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

>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.

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 :)

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.

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.

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.

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.

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.

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

Thinking helps performance scores but we'll leave it up to users to add additional tokens if they want. Our goal here was the leanest weight and token base for blazing fast performance for you all.