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patrickbdevaney
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Model Details
Model Provider
patrickbdevaney
Model Tree
XiaomiMiMo/MiMo-V2.6-Flash-RL
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Earlier wording implied more than has been measured. No weights were changed; only the description is corrected.
Not by activation frequency. Expert saliency was accumulated over a calibration corpus and the prune set was solved with HOPE, which minimises the output error a prune set actually causes including the interaction terms between experts — REAP is the same objective with the off-diagonal zeroed, and that off-diagonal cannot be recovered after the pass.
| setting | value |
|---|---|
| objective | hope |
| saliency criterion | reap_1_1_1 |
| prune ratio | 0.50, uniform across layers |
| per-domain protection | top 8% of every domain held out of the prune set |
| worst domain retained | 0.9935 (audio) |
| mean retained | 0.9964 |
| HOPE objective pᵀFp | 0.01624 |
Selection is scored per domain and ranked by the worst one, never the mean: an average is how a criterion that destroys one capability outscores one that preserves all of them.
| domain | retention |
|---|---|
| audio | 0.9935 |
| image | 0.9937 |
| video | 0.9938 |
| science | 0.9973 |
| math | 0.9974 |
| finance | 0.9974 |
| ballast | 0.9976 |
| code | 0.9983 |
| agentic | 0.9983 |
Pruning an expert leaves its router column behind. The routers were refitted by output matching against the unpruned teacher, routers only, every expert frozen: 47 routers refitted; all 47 kept at the teacher weights (the fit did not beat the baseline).
A refit that failed to beat the untouched baseline was discarded in favour of the baseline, so no router here is worse than simply slicing the teacher's.
dflash/ speculative-decoding draft head from the source repo is not included: it was
trained against the unpruned expert set and is not valid for this checkpoint.Produced by patrickbdevaney/xiaomi-2.6-flash-REAP on a single Jetson AGX Thor. MIT, inherited from the base model — attribution to Xiaomi MiMo.