hjLabs AI Playground
Starting the on-device runtime…
Qwen3 0.6B is a 0.6B-parameter ONNX model that generates text a token at a time in response to a prompt, streaming its reply as it decodes. On this page it runs entirely inside your browser: the 928 MB download comes straight from the Hugging Face repository “onnx-community/Qwen3-0.6B-ONNX” into your browser cache, and every inference after that executes on your own GPU through WebGPU. This is a serious download — do it once on a connection you are not paying by the megabyte for. Once it is in the Cache Storage API it stays there, and subsequent visits load it from disk in seconds. The model is multilingual, so it is not restricted to English input.
Emits an explicit <think> block before answering, which makes its reasoning easy to inspect; large vocab means a large download. In practice people reach for a model like this for drafting, rewriting, summarising a pasted passage, or simply having a conversation that no server ever sees.
Available precisions: q4 (4-bit) and q8 (8-bit). q4 comes first because ONNX Runtime executes 4-bit block-quantised matmuls natively on WebGPU, which is what makes decoding fast enough to be usable — it is a speed choice at least as much as a size one. Hugging Face records about 19k 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 Qwen3 0.6B here is the one-off 928 MB download, and once that is cached the page keeps working with the network switched off.
qwen3 0.6bqwen3 0.6b onnxqwen3 0.6b browserrun qwen3 0.6b locallyonnx-community/qwen3-0.6b-onnxchat modelqwen3 0.6b webgpuqwen3 0.6b download sizefree chat modeltransformers.js model
About 928 MB for the q4 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. onnx-community/Qwen3-0.6B-ONNX 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.