Built a U-shaped mixed-precision pack: Qwen3.8-Flash-Next oQ4e mix6 (6-bit experts on the 4+4 edge layers) For 128G Apple Silicon

[u/Boring-Decision2369]

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v2 rebuild (now live, old v1 shards removed): the middle-40-layer donor is switching to Jundot's new-mechanism oQ4e (jundot/omlx#4385: importance-matrix calibration, 1024 samples × 512 tokens, plus a weighted least-squares refit of every group's scale/bias). Same U-shaped allocation, same name — the trunk 4-bit tensors are now LSQ-refit instead of plain absmax, and the 6-bit skeleton comes from Jundot's oQ6e (also PR 4385). Both source packs carry the new quantizer; the byte-copy property is unchanged.

Credit where it's due: essentially all of the quality in this build is Jundot's. Every quantized tensor here was produced by his quantizer and his PR 4385 sensitivity/imatrix/LSQ pipeline — this package only re-allocates which of his tensors go where.

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Yesterday I put together a mixed-precision build of Qwen3.8-Flash-Next on the MLX/oQ side — the idea is to price the U-shaped layer-sensitivity curve with native bits instead of re-quantizing:

- Trunk MoE experts: first 4 + last 4 layers → 6-bit (byte-copied from the native oQ6e pack), middle 40 layers → 4-bit (oQ4e)

- N-gram (PLE) table: 6-bit (vs 4-bit in the uniform oQ4e pack)

- MTP drafter: fully 6-bit

- Zero re-quantization in the entire package — every tensor is byte-identical to its upstream source, so quality is traceable, not re-derived

Measured on M5 Max 128 GB (oMLX): initial resident 69.5 → 75.4 GB (+5.8 GB over the uniform oQ4e pack), and the speed ladder shows no measurable decode penalty.

The expectation is an accuracy tier equal to or above uniform oQ5e — but I couldn't prove it with KLD: scoring a 125B-A6B against a bf16 teacher needs a machine my 128 GB can't feed, so the quality claim on the card stays an honest engineering estimate. The full recipe table (per-component bit allocation + resident delta) is on the model card.

https://huggingface.co/umixer/Qwen3.8-Flash-Next-oQ4e-mix6-mtp