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. 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.

⚠️ 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.

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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.

Base model	Motif-Technologies/Motif-3-Beta — 314.84B total / ≈13B active
Architecture	Sparse MoE, 53 layers (0-1 dense, 2-52 MoE); 384 routed experts, top-8 + 1 shared
Routed-expert mass	97.84% of parameters
Attention	GDLA (Grouped Differential Latent Attention): MLA-style low-rank q/kv + differential-v2 + elementwise output gate; 80 heads / 16 KV groups, head_dim 192
Sliding window	interleaved SWA — window 128 on 40 layers; 13 full-attention layers carry YaRN, the 40 SWA layers plain rope
Other components	mHC (manifold-constrained hyper-connections, ×2/layer); Grouped PolyNorm per-expert activation; rms_norm eps 1e-6
Tokenizer	vocab 220,160; stop tokens {0, 3, 6}; chat template auto-opens <think>
Context	256K (262,144) native
Bits/weight	4.547 (measured; per-tensor mixed, experts 4-bit/g64)
On-disk size	167 GB (167 GiB)
Format	MLX (Apple Silicon); library_name: mlx
Role	daily driver and DWQ teacher for the 2.3bpw floor build

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.

Component	Bits	Notes
Routed experts (gate/up/down)	4-bit g64	≈97.8% of params — sets the footprint
Attention (GDLA projections)	6-bit g64	per-token critical path, promoted vs the floor build
wkv_a (KV-A latent down-projection)	8-bit g64	latent chokepoint — kept high across every profile
Token embedding · LM head	6-bit g64	distribution-sensitive
Router gate · mHC · lambda_proj · all norms · act_fn	fp (unquantized)	discrete control paths + normalization

Architecture

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Build ladder

Build	Recipe	Size	bpw	Target
2.3bpw (FLOOR)	experts 2b/g128 · attn 4b · wkv_a 8b	85 GB	2.315	128 GB Mac
4.5bpw (this, C6)	experts 4b · attn 6b · wkv_a 8b · embed/head 6b · router/mHC/norms fp	167 GB	4.547	256 / 512 GB Mac · DWQ teacher
8bit (Q8)	all 8b/g64	312 GB	8.503	512 GB Mac · reference

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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.

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Method — alis-dwq pipeline

Weights are produced with 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.

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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.

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).

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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.

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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.

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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.