Simon Willison’s Weblog

Function calling with Gemma (via) Google's Gemma 3 model (the 27B variant is particularly capable, I've been trying it out via Ollama) supports function calling exclusively through prompt engineering. The official documentation describes two recommended prompts - both of them suggest that the tool definitions are passed in as JSON schema, but the way the model should request tool executions differs.

The first prompt uses Python-style function calling syntax:

You have access to functions. If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response if you call a function

(Always love seeing CAPITALS for emphasis in prompts, makes me wonder if they proved to themselves that capitalization makes a difference in this case.)

The second variant uses JSON instead:

You have access to functions. If you decide to invoke any of the function(s), you MUST put it in the format of {"name": function name, "parameters": dictionary of argument name and its value}

You SHOULD NOT include any other text in the response if you call a function

This is a neat illustration of the fact that all of these fancy tool using LLMs are still using effectively the same pattern as was described in the ReAct paper back in November 2022. Here's my implementation of that pattern from March 2023.

# 26th March 2025, 8:23 pm / prompt-engineering, google, generative-ai, llm-tool-use, gemma, ai, llms, local-llms

gemma-2-27b-it-llamafile (via) Justine Tunney shipped llamafile packages of Google's new openly licensed (though definitely not open source) Gemma 2 27b model this morning.

I downloaded the gemma-2-27b-it.Q5_1.llamafile version (20.5GB) to my Mac, ran chmod 755 gemma-2-27b-it.Q5_1.llamafile and then ./gemma-2-27b-it.Q5_1.llamafile and now I'm trying it out through the llama.cpp default web UI in my browser. It works great.

It's a very capable model - currently sitting at position 12 on the LMSYS Arena making it the highest ranked open weights model - one position ahead of Llama-3-70b-Instruct and within striking distance of the GPT-4 class models.

# 2nd July 2024, 10:38 pm / llamafile, google, generative-ai, ai, local-llms, llms, justine-tunney, llama-cpp, gemma, lm-arena

PaliGemma model README (via) One of the more over-looked announcements from Google I/O yesterday was PaliGemma, an openly licensed VLM (Vision Language Model) in the Gemma family of models.

The model accepts an image and a text prompt. It outputs text, but that text can include special tokens representing regions on the image. This means it can return both bounding boxes and fuzzier segment outlines of detected objects, behavior that can be triggered using a prompt such as "segment puffins".

You can try it out on Hugging Face.

It's a 3B model, making it feasible to run on consumer hardware.

# 15th May 2024, 9:16 pm / google, generative-ai, google-io, ai, local-llms, llms, gemma, vision-llms

Gemma: Introducing new state-of-the-art open models. Google get in on the openly licensed LLM game: Gemma comes in two sizes, 2B and 7B, trained on 2 trillion and 6 trillion tokens respectively. The terms of use “permit responsible commercial usage”. In the benchmarks it appears to compare favorably to Mistral and Llama 2.

Something that caught my eye in the terms: “Google may update Gemma from time to time, and you must make reasonable efforts to use the latest version of Gemma.”

One of the biggest benefits of running your own model is that it can protect you from model updates that break your carefully tested prompts, so I’m not thrilled by that particular clause.

UPDATE: It turns out that clause isn’t uncommon—the phrase “You shall undertake reasonable efforts to use the latest version of the Model” is present in both the Stable Diffusion and BigScience Open RAIL-M licenses.

# 21st February 2024, 4:22 pm / google, generative-ai, ai, local-llms, llms, gemma