Muse-Glimmer-30B is a ~30B-parameter multimodal (vision-language) model distributed in
the sovereign .knot format and served by the Gnosisfat-station inference runtime. It pairs a Qwen2.5-VL-class perception encoder with a
Qwen3-class dense text decoder, and is designed to run CPU-only, scale-to-zero on
commodity edge compute (e.g. Cloudflare Containers) with no GPU and no fixed cost.
Released under the Apache-2.0 license.
Files
File
Role
Notes
muse-glimmer-30b.knot
Text decoder
52-layer dense transformer
muse-glimmer-30b-mmproj.knot
Vision encoder + projector
CLIP ViT + 3-layer GELU projector → text hidden space
The .knot format is the Gnosis distributed-inference container format: a
streamable, sharded, tensor-cache-friendly package (quantized weights are demand-streamed
from object storage and held resident per-shard). It is not GGUF or safetensors.
Architecture
Text decoder (muse-glimmer-30b.knot)
Qwen3-class dense transformer:
Field
Value
Layers
52
Hidden size
6656
Attention
GQA — 32 query / 2 KV heads, head_dim 128
Normalization
RMSNorm + QK-norm (per-head query/key norm)
Attention gate
output gate — attn_out *= sigmoid(x @ attn_gate) before o_proj
FFN
SwiGLU
Vocabulary
202048
RoPE θ
500000
Image token id
200092 (placeholder positions spliced with vision embeddings)
3-layer GELU MLP: 6144 → 4096 → 4096 → 6656 (into the text hidden space)
Quantization
Q4_K / Q6_K — attention & FFN weights
F32 — token embeddings, norms, biases, and the full vision projector
Intended use
Multimodal understanding — describe / reason about images, and vision-grounded text
generation — on sovereign, GPU-free infrastructure. The perception encoder emits dense,
image-sensitive [n_tokens × 6656] embeddings that splice directly into the text decoder's
hidden space at image_token_id positions.
Serving
Served natively by fat-station (Rust HTTP inference). Two deployment shapes:
Single container (role=both) — one box serves /embed, /forward, /lm-head,
/tokenize, /generate, /detokenize.
Sharded mesh — the 52 decoder layers split across N containers (entry / mid / exit),
coordinated over a lightweight /embed → /forward → /sample-next protocol.
Vision:
POST /glimmer-vision-embed
{ "pixels": [<CHW f32, normalized>], "grid_h": H, "grid_w": W }
→ octet-stream [n_tokens × 6656] f32 embeddings
Text (OpenAI-compatible, via the aeon-openai-adapter front):
POST /v1/chat/completions
{ "model": "muse-glimmer-30b", "messages": [...], "stream": true|false }
Operational notes
KNOT_HTTP_PIN_ALL=0 for the vision encoder and for text shards — pinning the full
vision weights resident dequantizes to ~10 GB f32 and OOMs; stream instead.
Text shards benefit from a resident quantized tensor cache
(KNOT_HTTP_TENSOR_CACHE_CAPACITY) so warm tokens skip re-streaming.
Performance (measured, CPU)
On a 4-shard mesh of scale-to-zero Cloudflare Containers (4 vCPU / 12 GiB each):
Path
Warm
Notes
Text generation
~3.0 s/token
down from ~42 s/token via resident tensor cache + on-device sampling (~14×)
Vision embed
~1.5–2.2 s
with a dequantized-weight cache (~25× over cold)
Cold start
~160 s
full weight stream on wake (accepted tradeoff for scale-to-zero)
Numbers are latency on CPU edge compute, not throughput benchmarks; there are no GPU or
accuracy benchmarks in this card.
Limitations & status
Vision encoder: admission-gated — proven to produce finite, dense, non-degenerate,
image-sensitive embeddings (a gradient vs a checkerboard yield different outputs).
Text decoder: functional after the attention-output-gate fix (distinct, non-degenerate
token sequences); a formal generation-quality admission gate is still pending. Treat text
generation as a research artifact, not a production-graded chat model.
CPU-first design: high-quality but latency-bound (seconds/token), with multi-minute cold
starts under scale-to-zero.
No safety tuning, RLHF, or content filtering is claimed. No training-data or provenance
disclosure is made in this card beyond the architecture above.
The knots are also mirrored on the Forkjoin object-storage plane for edge serving.
Citation
bibtex
1@misc{muse_glimmer_30b,
2 title = {Muse-Glimmer-30B: a sovereign CPU-first vision-language model in the knot format},
3 author = {Forkjoin.ai},
4 year = {2026},
5 url = {https://huggingface.co/forkjoin-ai/muse-glimmer-30b}
6}
