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Edit: Per even quicker testing the Finnish language performance degrades rapidly with the smaller models, as is usually the case. Would be great to have language specific distillations from larger models.
Greetings from the Gemma team! We just got Gemma 3 out of the oven and are super excited to show it to you! Please drop any questions here and we'll answer ASAP. (Opinions our own and not of Google DeepMind.) PS we are hiring: https://boards.greenhouse.io/deepmind/jobs/6590957
How good is Gemma at structured output generation, JSON schema compliance and tool use? Particularly the smaller versions, particularly in foreign languages? We will run our internal evals on it for sure, but just wanted to ask whether that's even a use case that the team considered and trained for.
Greetings from the Gemma team! We just got Gemma 3 out of the oven and are super excited to show it to you! Please drop any questions here and we'll answer ASAP. (Opinions our own and not of Google DeepMind.) PS we are hiring: https://boards.greenhouse.io/deepmind/jobs/6590957
Thank you! Question: your model supports 140 languages. Given that you are focusing on compactness and efficiency, would you not have gains in also developing models on a selected limited number of languages (e.g. the topmost (in cultural production) four "western" ones with shared alphabet - or similar set)? Edit: of course the multilingual capability can be can be welcome. On the other hand, there are evident cases…
They say all the models were distilled from a teacher model but they didn't specify what that teacher model is. Interesting thing to hide.
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The argument is that it simply improves the product. For instance, Github Copilot is apparently refusing to do anything with variable names like "trans" and anything related to sex or gender, regardless of the intended meaning. That is a serious flaw and makes the product less useful. See this: https://github.com/orgs/community/discussions/72603
You don’t know if the censorship is in the model or the system prompt.
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Thank you! Question: your model supports 140 languages. Given that you are focusing on compactness and efficiency, would you not have gains in also developing models on a selected limited number of languages (e.g. the topmost (in cultural production) four "western" ones with shared alphabet - or similar set)? Edit: of course the multilingual capability can be can be welcome. On the other hand, there are evident cases…
That's an idea we've thought about. However, we think the open source community has already created a very impressive set of language or region-specific finetunes [1] [2]. Also there is a lot of cultural and nuance context in every language that we don't have the capacity to cover sufficiently. So for v3 we focused on creating the best foundational multilingual model. [1] https://huggingface.co/aiplanet/buddhi-indic…
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Picking model sizes is not an exact science. We look for sizes that will fit quantized on different categories on devices (e.g., low-end and high-end smartphone, laptops and 16GB GPUs, and bigger GPUs/TPUs). We also want the ratio of model width to depth (number of layers) to be consistently around 90, which we found works best. The models are trained with distillation from a bigger teacher. We train them independent…
Thanks again, very interesting. One unexpected (to me) use-case appeared not long ago when I found myself without internet but wanting to fix some non-standard Linux configuration issue. As a Windows guy I tend to web search such things, but local LLM to the rescue! Even smaller models like Gemma 2 9B has enough compressed knowledge that it managed to help me quickly solve my issue. This got me thinking how such smal…
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Have you considered that selection of material contributes to specialization and efficiency? This is meant to be a weights-small model.
its also apparently a well known result that filtering nsfw content IMPROVES scores https://x.com/swyx/status/1661359483447316480
Or perhaps the measurement of improvement was biased. If a model doesn't understand the word gay there would certainly be people who would find real world use of the model to be substandard.
Did the assessment of what counts as improvement come from the same community that decided that excluding things with 'gay' was cleaning the data?
> They are designed to help prevent our models from generating harmful content, i.e., > [...] > Sexually explicit content Dear tech companies. Sexually explicit content is not harmful. Why are you all run by puritans? I don't even want to make edgy porn, I just want to be treated like an adult.
It's harmful in that there exists a significant and vocal subset of users who does not wish to see that content or does not wish their children to do so. It's easier to teach your model never to produce that kind of content than to teach it to perfectly distinguish whether this user should see that content or not. TV channels are barred from broadcasting this kind of content for similar reasons. Sure, there are alway…
Then it's up to users (or parents, in the case of children) to choose the adequate version for each purpose. Just like there are child-friendly movies and adult-only movies, and no one beyond fringe puritan crusaders would say that the latter should outright not exist.
> They are designed to help prevent our models from generating harmful content, i.e., > [...] > Sexually explicit content Dear tech companies. Sexually explicit content is not harmful. Why are you all run by puritans? I don't even want to make edgy porn, I just want to be treated like an adult.
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So you’re arguing for teenagers to be encouraged to share explicit content of minors with each other?