avlp12/Motif-3-Beta-Alis-MLX-Dynamic-4.5bpw

[!IMPORTANT] ## ➡️ 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: [avlp12/Motif-3-Alis-MLX-4.5bpw](https://huggingface.co/avlp12/Motif-3-Alis-MLX-4.5bpw) (직접 대응 빌드 / direct counterpart)

[!NOTE] ## 📌 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.

Motif-3-Beta-Alis-MLX-Dynamic-4.5bpw

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. This is the "C6" build: 4.547 bpw measured, 167 GB on disk — the high-fidelity daily driver of the family and, at the same time, the teacher that distilled the [2.3bpw floor build](https://huggingface.co/avlp12/Motif-3-Beta-Alis-MLX-Dynamic-2.3bpw). Part of the first community MLX port of Motif-3-Beta in any format.

If your Mac has 256 GB or more, this is the build to run: 4-bit experts with 6-bit attention and an 8-bit KV-A latent recover the base model's behavior at less than half the footprint of the 8-bit reference.

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⚠️ 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.

Model at a glance

Motif-3-Beta is a text-generation LLM (no vision/audio) built on several non-standard components. The port reimplements each for MLX and verifies it against a bug-fixed reference.

Recipe (verified from config.json)

97.84% of the parameters are routed experts, so expert bit-width sets the size; the per-token critical path and the latent chokepoint get extra bits.

Architecture

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Build ladder

How to run

Motif support is not yet in an `mlx-lm` release. Install from the fork/branch that carries the motif model class:

pip install git+https://github.com/avlp12/mlx-lm.git@motif

Python:

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

model, tokenizer = load("avlp12/Motif-3-Beta-Alis-MLX-Dynamic-4.5bpw")

messages = [{"role": "user", "content": "한국어와 영어로 자기소개를 해줘."}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
# The chat template auto-opens a <think> block: the model reasons first, then answers.
sampler = make_sampler(temp=1.0, top_p=0.95)   # recommended; greedy can loop on some prompts
print(generate(model, tokenizer, prompt=prompt, max_tokens=1024, sampler=sampler))

CLI / server:

mlx_lm.generate --model avlp12/Motif-3-Beta-Alis-MLX-Dynamic-4.5bpw \
  --prompt "Explain MoE routing in two sentences." --temp 1.0 --top-p 0.95 -m 512

mlx_lm.server --model avlp12/Motif-3-Beta-Alis-MLX-Dynamic-4.5bpw   # OpenAI-compatible

• —Sampling: temperature 1.0, top-p 0.95 are recommended (greedy can loop on some prompts).
• —Stop tokens: the tokenizer stops on {0, 3, 6}; pass all three as stop ids if you drive generation yourself.
• —Reasoning mode: the chat template opens a <think> block automatically — expected behavior, not a template bug.
• —Long context: on a 256 GB Mac, quantize the KV cache for long prompts (--kv-bits 8 --kv-group-size 64 --quantized-kv-start 4096). See Hardware.

Method — alis-dwq pipeline

Weights are produced with [alis-dwq](https://github.com/avlp12/alis-dwq), the same pipeline used for the GLM-5.2 (745B) and Hy3 (295B) Alis MLX builds: mixed-bit affine quantization with the experts lowest, the router / mHC / lambda_proj / norms kept fp, the wkv_a latent chokepoint at 8-bit, and embed/head at 6-bit. At 4-bit-and-above the naive per-tensor recipe is already near-optimal for its size, so — unlike the 2.3bpw floor — this build ships as a direct mixed-precision quantization (no clip-search / DWQ pass on top; those levers are 2-bit-specific and measured neutral-to-negative at this bit-rate).

The port itself is parity-verified: MLX logits match a bug-fixed HF reference to KL ≈1e-7 / token (see Port notes).

Doubles as the DWQ teacher

This same checkpoint is the teacher for the 2.3bpw floor build's layerwise DWQ distillation (45% Korean calibration mix). A mid-bit teacher is the sweet spot for a 2-bit student — sharper than the 8-bit reference where it counts, without over-driving the student — and it cut the floor build's KL 58-72% while breaking its code-generation loop.

Evaluation

This build is the high-fidelity tier and was itself the DWQ teacher, so it is not separately KLD-evaluated against the 8bit (Q8) reference — its distribution is by construction within teacher-grade fidelity of it (it is the near-golden target the floor build was distilled toward). The discriminating KL measurements in this family are made against the 8bit reference for the more aggressive builds; see the 2.3bpw card for that ladder. For orientation, that build's KL vs Q8 is EN 0.277 / code 0.100 / KO 0.522 after distilling toward this teacher — this 4.5bpw build sits well below those figures.

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The chart shows what this teacher bought the floor build: it is the teacher, not the student, so it is not itself plotted.

Qualitatively: fluent, coherent bilingual generation (Korean including hanja, English) and code, with the reasoning <think> trace intact. General knowledge, code, and bilingual generation are reliable.

Honest caveat (shared across the family): very-long-tail factual recall can be weak — e.g. the model does not reliably reproduce obscure text such as the 4th verse of the Korean national anthem. This is a base-model property, not a quantization artifact (the 8bit reference shares it).

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 the repo discussion Motif-3-Beta/discussions/6): a YaRN RoPE dimension crash, an eager-attention GQA crash, and a grouped_mm path that applied expert-0 PolyNorm coefficients to all experts. Motif Technologies pushed fixes (SHA `d2c9ac6`) and this port matches the fixed reference. The root cause of the early incoherence was 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

Built for 256 GB (and up) Apple Silicon. Weights are 167 GB, leaving comfortable headroom for KV cache and prefill activation on a 256 GB machine (274.9 GB total); on a 512 GB machine, long-context prefills run with room to spare. For long context, prefer int8 KV (--kv-bits 8). If your Mac has 128 GB, use the 2.3bpw floor build instead; for a near-lossless archival reference on 512 GB, the 8bit build.

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."

This is a non-commercial research license that explicitly permits modification and redistribution for personal / educational / non-commercial research use. A quantized MLX port is a modification-and-redistribution, so it is permitted provided it stays non-commercial and carries the same terms. This derivative inherits that 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 & `mlx-lm`: Apple ml-explore.
• —MLX port, `motif` model class, mixed-precision recipe: 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-4.5bpw — 4.547 bpw MLX quantization of Motif-3-Beta for 256 GB+ Apple Silicon; the family's high-fidelity daily driver and DWQ teacher.