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FunctionGemma 270M Model
21–30 of 60 posts
Re: FunctionGemma 270M Model
#22can you run this from n8n?
I just looked through their webpage and github and I'm not sure. But maybe someone should make a feature request!
Re: FunctionGemma 270M Model
#23Hi all, I'm a research lead on this model. Same as every model release post, I enjoy working at Google for a multitude of reasons, and opinions here are my own. Happy to answer whatever technical questions I can!
Some fine tuning data questions:
i see the the dataset Google published in this notebook https://github.com/google-gemini/gemma-cookbook/blob/main/Fu... -- from looking at the dataset on huggingface, it looks synthetically generated.
1. do you recommend any particular mix or focus in the dataset for finetuning this model, without losing too much generality?
2. do you have any recommendations for how many examples per-tool?
thank you for your (and your teams) work!
Re: FunctionGemma 270M Model
#24Hi all, I'm a research lead on this model. Same as every model release post, I enjoy working at Google for a multitude of reasons, and opinions here are my own. Happy to answer whatever technical questions I can!
Thanks for all the great work. How good is the model at composing actions and is there a way to say, give the model ability to scope actions, for example if actions are related to permissions or some other context? Would one need to pass the role or permission as context or finetune separately? I hope those questions make sense
> 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.
Re: FunctionGemma 270M Model
#25Hi all, I'm a research lead on this model. Same as every model release post, I enjoy working at Google for a multitude of reasons, and opinions here are my own. Happy to answer whatever technical questions I can!
Hey! Love the Gemma series. Question that came to mind reading the announcement post - the proposal there is that you can use this as a local backbone and have it treat a larger model as a 'tool call' when more reasoning is needed. In my mind we want a very smart layer frontier model orchestrating, but not slowing everything down by doing every little thing; this seems like the opposite - a very fast layer that can b…
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.
Re: FunctionGemma 270M Model
#26Do you think this would be appropriate for a command line tool that hits various apis as the function calls? Ex: "what's the weather in SF tomorrow?" Or "daily price change of apple, Tesla stock for past week"? (Let's assume I have documented the apis thoroughly somewhere that the model has access to or fine tuned it on this data)
Hi, also on the FunctionGemma team! Something like this would be a good use case for the model. Based on how complicated the API is you might need to finetune it (we released a colab that guides you through the experience + how to export/run it locally). Generally better tool descriptions help although if it is something very complicated finetuning would be better.
Re: FunctionGemma 270M Model
#27Hi all, I'm a research lead on this model. Same as every model release post, I enjoy working at Google for a multitude of reasons, and opinions here are my own. Happy to answer whatever technical questions I can!
Some fine tuning data questions: i see the the dataset Google published in this notebook https://github.com/google-gemini/gemma-cookbook/blob/main/Fu... -- from looking at the dataset on huggingface, it looks synthetically generated. 1. do you recommend any particular mix or focus in the dataset for finetuning this model, without losing too much generality? 2. do you have any recommendations for how many examples per…
> 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.
Re: FunctionGemma 270M Model
#28Hi all, I'm a research lead on this model. Same as every model release post, I enjoy working at Google for a multitude of reasons, and opinions here are my own. Happy to answer whatever technical questions I can!
Not FunctionGemma related, but would love to see an open weights model from Google for speech to text transcription (diarization, timestamps, etc.). Whisper is old and resource intensive for the accuracy it provides.
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.
Re: FunctionGemma 270M Model
#29Hi all, I'm a research lead on this model. Same as every model release post, I enjoy working at Google for a multitude of reasons, and opinions here are my own. Happy to answer whatever technical questions I can!
I have often wondered how much a specialized local LLM could benefit an agentic tool like Gemini CLI. I would think there could be a good win for speed and minimizing token use if coding agents used a local model. A local model could handle a lot of the low level system interaction type tasks and then send the prompts that require deeper reasoning to frontier models. It seems wasteful and slow to use frontier models to figure out how to grep a codebase, run tests, git diff, etc.
Might Gemini CLI offload some of its prompts to FunctionGemma?
Re: FunctionGemma 270M Model
#30Unbelievable shipping velocity from Google in December, and it sounds like they're not done for the week: https://x.com/osanseviero/status/2001723652635541566
:popcorn gif: