Model Detail
Muse-Glimmer-30B
▲ 2.2%Muse-Glimmer-30B is a multimodal model with 30B parameters released by meta-models. The model is registered under the image-text-to-text pipeline tag on Hugging Face, and supports text+image->text inputs, distributed under the permissive apache-2.0 license.
Muse-Glimmer-30B is priced at $0.35/M input tokens and $1.5/M output tokens. Operationally the model offers a 131K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.
Muse-Glimmer-30B ships with 30B parameters. Total weight footprint is approximately 29.8 GB, which is the relevant figure when planning local-inference VRAM. The apache-2.0 license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.
Downloads of Muse-Glimmer-30B have moved +2.2% over the past 24 hours. That is a slight downtrend, consistent with normal cooling as newer models compete for the same workloads. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
Muse-Glimmer-30B is best fit for mixed text-and-image reasoning tasks such as document understanding, and high-volume batch jobs where per-call cost dominates the budget. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
MUSE: A Run-Centric Platform for Multimodal Unified Safety Evaluation of Large Language Models
arXiv:2603.02482v2 Announce Type: replace-cross Abstract: Safety evaluation of multimodal large language models requires tracking not only whether an attack succeeds, but also how the interaction unfolds across turns and input modalities. We present MUSE (Multimodal Unified Safety Evaluation), an op
Meta is back with Muse Glimmer: local, agentic, multimodal, and open source
Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions
arXiv:2511.15830v3 Announce Type: replace Abstract: Despite rapid progress in artificial intelligence, current systems struggle with the interconnected challenges that define real-world decision making. Practical domains such as business management require open-ended optimization, actively learning
Back to the museum: Investigation of the acceptance of Android Andrea with and without emotion simulation in a museum
arXiv:2607.16428v1 Announce Type: cross Abstract: For a second time, the android robot Andrea was set up at a public museum in Germany for six consecutive days to have conversations with visitors, fully autonomously. Building on previously gathered qualitative results, the robot was now capable of e
Muse: Representation Geometry of Muon Beyond Normalized Momentum
arXiv:2607.14536v1 Announce Type: new Abstract: Muon-style optimizers apply a polar map to matrix momentum, but their updates also depend on the representation of each parameter block before orthogonalization. We study this representation choice as a form of optimizer geometry and introduce {\method
AMUSE: Anytime Muon with Stable Gradient Evaluation
arXiv:2605.22432v2 Announce Type: replace Abstract: Modern deep learning commonly relies on AdamW with prescribed learning rate schedules, but recent works challenge both components: Schedule-Free optimization removes explicit schedules via iterate averaging, and Muon improves the update geometry by