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.
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.
Loading model... + Loading search results...
Or sometimes "Service Worker API is available and in use." + "Loading search results...".
Tested on Macbook Pro M1 8gb RAM and Macbook Air M1 8gb RAM. Mostly likely because of M series of chips. All tests were done on Brave/Chrome.
Does not work on iPhone 11 Pro Max and iPhone 16 Pro. Mostly likely because of A series of chips. Tests were done on Safari and Chrome and it crashes on both.
The workaround is to unregister/stop the service worker from the DevTools > Application tab > Service workers.
Inference is nice and quick after that.
Keep up the great work!
"Hmm, 7MB would barely make a dent in the size of the app and allow us to do some of our basic ML without calling the backend"
Probably a lot more practical to use this though: https://developer.apple.com/apple-intelligence/
Re excessive browser memory use: Yes, it adds non-negligible weight, but again, you could already achieve excessive browser memory usage before this. For comparison, a true color 1080p image, uncompressed (which is needed for actual display on screen) is only slightly smaller at 6.22Mb.
There are JS files larger than 7MB in the wild. They run on JIT engines that displayed severe CVEs over the years. PDFs, video running directly on special hardware encoders. That's the web now.
A WASM model is not that offensive.
Language.complete('the quick brown fox jumped over the lazy')
Image.generate('a spaceship flying toward a planet')