31–40 of 60 posts
Hi 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!
Hi 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…
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.
Hi 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!
Much-appreciate the focus on local-first (on-device) ! I'm wondering how your approach differs from (or integrates with) something like "Differentiable Programming for LLM Tool Selection" https://viksit.substack.com/p/optimizing-tool-selection-for-...
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.
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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…
I would be wary of having a LLM with 85% accuracy call tools on my system. Isn’t that fairly far away from production-grade performance?
I also don’t see that the fact that accuracy can be boosted from 50% to 85% is any indication that it can be boosted further.
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Wen gemma4? :) But on a serious note, I'm happy to see more research going into vSLMs (very small...) My "dream" scenario is to have the "agentic" stuff run locally, and call into the "big guns" as needed. Being able to finetune these small models on consumer cards is awesome, and can open up a lot of niche stuff for local / private use.
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 tes…
FTA: In our "Mobile Actions" evaluation, fine-tuning transformed the model’s reliability, boosting accuracy from a 58% baseline to 85%. This confirms that for edge agents, a dedicated, trained specialist is an efficient path to production-grade performance. I would be wary of having a LLM with 85% accuracy call tools on my system. Isn’t that fairly far away from production-grade performance? I also don’t see that the…