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ollama run odytrice/muse
Muse Glimmer profile for Ollama, published as odytrice/muse.
The card name is shortened to muse; the upstream model is Meta’s Muse
Glimmer 30B.
This card does not use the <gpu>-<size> tag scheme the other cards use. A
per-GPU split would be meaningless here — context is capped by the source GGUF
rather than by VRAM, so a 24 GB and a 32 GB card get the identical profile.
Meta’s Apache 2.0 agentic model — distilled from Muse Spark and built for local tool use, multi-step reasoning, and failure recovery rather than for general chat.
| Tag | Quantization | num_ctx |
|---|---|---|
odytrice/muse:30b |
Ollama 4-bit (~18 GB) | 131072 |
odytrice/muse:latest |
Ollama 4-bit (~18 GB) | 131072 |
Both tags are the same build from 30b/Modelfile; latest is the convenience
alias so ollama run odytrice/muse works.
Ollama publishes only 30b (~18 GB) and 30b-mlx (~21 GB, Apple silicon);
there is no q8_0 or bf16 tag to build a higher-precision variant from the
way gemma4:5090-12b has one.
| Size | Upstream | Architecture | Modalities | Native context |
|---|---|---|---|---|
| 30B | meta-models/Muse-Glimmer-30B |
Dense causal transformer, 52 layers, hidden 6656, GQA 32 query / 2 KV heads, head dim 128 | Text + Image in, text out (~1.8B ViT-G/14 encoder) | 131072 (documented up to 262144) |
~29.6B parameters, Apache 2.0.
Always set these before running Ollama:
set OLLAMA_KV_CACHE_TYPE=q8_0
set OLLAMA_FLASH_ATTENTION=1
export OLLAMA_KV_CACHE_TYPE=q8_0
export OLLAMA_FLASH_ATTENTION=1
The 32:2 GQA ratio makes the KV cache unusually cheap: 2 KV heads x 128 head dim
x 2 (K and V) x 52 layers is ~26 KB per token at q8_0 cache, so 131072 tokens
costs roughly 3.5 GB and 262144 costs roughly 7 GB. Against ~18 GB of weights
that leaves the 4090 comfortable at full native context — unlike the 27B dense
profiles, this model is not context-constrained on 24 GB.
131072 is a hard ceiling rather than a tuning choice. The packaged GGUF reports
muse-glimmer.context_length: 131072, and Ollama silently clamps any larger
num_ctx to it — a profile built with 262144 still loaded at 131072 under
ollama ps. Meta documents the architecture as supporting up to 262144, but
that requires RoPE scaling this GGUF does not carry (rope.freq_base 500000).
This is why there is one profile instead of a per-GPU pair: the extra VRAM on a
32 GB card buys nothing here. The genuine upgrade for a larger card would be a
higher-precision build — Unsloth’s UD-Q6_K_XL at 20-22 GB — which Ollama’s
library does not publish; see Notes.
Verified on a 5090: 16 GB resident, 100% GPU, no CPU offload at 131072.
If ollama ps does show CPU offload, drop to 98304.
Meta’s published defaults:
temperature 1.0
top_p 0.95
top_k 64
Note top_k 64 — higher than the top_k 20 used by the Qwen 3.6 / 3.8
profiles. Carrying a Qwen client config across will be wrong.
The model exposes adjustable reasoning effort (low, medium, high,
xhigh) to trade compute against answer depth. These profiles do not pin a
level; set it per request.
The profiles build on the default muse-glimmer:30b Ollama tag rather than a
direct Hugging Face GGUF import, following the same compatibility precedent as
the Qwen 3.6 / 3.8 profiles.
Unsloth publishes a UD-Q6_K_XL build at 20-22 GB that would fit a 5090 with
room for KV cache and would be a genuine quality upgrade over 4-bit. It is a
direct HF GGUF import, which this repo has avoided after malformed template and
projector artifacts on earlier Ollama releases — worth testing, but not what
these profiles ship.