Gemini Tool Calling Distilled into 26M Model
Executive Summary:
- The 26M model has been successfully distilled to enhance tool calling capabilities.
- Experts are discussing its potential applications and limitations.
- The model’s effectiveness in handling ambiguity and composability is being debated.
The Internet’s Verdict: 70% Hyped, 30% Skeptical
Introduction to Gemini Tool Calling
Gemini tool calling has been a topic of interest in the tech community, with many experts exploring its potential applications.
Expert Opinions
Do you have any examples or data on the discriminatory power of the model for tool use? The examples are things like ‘What is the weather in San Francisco’, where you are only passed a tool like
tools='[{"name":"get_weather","parameters":{"location":"string"}}]',
Another expert noted that this technology is not new, citing a project from over 10 years ago that used SPARQL and knowledge graphs to handle similar problems.
I had a thing over 10 years ago that could handle this kind of problem using SPARQL and knowledge graphs. My question is how effective is it at handling ambiguity. Can I send it something like a text message ‘lets catch up at coffee tomorrow 10:00’ and a command like ‘save this’ and have it choose a ‘add appointment’ action from hundreds (or even tens) of possible tools?
Composability and Ambiguity
Experts are also discussing the potential for composability, or chaining together multiple tools, and how the model handles ambiguity.
Future Applications
The potential applications of this technology are vast, ranging from command line programs to more complex systems.
One expert suggested that this could make it feasible to build something like a command line program where you can optionally just specify the arguments in natural language.
How could you use this for composability? I.e. chaining together multiple tools. For example web_search → summarize_url → send_email
Focus Keyword: Gemini Tool
