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Isn’t this basically what multimodal LLM does as well… it can do anything on the fly it can understand. What’s different here?
by tsunamifury 3y ago
Isn’t this basically what multimodal LLM does as well… it can do anything on the fly it can understand.
What’s different here?
- navanchauhan 3y agoThese are smaller scale model that you can export and run anywhere. Even the smallest multimodal LLM would be wayyyyyyy bigger than an exported model from this
- jatins 3y agoHow small are these models? can I export a model here and embed it in an Android/ios app?
- vineyardmike 3y agoYou can supposedly embed them for Arduinos, so an app should be no problem.
- jzombie 3y agoThe website says they can be embedded in a web app, and export to a format called TensorFlow Lite. I am sure you could embed it. https://www.tensorflow.org/lite https://www.tensorflow.org/lite
- pona-a 3y agohttps://www.tensorflow.org/lite/android https://www.tensorflow.org/lite/android TF Lite has first-class Android support with hardware acceleration if I'm not mistaken.
- jzombie 3y agoI think this is more like fine tuning an existing model to recognize features you specifically intend it to, and be light enough to run locally in a browser.
- tsunamifury 3y agoThanks!
- jerbear4328 3y agoIt's not even fine tuning, it's creating a model from scratch. This isn't like our modern huge models either, these tiny single-purpose models have been around for ages and are quite versatile. They're so small you can't just easily run them in the browser, but train them effectively, which is what this project lets you play around with! Super cool stuff.
- dbish 3y agoThe teaching part is what matters, it’s training (tuning in this case) a model, not just using a model already trained for inference (which is what I assume you mean). You’re providing new data that is used to update the model. Inference across an existing multimodal model doesn’t change how it classifies in any way.