Hobby project, I wanted to "ship a useful model in a web browser". so I distilled a small sentence encoder from MiniLM with ternary quantization-aware training. Also wrote the inference engine from scratch and shipped in Rust → WASM SIMD.

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.

This is cool!

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.

This would be nice as an Astro (or generic meta-framework plugin) that automatically parses all generated html files and generates a small db of embeddings.

This way on the frontend you can lazily load this. Maybe you could even store the HNSW in chunks and just load the pieces you need for your specific search query.

i.e. like https://pagefind.app/ but to get fully static vector search.

so this is really cool and I think could be the missing piece for something I wanted to build, I found this awhile back and using https://github.com/npiesco/absurder-sql you could keep the entire raw corpus in browser (persisted via IndexedDB/SQLite)...then you could generate + cache embeddings on demand with Ternlight (instead of pre-indexing everything i.e., https://weaviate.io/blog/chunking-strategies-for-rag). then this opens up the door for Reciprocal Rank Fusion (RRF) aka hybrid retrieval where you combine FTS5/BM25 from the native SQLite plues the semantic search using from TernLight!

This would be a pretty cool addition to the duckdb HNSW search project I found on here some time ago: https://github.com/jasonjmcghee/portable-hnsw

What I think is really cool is that the search happens using http range queries across statically hosted parquet files.

I think things like this could bloom into a relatively open and distributed search ecosystem that isn’t controlled by major corporations.

Nice work.

It’s advertised 7MB, but also comes with a 5MB mini version.

Looks like mini saves space by using 256 element vectors internally instead of 384, but then projects it up to 384 at the end for compatibility.

It’s a third smaller, but the loss is not linear, looks like you give up less than 1/3 of information with the smaller data path.

Demo works quite strangely. For example "how to use typescript with createContext" show only typescript entries on top. Similarity search failed.

Cool project! I tried something similar a while ago [1] - I wanted to load up an embedding model and semantically order texts, all in the browser.

So I pull ONNX weights from HuggingFace (MPNet, MiniLM), use Transformers.js to embed, and use a clusterer from scikit-learn (running on pyiodide - it was a surprise to me that this worked flawlessly) on the page - all client-side.

[1] http://sol.quipu-strands.com/

Thank you for this! Local models will bring privacy at some point, and I already know an excellent use case for such a small embedding model (cheap and fast search in a product base). Relying on the CPU is also a plus in my case.

Can the 30 second embedding time be done beforehand and sent to the browser?

Inference is nice and quick after that.

I added an offline search engine to app.wazzup.im/search (no login or payment required).

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.

FWIW -- Granite r2 small is a 30M model, still small enough to run on CPU, and a good baseline for fine tunes.

Great, now my websites are gonna push entire LLMs onto my browser in order to use my CPU to make inferences about my shopping habits or whatever.

Prime example of wasm supremacy over JavaScript. Stack machines for the win hehe

What we need is a W3C LLM API like the one Chrome already offers: https://developer.chrome.com/docs/ai/built-in

    Language.complete('the quick brown fox jumped over the lazy') 

    Image.generate('a spaceship flying toward a planet')

Interesting project. Happy to see someone who shares an interest in tiny vector embeddings models. I've worked on tiny (1MB - 4MB, 250K - 950K parameters) embeddings models called BERT Hash https://huggingface.co/blog/NeuML/bert-hash-embeddings

Keep up the great work!

That's really impressive, congratulations. It's nice to see novel applications of browser models.

Why do these things download into the browser automatically? This could be used to distribute malware and also or hog excessive browser memory.

Very cool! I'd love to point it at my own corpus to index/embed. Would be cool if you could give it a link to a markdown file or even a website to crawl.

cool stuff

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