Ternlight – 7 MB embedding model that runs in browser (WASM)
Comments
soycaporal
maelito
Awesome. We have a dictionary of words to OpenStreetMap tags here : https://codeberg.org/cartes/web/src/branch/master/components...
Do you think your work could help us let users type "pancake" and get "crêpe" without writing an explicit "pancake = crêpe" dictionary entry ?
In practice : if I understand well, your lib would first need to download 5 Mb, once and for all, and would then be used as we use Fuse.js right now ?
How well does it handle languages other than English ?
Could it be "trained" on the OpenStreetMap tag wiki ?
Thanks a lot for your work.
soycaporal
The corpus is mainly trained in english, unfortunately no other languages have been included in the distillation training. Yes it would work like fuse.js, but unlocks semantic search.
Source code has the entire embedding distillation pipeline includes dataset preparation. You could run the same distillation training (but not sure if teacher model used a multi-language dataset).
Open up an issue on the github repo and I can reply with details
yorwba
The OSM tag wiki would probably not make for good training data because it only has a single short description for each tag (and even on the French wiki many descriptions seem to be in English?) whereas you want to map multiple descriptions to the same tag.
Ideally you would have real query data (e.g. from cartes.app telemetry), then you could get a LLM to write a bespoke Overpass query for each one and use that as the ground truth. Alternatively, start from the list of OSM tag values used in the wild and ask an LLM to list possible reasons to visit that POI.
You could then use that data to finetune an embedding model for your use case. But, you know, somewhere in that model there's going to be a token vocabulary that the model knows about and at the other end you get a similarity score for each tag value. If you don't need to support complex queries where interactions between words matter ("any restaurant that is NOT Korean"), you could get away with a simple list of words and tags that they match to. Which is right where you started, except it could be more exhaustive. Why limit yourself to two Korean dishes when you can have a LLM list many more for you?
maelito
Thanks a lot !
dwheeler
Thanks! I strongly suggest copy-pasting that explanation to your web page, that's a nice summary.
versteegen
Nice, I'm really interested in using this for simple semantic search in a native desktop application.
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.
soycaporal
the base model I clocked it at 5 ms per embedded on my mac studio. There is a mini variant (the demo version) that is sub - 2 ms. It could be a SIMD issue.. I'll look into this for better runtime support (also fee free to file an issue)
heltale
Same! I’m trying to find small models that can embed effectively to enable BM25/hybrid search over a large number of documents for a personal information repository. Ideally, it should run on consumer hardware.
bge-small-en-v1.5 is one that is comparable and what we’re working with for now.
dannyw
Huge kudos for sharing everything including your training code. Awesome project!
keynha
0.84 Spearman fidelity to the MiniLM teacher at ternary precision is a striking result. How much of that is the quantization-aware training doing the work, versus what a post-training ternary quant of the same encoder would give you?
soycaporal
It's entirely the QAT. The whole distillation process is quantization-aware from the start, so the ternary weights are learned rather than fitted after the fact.
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.
sodimel
Thank you for this tool!
We've just used it to embed the entire django doc + our private knowledge base, allowing us to search in the 2 sources instantly!
soycaporal
super cool use case! Hopefully it can provide quality embeddings + retrieval. Would love to learn to results/issues or feedback. Please feel free to file for issues on github
abrookewood
What is the process for adding different text? What are the limitations on that process? The demo is very cool by the way.
fellowniusmonk
Awesome! Besides size, how does this compare to gte-small?
soycaporal
gte-small outscores all-MiniLM-L6 on MTEB (~61 vs ~56 avg per the GTE paper). MiniLM is ternlight's teacher (ternlight holds 0.84 Spearman fidelity to teacher). I haven't run a head-to-head yet; STS-B/MTEB numbers are on the roadmap. Also on the roadmap is to distill gte-small as teacher.
dirteater_
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.
Waterluvian
Agree. But this also reminds me fondly of the days where the sounds of my computer so intimately indicated what’s going on.
dannyw
I really love the coil whine of my GPU (a 5090 FE) when it’s doing LLM stuff. I can hear the different stages, like prefill and decode, and the sounds actually make me reminisce about dial up.
mwcz
Amiga floppy disk sounds are the deepest of sense memories.
aitchnyu
Also the rotating sound of my 8.4 GB HD. Apparently noisy HDs are damaged HDs.
