Google's NEW FunctionGemma is INSANE!

Your phone just became an AI assistant that works completely offline. No internet needed, no cloud connection. Google just dropped Function Gemma and it changes everything. Today, I'm going to show you exactly what this model does, why it matters, and the real numbers that prove it actually works. You're sending your personal data to the cloud every single time you use AI. You're waiting for responses when your phone could answer instantly. Not anymore. Function Gemma is Google's newest model, and it's completely different from what you've been using. This thing runs entirely on your phone. You speak to your phone and it executes real actions. Turn on the flashlight, create a calendar event, send an email, add a contact, all locally on your device. This isn't some demo feature either. Google released this as open source. You can download it right now from Hugging Face or Kaggle. And I'm [snorts] going to walk you through everything you need to know. Let me break down exactly how this works.

Google took their Gemma 3 model and created a specialized version called function Gemma. It has 270 million parameters. is that small. Really small compared to most AI models. But that's the point. This model runs entirely on your phone. No internet needed. No cloud connection. You speak to your phone and it executes real actions instantly. Hey, if we haven't met already, I'm the digital avatar of Julian Goldie, CEO of SEO agency Goldie Agency. Whilst he's helping clients get more leads and customers, I'm here to help you get the latest AI updates. Julian Goldie reads every comment, so make sure you comment below. Now, here's where it gets really interesting. Every time you use Siri or Google Assistant right now, your voice goes to a server. That server processes it and sends back a response. Your data is traveling across the internet. You're waiting for that round trip function. Gemma eliminates all of that. Everything happens on your device. Your data never leaves your phone. And the speed

difference is wild. Google published the actual numbers and they're insane. The base model starts at 58% accuracy for function calling. After you fine-tune it for your specific use case, accuracy jumps to 85%. That's the same accuracy as models 10 times bigger. Google proved this with their mobile actions evaluation. The model learned to understand requests like create a reminder for Friday at 2 p. m. After fine-tuning, it got 85% of these commands exactly right. The speed is what blows my mind. Function Gemma processes about 50 tokens per second on your phone. No network delay, no server processing time. It just works instantly. And if you're serious about learning how to use AI tools like Function Gemma to automate your workflow and save time, you need to check out the AI Profit Boardroom. It's the best place to scale your business, get more customers, and automate everything with AI. Over 1,800 members, all focused on learning AI together and sharing what actually works. Link in the description.

Google showcased two demos in their AI Edge Gallery app. You can download this from Google Play Store and test function Gemma yourself right now. First demo is Tiny Garden. It's a voice controlled game that runs completely offline. You tell it to plant sunflowers in the top row or water specific plots. Function Gemma translates those commands into game functions locally. All happening on your device. Second demo is mobile actions. This is the practical one. Function Gemma controls your phone's operating system. Tell it to show a location on the map. Turn on do not disturb. send a text all through natural language all offline. Now, let me tell you why this matters for privacy. Your calendar events, contacts, and messages never leave your device. You're not sending sensitive information to company servers. Everything stays local. Now

let's talk about how fine-tuning works because this is where function gemma becomes really powerful. Google released a data set called mobile actions on hoging face. Each entry has a user prompt and the expected function call. For example, turn on the flashlight gets matched with the exact function call format. You take this data set and fine-tune function gemmer on it. Google provides step-by-step instructions. After fine-tuning, your model becomes specialized for your specific commands. This is key. Small models need specialization. Function Gemma won't compete with GPT4 on general knowledge, but fine-tune it for a specific task and it matches much larger models on that task. That's the secret. Google tested

this on a Samsung S25 Ultra. The model handled 512 tokens of context and generated 32 tokens output running on the phone CPU. No GPU needed. Low latency for real-time interaction. Small memory footprint. This is production ready. You can deploy this today. And because it's open source, you can modify it for your exact use case. You own the deployment. You're not dependent on a third party API. Now, let's talk limitations because I want to give you the full picture. Function Gemma is designed for single turn or simple multi-turn conversations. It's focused on commands and function calls. For deep conversations, you'd route to a larger model. The model needs finetuning for best results. The base version at 58% accuracy isn't production ready. You need to create or find a data set for your use case. This requires some technical knowledge, but Google's documentation makes it accessible. The 32K context window works great for function calling, but for summarizing long documents, you'd need a different approach. Know the strengths

and limitations. This release is significant. Google has over 300 million downloads of Gemma models. The community is massive. Developers are sharing fine-tuning recipes and solving problems together. The timing matters, too. The industry has been focused on making models bigger. But there's a counter trend happening. Make models smaller and more specialized. Run them locally. Function Gemma is part of this movement. Apple is working on ondevice AI. Microsoft is pushing local models. Meta released Llama for edge deployment. Not every task needs a giant cloud model. For anyone learning AI, understanding how to fine-tune and deploy small models is becoming really valuable. Companies need people who can adapt these open-source models for specific business needs. And smaller models are easier to experiment with. You can fine-tune Function Gemma on a regular laptop. You don't need expensive cloud compute. You can iterate quickly and learn what works. The practical applications are everywhere. Smart home control without sending data to the cloud. Media players with natural language navigation. Accessibility tools for people with disabilities. All completely private. Business applications too. Internal company apps that handle sensitive data. You can build AI features without exposing data to external services. Customer service apps that work offline. Field service tools that don't require connectivity. As AI evolves, we're seeing more of this pattern. General purpose models in the cloud for complex reasoning, specialized models on device for specific tasks. The combination gives you flexibility, privacy, and efficiency. For anyone building with AI, pay attention to these small language models. Their purpose-built tools for specific jobs match the right tool to the right task. The open- source aspect matters. You're not locked in. You can modify the model. You can audit how it works. You can deploy it anywhere. This level of control is rare in AI tools. Function Gemma represents a new approach to AI assistance. Fast, private, powerful enough for real applications. Whether you're building apps, automating workflows, or learning how AI works, this model is worth exploring. And if you're serious about learning how to use AI tools like Function Gemma to automate your workflow and save time, you need to check out the AI profit boardroom. It's the best place to scale your business, get more customers, and automate everything with AI. over 1,800 members, all focused on learning AI together and sharing what actually works. Link in the description. And if you want the full process, SOPs, and 100 plus AI use cases like this one, join the AI Success Lab links in the comments and description. You'll get all the video notes from there, plus access to our community of 38,000 members who are crushing it with AI. This is where you learn how to actually implement this stuff, not just watch videos about it, but get hands-on and make it work for your business. All right, thanks for watching.