gbuzhf/KAT-Coder-V2.5-Dev-REAP-205E-MTP-GGUF overview
Withdrawn — KAT Coder V2.5 Dev REAP 205E MTP GGUF These artifacts were removed on 2026 09 12. The repository is kept only so the DOI continues to resolve. What…
Repository Files & Downloads
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|---|---|---|---|---|
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Model Details
Model README
---
license: mit
tags:
- retired
- gguf
---
Withdrawn — KAT-Coder-V2.5-Dev REAP-205E MTP GGUF
**These artifacts were removed on 2026-09-12. The repository is kept only so the DOI
continues to resolve.**
What was here
An expert-pruned build of Kwaipilot/KAT-Coder-V2.5-Dev published 2026-08-10:
256 → 205 experts per layer (19.92% pruned) by REAP saliency, with an MTP head grafted,
a BF16 master and nine quantized tiers.
Why it was withdrawn
A publication gate was skipped. The build procedure made draft-acceptance
measurement a hard stop — publish only above ~0.80 — and that measurement was never
run. No acceptance figure for this pruned model exists. The model card cited the
unpruned model's 0.90–0.94, which reads as validation of something that was never
tested. A separate open defect (wrong-language output) was also never closed.
Re-verification in 2026-08-15 confirmed the methodology was sound: the keep-index is
bit-exact REAP top-205/layer across all 40 layers, every rate-table number reproduces,
the GGUF structure passes all checks, and recipe parity against the unpruned release is
exact — 753 tensors, zero name or quant-type differences. **The pruning was done
correctly; the resulting model was never shown to work.** Those are different claims,
and only the first one was ever supported.
Withdrawing is the honest resolution. The alternative — leaving weights online whose
headline capability claim was borrowed from a different model — is worse than removing
them.
Where to go instead
| you want | go to |
|---|---|
| the unpruned model, same recipes, measured | gbuzhf/KAT-Coder-V2.5-Dev-MTP-GGUF |
| the upstream model | Kwaipilot/KAT-Coder-V2.5-Dev |
If you want to reproduce the pruning
The method is reproducible without these files. The saliency measurement and keep-index
derivation are documented, and the pruning itself is deterministic given the saliency
tensor. Anyone repeating it should run the acceptance gate that this release did not.
Citation
The DOI below still resolves to this page. It refers to the 2026-08-10 release, whose
files are no longer distributed and whose capability claims should not be cited.
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