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Lie24/qwen38-mtp-head-4bit-cons
qwen38-mtp-head-4bit-cons is a machine learning model from Lie24. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Requantisation of the organizer-pinned EigenLabs/Qwen3.8-27B-MTP-bf16 (@26a328e0) for the mlxfast Qwen-MTP challenge. Identical geometry and size to the naive mx.quantize(w, 64, 4) heads on the board (31 tensors, 238,…
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Updated Aug 18, 2026
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From the Hugging Face model README
Requantisation of the organizer-pinned EigenLabs/Qwen3.8-27B-MTP-bf16
(@26a328e0) for the mlxfast Qwen-MTP challenge. Identical geometry and size to
the naive mx.quantize(w, 64, 4) heads on the board (31 tensors, 238,934,093
bytes). Every group keeps its exact min/max endpoints — outlier anchoring is
pointwise identical to the board's head. The only per-group choice is WHICH of
two code-derivation rules is stored: the classic one (codes from fp32 scale,
scale then rounded to bf16) or the self-consistent one (scale/bias rounded to
bf16 first, codes derived from the stored values). The candidate with the
smaller true as-stored reconstruction error wins, so by construction every
group is at least as faithful as mx.quantize. 56% of groups switch; mean
reconstruction error falls 5.1–7.4% per tensor with zero range shrink.