For research tasks, I like Meta's muse glimmer 30b model run locally. Whatever they did in post-training to make it so terse and direct, it worked. Gets right to the point and wastes no tokens. Here's a snippet from a recent thinking trace:
> Calculation done. Potential confusion: sq km vs acres. Provide both. Output.
Other models would have written paragraphs dancing around the idea. Glimmer barely does sentences.
It doesn't really sound human - which is a plus in my book. I want my robots to talk to me like they are robots.
> Calculation done. Potential confusion: sq km vs acres. Provide both. Output.
Other models would have written paragraphs dancing around the idea. Glimmer barely does sentences.
It doesn't really sound human - which is a plus in my book. I want my robots to talk to me like they are robots.