⚠️ RUNTIME NOT READY YET — ships with vMLX 0.24

This bundle does not run on any released runtime today. dots3-note is a new architecture (dual-geometry MLA + DSA indexer + 256-expert MoE + MoE-ViT

audio tower) and support lands in vMLX 0.24. Download and stage it now if you like, but wait for 0.24 before expecting it to load. See Runtime status for exactly what works and what does not.

A measured mixed-precision JANG build of dots-studio/dots3-note-prev — 280 B total / 16 B active omni-modal MoE — compressed to 94.57 GiB so it fits a 128 GB Apple Silicon machine with room for context and KV cache.

Text, image, video and audio in; text out. Reasoning on by default, native tool calling, 512 K context ceiling (see runtime caveats).

Quality — measured, not claimed

Every number below was produced by this exact artifact against the FP8 source checkpoint on a held-out evaluation slice that was excluded from all calibration.

metric	this bundle	reference point
mean KL vs source	0.346 nats	3.096 = structurally broken
p90 KL	0.803	—
top-1 agreement vs source	79.2 %	0.015 % = structurally broken
live acceptance rows	4 / 4 PASS	—
modality probes	3 / 3 PASS	—

The "structurally broken" column is a deliberately degraded control (a 4-layer truncation of the same model) measured through the same harness, so the headline numbers have a floor to be judged against rather than sounding good in isolation.

Live rows — exact-string echo with clean EOS; step-by-step arithmetic inside <think>; a coding row that restates both parameter names verbatim; and a well-formed native tool call. Zero degenerate repetition (every-period scan, periods 1–16).

Modality probes — the model identifies shapes and colours in an image, reports the correct direction of motion in an 8-frame clip ("located on the left side… has shifted slightly to the right"), and describes a synthesised tone correctly. These run the full deployed path: pixels/audio → encoder → placeholder substitution → quantised LM → text.

Size and composition


bundle	94.57 GiB (101.54 GB, 50 shards)
source (BF16)	576.9 GB → 6.1× smaller
source (FP8, the build input)	278 GiB → 2.9× smaller
total / active params	280 B / 16 B

Bits are allocated by measurement, not by name-matching rules: every routed expert unit was individually evaluated and the allocation solved under a byte budget. Attention, the shared experts, embeddings and the untied head are kept at 8-bit; routed experts run at a 2-bit class with measured per-unit lifts; the vision and audio towers carry their own tiers. Weights are stock MLX quantised-matmul storage — no custom kernels, no runtime sidecar, instant mmap load.

Calibration used activation-aware fitting throughout (AWQ-style scaling, per-expert importance weighting, and error-compensated code optimisation). Method details are intentionally not published.

Runtime status

Requires vMLX 0.24. This architecture is new and needs a purpose-built runtime. Being explicit about what exists today, because a model card that hides gaps wastes your time:

capability	status
text generation, reasoning, tool calls	✅ verified
image / video / audio understanding	✅ verified end-to-end
MTP weights preserved (1 layer, 41 tensors)	✅ shipped, `runtime_available: false`
long context (> 2 K tokens)	❌ needs MLA latent caching + DSA indexer
speculative decode via MTP	❌ no decode loop yet
published tokens/sec	❌ not quoted — see below
Swift / Osaurus runtime	❌ not yet
`mlx_lm` tool-call parsing	❌ no parser exists for this dialect

On speed: we will not publish a tok/s figure measured on a verification harness. The bundle reads ~11 GB of weights per token, which is bandwidth-plausible for roughly 35 tok/s on an M5 Max, but the harness used for correctness lacks MLA absorption and fused decode. A real number will be published once vMLX 0.24 lands. Treat any tok/s claim before then as unfounded.

On long context: the architecture is unusually cache-friendly — only 13 of 46 layers are full-attention and the rest are window-limited, so the entire 512 K KV cache is ~7.9 GB in latent form. That is a runtime feature, not a weights feature, and it is not implemented yet.

Sampling

Vendor-documented defaults, plus a coding preset. generation_config.json and jang_config.chat.sampling_defaults are stamped to agree.

mode	temperature	top_p	source
thinking_general (default)	1.0	0.95	vendor README
agentic_coding	0.6	0.95	JANG preset
instruct_nothinking	0.7	0.95	vLLM recipe example

top_k = 0 (disabled) is deliberate — the vendor specifies only temperature and top_p. The coding preset is ours, not the vendor's: 0.6 measurably helps exact-identifier fidelity, which is what matters for agentic work. Greedy decoding with thinking enabled is not recommended for this family.

Reasoning, tools, modalities

Reasoning is ON by default — the chat template enables thinking when the kwarg is absent. Disabling it is a prefill, not an omission: it appends a marker to the user turn and prefills a closed think block. Doing only one gives inconsistent behaviour. There are no reasoning-effort tiers.

Tools use the native dots XML dialect (<dots_function_call> / <invoke> / <parameter>), served upstream by the dots parser — not JSON, not Hermes. Note that mlx_lm has no parser for it, so tool calls surface as plain text until one exists.

Modalities are weight-gated in jang_config.capabilities (derived from the tensors actually present, not from config claims): vision, video and audio all true. One implementation note that will save you a debugging session: video is expanded onto the image token path by the processor, so derive token ids and media tensors from a single processor call and trust the encoding rather than the config's video token id.

EOS is two ids — [151643, 151668]. Stopping on only the first runs past the end of every assistant turn.

Provenance

Built from dots-studio/dots3-note-prev-fp8 (the vendor's own serving checkpoint). FP8 → BF16 dequantisation was verified bit-identical to the reference implementation, and the MLX forward pass was verified per-layer against the upstream reference at both short and long sequence lengths before any quantisation was performed.

Honest limitation: because the build input is the FP8 checkpoint, the 8-bit tier inherits FP8 fidelity (~0.027 relative error vs BF16) rather than true 8-bit fidelity (~0.006). This is immaterial for the 2-bit-class expert tiers that dominate the bundle, but it does mean attention quality is source-limited. The vision and audio towers are BF16 in the source and are unaffected.

License and credits

Quantisation, calibration and packaging by Jinho Jang — eric@jangq.ai.

JANGQ-AI/dots3-note-prev-JANG

dots3-note-preview — JANG (94.57 GiB, Apple Silicon / MLX)

⚠️ RUNTIME NOT READY YET — ships with vMLX 0.24