hjLabs AI Playground
Starting the on-device runtime…
MedEmbed Small is a 33M-parameter ONNX model that turns text into a dense vector whose distances encode meaning. On this page it runs entirely inside your browser: the 35 MB download comes straight from the Hugging Face repository “Romelianism/MedEmbed-small-v0.1” into your browser cache, and every inference after that executes on your own GPU through WebGPU. At this size the download is over before you have finished reading this paragraph, even on a phone tethered to a hotspot, and it costs almost nothing to keep cached alongside a dozen other models. It is an English-language model; expect quality to fall off sharply on other languages.
Biomedical retrieval in 35 MB — the smallest model on this page and the one to reach for when the corpus is clinical rather than general. In practice people reach for a model like this for semantic search, deduplication, clustering and retrieval-augmented generation — all with a local index.
Available precisions: q8 (8-bit) and fp32 (full precision). q8 comes first because it roughly halves the download at a quality cost you will struggle to measure on this class of model; fp32 is there when you want the reference numbers. Hugging Face records about 85 downloads a month. It is a specialist choice rather than a default, which is exactly why it is worth trying when the popular model gets your particular input wrong.
Because there is no inference server behind this page, there is nothing to rate-limit and nothing to bill: the only cost of running MedEmbed Small here is the one-off 35 MB download, and once that is cached the page keeps working with the network switched off.
medembed smallmedembed small onnxmedembed small browserrun medembed small locallyromelianism/medembed-small-v0.1embeddings modelmedembed small webgpumedembed small download sizefree embeddings modeltransformers.js model
About 35 MB for the q8 build, including the tokenizer and config files the runtime also has to fetch — this is the whole payload, not just the weights file. It is downloaded once and then served from the browser Cache Storage API.
Yes, through the WebAssembly fallback, which is slower but works anywhere. WebGPU (Chrome or Edge 113+, Safari 26+) is what makes it feel interactive. On WASM, prefer the smaller quantized build.
No. Romelianism/MedEmbed-small-v0.1 is a public repository, so your browser fetches the ONNX files directly over HTTPS. There is no account, no key and no server in between.
No. The model weights are downloaded from Hugging Face to your browser once, and every inference after that runs on your own GPU through WebGPU. Your text, images and audio are never sent anywhere — hjLabs.in has no inference server and no way to see your input.