You need to agree to share your contact information to access this model
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
Access is manually reviewed. This is a preview-weights build with an incomplete runtime story — please read the Runtime status section before requesting.
Log in or Sign Up to review the conditions and access this model content.
dots3-note-preview — JANG (94.57 GiB, Apple Silicon / MLX)
⚠️ 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 | top_k | source |
|---|---|---|---|---|
| thinking_general (default) | 1.0 | 0.95 | 0 | vendor README |
| agentic_coding | 0.6 | 0.95 | 0 | JANG preset |
| instruct_nothinking | 0.7 | 0.95 | 0 | 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
Apache 2.0, inherited from the base model. Base model © Xiaohongshu / dots studio.
Quantisation, calibration and packaging by Jinho Jang — eric@jangq.ai.
- Downloads last month
- 2
2-bit
Model tree for JANGQ-AI/dots3-note-prev-JANG
Base model
dots-studio/dots3-note-prev