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I wrote up how DeskMind's local decision models work: a 0.8B and a 4B (Qwen3.5 fine-tunes, 8-bit MLX) that answer each desktop step as a multiple-choice question with a probability for every option. The 0.8B answers first, unsure steps go to the 4B, and when a task is ambiguous it asks the user instead of guessing.
The article also has three lessons from about twenty training rounds, including how smoothing the labels to 0.95 squeezed the 0.8B's confidence right onto the routing threshold.
Article: https://huggingface.co/blog/gxcsoccer/deskmind-local-decision-models
Models:
deskmind
The article also has three lessons from about twenty training rounds, including how smoothing the labels to 0.95 squeezed the 0.8B's confidence right onto the routing threshold.
Article: https://huggingface.co/blog/gxcsoccer/deskmind-local-decision-models
Models: