How to Implement Tool Calling with Gemma 4 and Python MachineLearningMastery

Izzo You're listening to Exploring Next, episode 287. Today, we're talking about tool calling with Gemma 4 and Python.

Boone Tool calling is a huge deal, Izzo. It's like a bridge between language models and the real world.

Izzo Exactly. And with the Gemma 4 model family, we have native support for agentic workflows and tool calling.

Boone That's right. The Gemma 4 models are designed to provide frontier-level capabilities under a permissive Apache 2.0 license.

Izzo So, what does this mean for users? Who's going to be using this technology?

Boone Well, Izzo, this is a game-changer for anyone working with language models. It's especially useful for applications where data privacy is a concern.

Izzo That's a great point, Boone. And with the gemma4:e2b model, we have a paradigm shift in what's possible on consumer hardware.

Boone The gemma4:e2b model is optimized for mobile devices and IoT applications, with a 2 billion parameter footprint and near-zero latency execution.

Izzo So, how do we implement tool calling with Gemma 4 and Python? What are the key steps?

Boone We can use Ollama as our local inference runner and the gemma4:e2b model. We'll also need to define local Python functions that act as our tools and define a strict JSON schema.

Izzo And what about the code? Where can our listeners find it?

Boone The complete code for this tutorial can be found at this GitHub repository.

Izzo Okay, so what's next? What should our listeners go research or try building?

Boone I'd recommend checking out the Gemma 4 model family and Ollama. You can also try building a local tool-calling agent using the gemma4:e2b model.

Izzo And don't forget to add it to your weekend project list, Boone.

Boone Ha! Yeah, I'll add it to the list. But seriously, this is a great project for anyone interested in language models and tool calling.

Izzo Alright, that's it for today's episode. Thanks for tuning in to Exploring Next.