler and faster than the usual tooling.
Working with pre-trained embeddings usually means slow load times and heavy memory use. Magnitude stores embeddings in its own optimized format that supports fast similarity querying, lazy loading and advanced out-of-vocabulary handling — including strategies for misspellings and typos — so models can look up vectors efficiently even on modest hardware. The project provides pre-converted Magnitude versions of popular embedding sets, so a corpus can be downloaded ready to query interactively without converting or loading everything into memory up front. Note that this Magnitude is Plasticity’s embeddings library — a distinct project from the other product of the same name listed on Tooloso.
Benchmarks published alongside the library compare its lookup speed and memory footprint against common alternatives used for the same job, which is part of why it became a default choice in many teaching materials.
Magnitude is free and open source under the MIT license. The library and its pre-converted embedding files cost nothing to download and use in commercial or research projects. There is no hosted service, and development is community-paced, with the repository last updated in 2023.
NLP engineers and researchers who still build on classic word embeddings and want fast, lightweight tooling, plus anyone maintaining legacy embedding-based models in production. It is a developer library, so expect a Python-first workflow rather than a graphical interface.