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.
Inference is nice and quick after that.
"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/
Keep up the great work!
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')
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.
Any comparisons with other tiny embedding models? Did you start from MiniLM-L6 because it's an especially good model in its class? It's hard to figure this out since all you provide is "Retrieval (SciFact NDCG@10)".
But the claimed performance seems way off, I get only 35 emb/sec in firefox on a i5-4570 rather than 400/sec. Is there an issue with falling back to a non-SIMD path? I'll try a native Rust binary next.
The only post-training quantization I applied was int4 on the embedding layer, and I ran a small ablation there to find the sweet spot between size and quality.