➡️ 이 리포는 Motif-3 Beta 기반입니다 — 정식판 리포를 사용하세요 / This repo quantizes the Beta checkpoint — use the official Motif-3 builds
정식 Motif-3 체크포인트 기반 양자화를 게시했습니다. 새로 받으신다면 정식판 리포를 사용해 주세요:
I have published quantizations built from the official Motif-3 checkpoint — please use those instead:
📌 KL 수치 보류 중 / KL numbers under review — the model itself is fine
2026-07-26. 이 카드의 "KL vs Q8(8bit 레퍼런스)" 수치는 현재 보류합니다.
레퍼런스로 쓰인 8bit 빌드에 결함이
확인되어(mx.split 2³¹ 초과 텐서 침묵 손상), 손상된 레퍼런스를 기준으로 측정된 값이기 때문입니다.
The "KL vs Q8" figures on this card are withheld pending re-measurement: the 8-bit
build used as the reference has been found defective, so those numbers were measured
against a corrupted baseline.
보류되는 것 / withheld: 슬라이스별 KL vs Q8 표 · "DWQ가 KL을 58–72% 감소" 주장 ·
eval_ladder.png의 KL 축
영향 없는 것 / unaffected — 이 빌드 자체는 정상입니다:
모델 품질(한국어·영어·코드 생성)은 직접 생성으로 검증되었고 그대로 유효합니다.
Generation quality is verified directly and stands.
포팅 패리티 KL ≈1e-7 / token 은 수정된 HF torch 레퍼런스 기준이라 8bit와 무관하며 유효합니다.
The port-parity figure is measured against the fixed HF reference, not the 8-bit build.
루프 프로브(distinct-4gram) 등 레퍼런스가 필요 없는 측정은 유효합니다.
8bit 레퍼런스를 재빌드한 뒤 재측정하여 이 카드의 수치를 갱신하고 이 주석을 내리겠습니다.
The reference is being rebuilt; these figures will be re-measured and this note removed.
Apple Silicon (MLX) mixed-precision quantization of
Motif-Technologies/Motif-3-Beta
— a 314.84B-parameter (≈13B active) Mixture-of-Experts reasoning model, text-only,
bilingual Korean / English (+ code), with 97.84% of its mass in the routed experts and
native 256K context — pushed to the lowest effective bit-rate the MLX affine
container supports (routed experts at 2-bit / group-size 128): 2.315 bpw measured,
85 GB on disk. To our knowledge this is the first community MLX port of Motif-3-Beta in
any format, the only build in the family sized to run on a 128 GB Apple Silicon Mac,
and the family's headline shippable floor.
Which Motif-3-Beta MLX build fits your Mac — 2.3bpw / 128 GB / 85 GB, 4.5bpw / 256 GB / 167 GB, 8bit / 512 GB / 312 GB
⚠️ Experimental floor build. 2-bit/g128 experts is the hard floor of the MLX affine
format — coherent and useful after DWQ, but the
4.5bpw /
8bit siblings are closer to
lossless. If your Mac has ≥256 GB, prefer a larger build.
⚠️ Requires a patched mlx-lm with the motif model class (see How to run). Stock
mlx-lm has no Motif support and will not load this checkpoint.
⚠️ Non-commercial research license, inherited in full from Motif-3-Beta (a beta
release). See License.
What clip-search + DWQ bought
KL divergence vs the Q8 reference and greedy loop-probe distinct-4gram, across raw FLOOR / +clip / +clip+DWQ — DWQ cuts KL 58-72% and breaks the code loop
The raw 2-bit floor loops on code and collapses on Korean; clip-search barely moves KL,
DWQ does the work — distilling toward the 4.5bpw (C6) teacher on a 45% Korean mix cuts KL
58-72% and eliminates the loops. It is the only build that received both low-bit levers.
Model at a glance
Motif-3-Beta is a text-generation LLM (no vision/audio), reimplemented for MLX and
verified against a bug-fixed reference.
discrete control paths + normalization — full precision is nearly free
Architecture
Motif-3-Beta architecture: embedding to 53 decoder layers (GDLA attention, mHC hyper-connections, dense/MoE feed-forward with Grouped PolyNorm) to RMSNorm to LM head
Sampling: temp 1.0 / top-p 0.95 recommended — greedy can loop at this bit-rate
(see the loop-probe in Evaluation).
Stop tokens: tokenizer stops on {0, 3, 6}; pass all three as stop ids if you drive
generation yourself.
Reasoning mode: the chat template auto-opens a <think> block — expected, not a bug.
Wired memory: on a 128 GB Mac the weights fit the default limit tightly; for real
context raise it and use 4-bit KV — see Hardware.
Method — alis-dwq pipeline
Weights come from alis-dwq, the pipeline behind
the GLM-5.2 (745B) and Hy3 (295B) Alis MLX builds: mixed-bit affine quantization (experts
lowest; router / mHC / lambda_proj / norms fp; wkv_a 8-bit; embed/head 6-bit), then two
held-out-gated passes:
Anchor-guarded clip-search — per 128-weight group, clipped grids are accepted only
when they lower MSE without raising max abs error beyond ≈1.1× the min-max grid's,
preserving outlier "anchor" weights.
