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Lie24/qwen38-mtp-head-q2q4-cons
qwen38-mtp-head-q2q4-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.
Rebuild of amal-david/qwen38-mtp-head-q2-q4-rerank-v1@ae62827 for the mlxfast Qwen-MTP challenge. The affine-2 shortlist tensors, precision-island tensors, and all norms are byte-identical to that artifact. The eight…
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Updated Aug 18, 2026
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How the weights are stored.
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From the Hugging Face model README
Rebuild of amal-david/qwen38-mtp-head-q2-q4-rerank-v1@ae62827 for the mlxfast
Qwen-MTP challenge. The affine-2 shortlist tensors, precision-island tensors,
and all norms are byte-identical to that artifact. The eight 4-bit/g64 decoder
triples (fc, q/k/v/o_proj, gate/up/down_proj) — verified byte-identical in the
original to a naive mx.quantize(w, 64, 4) of the organizer-pinned bf16 head —
are replaced with a per-group best-of-two coding of the SAME bf16 source:
each group stores whichever of {classic coding, bf16-consistent coding} has
the smaller true as-stored reconstruction error. No range shrink; min/max
endpoints and outlier anchoring are pointwise identical to the original; every
group is at least as faithful by construction (56% of groups improve, mean
reconstruction error −5.1–7.4% per tensor).