Muse-Glimmer-30B

Muse-Glimmer-30B is a ~30B-parameter multimodal (vision-language) model distributed in the sovereign .knot format and served by the Gnosis fat-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)

Vision encoder (muse-glimmer-30b-mmproj.knot)

Qwen2.5-VL-class CLIP ViT + projector:

Field	Value
Layers	50
Hidden size	1536
Heads	16 (head_dim 96)
Input	896×896, patch 14 → 4096 patches
Position	32×32 learned embeddings, bicubic-interpolated
Attention	2D-RoPE (θ 10000), window/full pattern [w, w, w, f]
Spatial merge	2×2
Projector	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.

Download

from huggingface_hub import hf_hub_download

text   = hf_hub_download("forkjoin-ai/muse-glimmer-30b", "muse-glimmer-30b.knot")
vision = hf_hub_download("forkjoin-ai/muse-glimmer-30b", "muse-glimmer-30b-mmproj.knot")

The knots are also mirrored on the Forkjoin object-storage plane for edge serving.

Citation

@misc{muse_glimmer_30b,
  title  = {Muse-Glimmer-30B: a sovereign CPU-first vision-language model in the knot format},
  author = {Forkjoin.ai},
  year   = {2026},
  url    = {https://huggingface.co/forkjoin-ai/muse-glimmer-30b}
}