Layerwise DWQ distillation against the 4.5bpw (C6) teacher on a 45% Korean
mix (EN 30 / code 25 / KO 45), with a per-round held-out rollback gate.
Port parity: MLX logits match a bug-fixed HF reference to KL ≈1e-7 / token.
Evaluation
Measured on a fixed EN / code / KO slice vs the 8bit (Q8)
build (near-lossless vs bf16,
which does not fit in memory).
KL(Q8 ‖ build) and greedy loop-probe (distinct-4gram, 256 tok/slice):
slice
KL vs Q8
loop distinct-4gram
EN
0.277
0.46
code
0.100
0.96
KO
0.522
0.88
unweighted mean
≈0.30
no cycles on any slice
What DWQ bought (see chart above): across slices DWQ cut KL 58-72% (KO
1.235 → 0.522) and turned the code slice from a hard cycle (distinct-4gram 0.011)
into 0.96. Telemetry: valid loss 0.379 → 0.200, rounds 1-6 accepted, round 7 reverted by
the rollback gate (natural stop). Clip-search alone was ≈flat on KL; DWQ carries the floor.
4-bit KV-cache probe (self-KL, fp16-KV vs 4-bit-KV): 0.006 overall (EN 0.005 / code
0.003 / KO 0.011), flip 3% → 4-bit KV is viable, which matters on the 128 GB target
where KV is the binding constraint.
Honest caveats:
KO is the softest slice (KL 0.522 vs EN 0.277) — the usual low-bit pattern,
substantially recovered by DWQ (raw was 1.235) but not erased.
Greedy can loop; use temp 1.0 / top-p 0.95. EN distinct-4gram under greedy is 0.46
(no cycle) — precisely why sampling is recommended over greedy.
Very-long-tail factual recall is weak — e.g. it does not reliably reproduce obscure
text like the 4th verse of the Korean national anthem. This is a base-model property,
not a quantization artifact (the 8bit reference shares it), most visible here. General
knowledge, code, and bilingual generation are reliable.
Correctness & port notes
Logit parity. On a truncated model (fp32, EN/KO/code prompts) MLX matches a bug-fixed
HF reference to KL ≈1e-7 / token, top-1 100%. SWA long-context self-consistency is a
bit-exact match (cached wide forward vs no-cache reference).
The shipped HF reference had bugs (documented in
discussions/6): a
YaRN RoPE dimension crash, an eager-attention GQA crash, and a grouped_mm path applying
expert-0 PolyNorm coefficients to all experts. Motif Technologies pushed fixes (SHA
d2c9ac6) and this port matches the fixed reference; the early incoherence root-caused
to the Grouped PolyNorm activation (sigmoid coefficients + output_scale 0.5 + routed
bias_clamp) — credit to Motif for the fix.
One MLX-core bug is worked around in the port: mx.split silently corrupts the tail
of a >2³¹-element tensor (the 8 GB bf16 gate_up); basic strided slices are used instead.
Upstream ml-explore/mlx#3836.
Hardware
The only build in the ladder that runs in a 128 GB Mac's unified memory. macOS caps
GPU-wired memory at a default iogpu.wired_limit_mb of ≈96 GB on a 128 GB machine; the
85 GB of weights fit under it, but tightly (≈11 GB free before KV/prefill). For real
context, raise the limit and use 4-bit KV:
If your Mac has ≥256 GB, use the 4.5bpw
or 8bit build — this floor is
deliberately bit-starved for the 128 GB target.
License
Motif-3-Beta ships no formal license name and no LICENSE file; its model card states,
verbatim:
"Permission is granted to use, modify, and redistribute this software for personal,
educational, and non-commercial research purposes only. Commercial use is prohibited
without prior written permission from Motif Technologies."
A quantized MLX port is a modification-and-redistribution, which these terms explicitly
permit provided it stays non-commercial. This derivative inherits the license in
full. The license: other / license_name fields are a best-effort mapping — the
authoritative terms are the quoted text and the base model's card.
Credits & attribution
Base model:Motif Technologies — Motif-3-Beta
(non-commercial research). All architecture — GDLA, Grouped PolyNorm, mHC — and the
trained weights are theirs, as are the reference fixes (SHA d2c9ac6).
MLX port, motif model class, mixed-precision recipe, clip-search + DWQ: Alis
(avlp12), via alis-dwq.
Independent, unofficial derivative — not affiliated with or endorsed by Motif Technologies.
Provided as-is, without warranty. Research / non-commercial use only.
Citation
Alis (avlp12) (2026). Motif-3-Beta-Alis-MLX-Dynamic-2.3bpw — experimental 2.315 bpw
MLX floor build of Motif-3-Beta
for 128 GB Apple Silicon, via anchor-guarded clip-search + layerwise DWQ.
