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Is anyone able to test it via AiStudio? I pay for Google's AI subscription, but any attempt to use this model results in a message telling me I've hit my rate limit.
I've also seemingly hit a rate limit on Gemini Pro 2.5 (on an account not subscribed to Gemini Advanced) yesterday, even though my last query is weeks past.
Possibly there's a capacity shortage (I'd presume it all runs on the same Google hardware in the end), and they are prioritizing paid inference?
Probably a better link: https://developers.googleblog.com/en/introducing-gemma-3n/ Gemma 3n is a model utilizing Per-Layer Embeddings to achieve an on-device memory footprint of a 2-4B parameter model. At the same time, it performs nearly as well as Claude 3.7 Sonnet in Chatbot Arena.
Imagine a model smarter than most humans that fits on your phone. edit: I seem to be the only one excited by the possibilities of such small yet powerful models. This is an iPhone moment: a computer that fits in your pocket, except this time it's smart.
Now would I take AI as a trivia partner? Absolutely. But that's not really the same as what I look for in "smart" humans.
Download the Edge Gallery apk from github: https://github.com/google-ai-edge/gallery/releases/tag/1.0.0
Download one of the .task files from huggingface: https://huggingface.co/collections/google/gemma-3n-preview-6...
Import the .task file in Edge Gallery with the + bottom right.
You can take pictures right from the app. The model is indeed pretty fast.
Is anyone able to test it via AiStudio? I pay for Google's AI subscription, but any attempt to use this model results in a message telling me I've hit my rate limit.
Same here. I've also seemingly hit a rate limit on Gemini Pro 2.5 (on an account not subscribed to Gemini Advanced) yesterday, even though my last query is weeks past. Possibly there's a capacity shortage (I'd presume it all runs on the same Google hardware in the end), and they are prioritizing paid inference?
Probably a better link: https://developers.googleblog.com/en/introducing-gemma-3n/ Gemma 3n is a model utilizing Per-Layer Embeddings to achieve an on-device memory footprint of a 2-4B parameter model. At the same time, it performs nearly as well as Claude 3.7 Sonnet in Chatbot Arena.
It's not a 4B parameter model. The E4B variant is 7B parameters with 4B loaded into memory when using per-layer embedding cached to fast storage, and without vision or audio support.
What is "Per Layer Embeddings"? The only hit I can find for that term is the announcement blogpost. And for that matter, what is >mix’n’match capability in Gemma 3n to dynamically create submodels It seems like mixture-of-experts taken to the extreme, where you actually create an entire submodel instead of routing per token?
You can try it on Android right now: Download the Edge Gallery apk from github: https://github.com/google-ai-edge/gallery/releases/tag/1.0.0 Download one of the .task files from huggingface: https://huggingface.co/collections/google/gemma-3n-preview-6... Import the .task file in Edge Gallery with the + bottom right. You can take pictures right from the app. The model is indeed pretty fast.
Earlier quoted context omitted.
It's understanding.
Sure, if you still think the word has meaning.
I'd go as far as saying LLMs are meaning made incarnate - that huge tensor of floats represents a stupidly high-dimensional latent space, which encodes semantic similarity of every token, and combinations of tokens (up to a limit). That's as close as reifying the meaning of "meaning" itself as we ever come.
(It's funny that we got there through brute force instead of developing philosophy, and it's also nice that we get a computational artifact out of it that we can poke and study, instead of incomprehensible and mostly bogus theories.)