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Comment by sbarre | Hacker News Reader

Can the Gemma models be downloaded to run locally, like open-source models Llama2, Mistral, etc ?

Or is your definition of "open" different?

Yes models can be downloaded locally. In addition to the python NN frameworks and ggml as options, we also implemented a standalone C++ implementation that you can run locally at https://github.com/google/gemma.cpp

Yes, you can get started downloading the model and running inference on Kaggle: https://www.kaggle.com/models/google/gemma ; for a full list of ways to interact with the model, you can check out https://ai.google.dev/gemma.

Can we have llamafile releases as well?

https://github.com/Mozilla-Ocho/llamafile

A small typo in your model link that breaks it. There’s an extra ; on the end.

Corrected - thanks :)

It should be possible to run it via llama.cpp[0] now.

[0] https://github.com/ggerganov/llama.cpp/pull/5631

Amazing how quickly this happened.

Mistral weights are released under an Apache 2.0 license, but Llama 2 weights are released under a proprietary license that prohibits use by large organizations and imposes usage restrictions, violating terms 5 and 6 the Open Source Definition[0]. Even if you accept that a model with a proprietary training dataset and proprietary training code can be considered "open source", there's no way Llama 2 qualifies.

For consistency with existing definitions[1], Llama 2 should be labeled a "weights available" model.

[0] https://en.wikipedia.org/wiki/The_Open_Source_Definition

[1] https://en.wikipedia.org/wiki/Source-available_software

Their definition of "open" is "not open", i.e. you're only allowed to use Gemma in "non-harmful" way.

We all know that Google thinks that saying that 1800s English kings were white is "harmful".

Not sure why you're getting downvoted. I would have thought HN of all places would recognize the power and value of OSI licensing and the danger of the proliferation of these source available but definitely not Open Source licenses.

> We all know that Google thinks that saying that 1800s English kings were white is "harmful".

If you know how to make "1800s english kings" show up as white 100% of the time without also making "kings" show up as white 100% of the time, maybe you should apply to Google? Clearly you must have advanced knowledge on how to perfectly remove bias from training distributions if you casually throw stones like this.

Tell me you take this seriously: https://twitter.com/napoleon21st/status/1760116228746805272

It has no problem with other cultures and ethnicities, yet somehow white or Japanese just throws everything off?

I suppose 'bias' is the new word for "basic historic accuracy". I can get curious about other peoples without forcibly promoting them at the expense of my own Western and British people and culture. This 'anti bias' keyword injection is a laughably bad, in your face solution to a non-issue.

I lament the day 'anti-bias' AI this terrible is used to make real world decisions. At least we now know we can't trust such a model because it has already been so evidently crippled by its makers.

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