The website says the model is for "tool calling, device use, and structured extraction". Your example just doesn't seem to be very relevant. FWIW, it did a pretty good job for tool calling when I tried it, and I think it could be pretty nice to have this running on locally and integrate with Home Assistant.

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evmaki 3 hours ago | parent [-]

False positives are definitely relevant and worth measuring - natural language interfaces always have a discoverability problem, i.e., users not knowing what actions the system does and does not support. If the frontend of that system lacks the ability to reject unsupported commands, weird stuff happens.

Nonetheless, this is very cool work! If I can offer a small suggestion to the team at Cactus, it would be to evaluate your releases on some usability criteria (including false positives). Any serious integrator or adopter of these models would want to have that information available.

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jdknezek 3 hours ago | parent | next [-]

> "confidence": 0

OP and the linked page talk about the confidence score and using it as an action threshold, so it looks like an appropriate total response to me.

	▲	evmaki 2 hours ago \| parent [-]
		Right, but that's not the same thing as reporting a benchmark across a test set. It doesn't help me determine how well the model does across a decently-large sample size of commands. It doesn't tell me with what reliability the confidence will be below a given threshold when it should be, above that threshold when it should be, etc.

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HenryNdubuaku 2 hours ago | parent | prev [-]

Thanks, noted!