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I want to say so much right now but I can't :)

The most generic thing I can say is I really do like working at Google because its one of the few (maybe only) company that has models of all sizes and capabilities. Because of this research and product development is insanely fun and feels "magical" when things just click together.

Keep following the Google Developer channels/blogs whatever. Google as a whole is pushing hard in this space and I personally think is building stuff that felt like science fiction just 3 years ago.

So I didn't even know this was going to be made until recently, and when I saw it, it also blew my mind. I didn't realize how far along web ml community had pushed things, and was impressed by the creativity of the HF folks with visuals and "game flow".

Personally speaking its really neat to see other people who take these models and run with them, creating things I could haven't have imagined. I'm hoping many others in the open community do the same in the coming weeks and the new year

> Do you recommend any particular mix or focus in the dataset for finetuning this model, without losing too much generality?

Astute questions, there's sort of two ways to think about finetuning, 1. Obliterate any general functionality and train the model on your general commands 2. As you asked maintain generality trying to preserve initial model ability

For 2 typically low learning rate or LORA is a good strategy. We show an example in our the finetuning tutorial in the blog.

> 2. do you have any recommendations for how many examples per-tool? This depends on the tool complexity and the variety of user inputs. So a simple tool like turn_flashlight_on(), with no args, will get taught quickly, especially if say you're only prompting in English.

But if you have a more complex function like get_weather(lat, lon, day, region, date) and have prompts coming in in English, Chinese, Gujarati and spanish, the model needs to do a lot more "heavy lifting" to both translate a request and fill out a complex query. We know as programmers date by themselves are insanely complex in natural language (12/18/2025 vs 18/12/2025).

To get this right it'll help the model if it was trained on data that shows it the versions of variations of inputs possible.

Long answer but I hope this makes sense.

Trust me as a daily at home Gemma user myself, I'm just excited for what's upcoming as you are, maybe even more because I have some hints for what's to come.

>My "dream" scenario is to have the "agentic" stuff run locally, and call into the "big guns" as needed.

FunctionGemma 270m is your starter pack for this, train your own functions to call out to whatever larger models you choose. It's been quite effective my testing, and the finetuning guides should show you how to add in your own capabilities.

Speaking from the research side its incredible how so many small models, not just Gemma, are achieving performance levels of must larger models from just a year or two ago. It's personally why I stay in this space.

We evaluate many things that you alluded to, such as speed on device, output correctness, and also "is this something that would be useful" the last one being a bit abstract.

The way we think about it is what do we think developers and users need, and is there a way we can fill that gap in a useful way. With this model we had the hypothesis you had, there are fantastic larger models out there pushing the frontier of AI capabilities, but there's also a nice for smaller customizable model that's quick to run and quick to tune.

What is optimal then ultimately falls to you and your use cases (which I'm guessing at here), you have options now between Gemini and Gemma.

It depends on a couple of things. If you expect reasoning or frontier level chat abilities then larger Gemma models or Gemini is better.

Another hard constraint is context limit, Gemma 270m is at 32k so if the search results returned are massive then this not a great model. The larger 4b+ Gemma models have 128k, and Gemini token window is in the millions

I'm not specifically promising anything but I do want to say 2026 is going to be a great year! Many of my colleagues are shipping models too, such as t5gemma which is on the front page, and I'm personally excited to see what we're all collectively going to release in the coming year.

I've only just skimmed this blog post but if I'm reading correctly FunctionGemma can work just like what's intended here, a "contextless" tool router.

Going one level up you as a developer have a choice how much context you want to provide to the model. Philipp Schmid wrote a good blog post about this, titling this "context engineering". I like his idea because instead of just blindly throwing stuff into a model's context window and hoping to get good performance, it encourages folks to think more about how what's going into the context in each turn.

https://www.philschmid.de/context-engineering

Similarly I think the blog post you linked has a similar sentiment. There's nuanced approaches that can yield better results if an engineering mindset is applied.

> How good is the model at composing actions?

I think you mean taking the results of one function call and putting it into another? We saw some promise but didn't heavily train for this use case in the base model. The thing we noticed with the 270m sized models, and the performance expectations of AI models in 2025, is that these size models perform best for _specific users_ when finetuned to that specific use case.

What I suggest is mocking some data either by hand or using some automated tool and finetuning in this kind of use case and using the finetuning colab setup.

> is there a way to give the model ability to scope action for example if actions are related to permissions

Permissions depend on your system architecture more than the model. The model itself just takes in tokens and outputs tokens. Permissions are defined by your security/system setup in which the model itself is running.

Yes a separate model is performing ASR in this case. Gemma270m (base, function, and others) are not multimodal out of the box.

That being said if someone in the community wanted to use other encoders like siglip and plug them into Gemma270m to make it multimodal that'd be a great way to have fun over break and build up an AI Eegineer resume :)

Its definitely a step in that direction. I use Gemma models on my local macbook all the time and am personally excited to have this one available for me at home now as well

Thank you much for the kind words

Coin flipping works only if the fails are roughly independent. More important is the complexity ceiling above which they fail all the time.