If you read my last post about Scott Jenson, you recall we discussed Local AI and the desperate need to stop relying on the cloud for every single interaction. Well, the universe has comedic timing. Google just dropped Function Gemma.
It’s not GPT-5.2. It won’t write Shakespearean sonnets. It has 270 Million parameters. By today’s standards, that’s a bacterium. But it is a surgical bacterium.
The Anti-Hype
The industry is obsessed with “Bigger is Better.” More VRAM, higher costs, absurd latency. Function Gemma goes the other way. It doesn’t want to chat with you. It wants to translate intention into JSON. Period.
Why this is genius (and I don’t use that word lightly):
- Runs on the Edge: At 270M, this runs on your phone, a Raspberry Pi, maybe even those smart fridges nobody uses.
- Real Privacy: Your data doesn’t leave the device just to turn on a lightbulb.
- Zero Latency: No round-trip to a server farm in Virginia.
The End of Mystical “Prompt Engineering”
We waste hours trying to convince 70B models to format a JSON correctly. Function Gemma was fine-tuned specifically for this. It is a routing tool, not an oracle.
The proposition is simple:
User speaks -> Function Gemma understands -> Spits out JSON -> Your code executes.
No hallucinations about the soul of machines. Just deterministic (mostly) input/output.
Where This Changes the Game
As someone who works with these systems, I see this as the missing link for Real Agents.
Imagine a banking app. You don’t want to send “Transfer $50” to OpenAI. You want a tiny local model that understands the sentence and calls transfer_money(amount=50) locally, within a controlled security environment.
The Catch (There’s always one)
Don’t try to use this “out of the box” for philosophy. It is dumb regarding generalities. And, crucially, it shines with Fine-Tuning. The video shows the base model has ~58% accuracy on specific tasks, but jumps to 85%+ with fine-tuning on your function definitions.
Meaning: You still have to do the work. There is no free lunch. You need to define your tools, generate synthetic data, and train the model for your domain. But the result is a model that runs on the client’s CPU and costs $0.00 per token.
Conclusion
We are finally moving from the “Chat Toys” phase to the “Software Components” phase. Is Function Gemma a glorified, statistical if/else statement? Yes. And that is exactly why it is useful.
Less magic, more engineering.