It's an embeddings model, not an LLM: text goes in, a 384-dim vector comes out, and cosine similarity between two vectors tells you how related the texts are — regardless of shared words ("reset my password" ↔ "I forgot my password" → 0.88). Used for semantic search, FAQ/intent matching, and clustering. Running it on-device means search-as-you-type semantic search is performant with no API dependencies.

Demo (2k React docs, fully on-device): https://ternlight-demo.vercel.app

Two tiers on npm: - @ternlight/base (7 MB, ~5 ms/embed, more capable embedings) - @ternlight/mini (5 MB wire, ~2.5 ms/embed).

Bundled for Node and browsers.

Repo - see technical details (MIT, training pipeline included): https://github.com/soycaporal/ternlight

Curious if this is something useful, what are the use cases for on-device embeddings.

Inference is nice and quick after that.

but also maybe you could put a button on the landing page to trigger the demo because it's a bit startling to hear my fans go crazy when opening a webpage.

First search downloads the model from the internet and subsequent runs are from the cache.

The model is very small so it's not the best for everything but it's good for basic math and coding.

Give it a try.