Kimitri
And the sound looping with the filter cycling on and off when things went sideways. Good times...
jonnonz
Same here, when the fans started up I got startled. However my bread toaster often scares me too
soycaporal
CPU cycle maxxing, who said GPUs were special?
aziis98
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.
weitendorf
We really wanted to use sqlite-vec for this for our SSG but last we checked it hadn’t implemented HNSW/had good support for running vector search in-browser yet (I think it was still doing full-table scans?). I was pretty disappointed because after so many months/years, to not have that suggested to me that they weren’t up to task of delivering on their project, and I had recommended them as a worthy project for a grant I had also applied for, that they won and I didn’t.
If anybody knows of a good solution in this space, or if I’m wrong about SQLite-vec, please let me know. For our own SSG we’ve basically decided that we’ll give it a couple months while we work on other infra we want, then if they’re still not done we’ll just do it ourselves.
kamranjon
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.
lexh
Similar idea here that may be of interest: SQLite DB on static host via HTTP range + WASM.
soycaporal
very cool, I'll look into this. Thanks for sharing.
TheTaytay
Cool idea. I love range requests and other static-hosted client-navigable formats!
scritty-dev
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!
chris-hartwig
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.
soycaporal
that's great! let me know if there is anyway I can support, or any specific use case a roadmap could address!
abhgh
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.
WhitneyLand
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.
aetherspawn
Can the 30 second embedding time be done beforehand and sent to the browser?
Inference is nice and quick after that.
soycaporal
yes, you could run a 1 time indexing run on the server side, and just ship the embeddings to frontend
wazzup_im
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.
Barbing
In Safari, stuck on:
Loading model... + Loading search results...
Or sometimes "Service Worker API is available and in use." + "Loading search results...".
wazzup_im
This is a known issue and I am actively trying to find why this is happening. So far it's pretty good on Brave/Chrome.
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.
soycaporal
ohh thanks for the report.. probably has to do with wasm runtime.. Will note this as a known issue
wazzup_im
Np!
The workaround is to unregister/stop the service worker from the DevTools > Application tab > Service workers.
bvrmn
Demo works quite strangely. For example "how to use typescript with createContext" show only typescript entries on top. Similarity search failed.
CobrastanJorji
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.
iammrpayments
If you think about it, running a crypto miner without being asked is probably less annoying than downloading an entire LLM, but only the first will get you in jail.
paytonjjones
Ha, I was literally thinking this but from the other side.
"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/
antonvs
Disabling WASM is the new disable JavaScript
jbellis
FWIW -- Granite r2 small is a 30M model, still small enough to run on CPU, and a good baseline for fine tunes.
soycaporal
awesome, noted, looking for capable teacher models to distill other architectures
jbellis
voyage 4 nano is sota at the next size up
and if you really want the best teacher models it's probably the voyage commercial APIs
iberator
Prime example of wasm supremacy over JavaScript. Stack machines for the win hehe
esafak
What we need is a W3C LLM API like the one Chrome already offers: https://developer.chrome.com/docs/ai/built-in
yesidoagree
If it was like Math (Math.round, Math.PI, etc.) it could be Language, as in:
Language.complete('the quick brown fox jumped over the lazy')
Image.generate('a spaceship flying toward a planet')soycaporal
I think standardizing the runtime is pretty effective, it then open up portability
dmezzetti
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!
gaigalas
That's really impressive, congratulations. It's nice to see novel applications of browser models.
soycaporal
thank you! hopefully it can unlock some novel applications, that would be cool
newspaper1
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.
soycaporal
love the idea! Will think of a way to host it probably on huggingface
ljcoco
cool stuff
rvz
Why do these things download into the browser automatically? This could be used to distribute malware and also or hog excessive browser memory.
akoboldfrying
This doesn't add any malware risks beyond what a JavaScript-enabled browser already allows.
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.
gaigalas
That's... how the web works? You download things on demand.
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.
VaporJournalAPP
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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.