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Muse Glimmer 30B

Muse Glimmer · Version meta-models/Muse-Glimmer-30B

AvailableChecked Oct 23, 2026
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Publisher-sourced inventory. Specifications come from official documentation and model cards. “Not documented” means we have no verified value. This inventory is separate from our editorial reviews.

Coverage & sources ↗

Current catalog answers

What to know about Muse Glimmer 30B

AI-compiled from linked model documentation. This is a catalog reference, not an independent performance review. Catalog checked October 23, 2026.

Which Muse Glimmer 30B does this page describe?

Muse Glimmer 30B is a model record from Meta in the Muse Glimmer family. Source

The recorded model identifier or checkpoint is meta-models/Muse-Glimmer-30B. Use that exact identifier when checking documentation or a provider listing. Source

How can you access Muse Glimmer 30B?

The catalog documents Open weights access for Muse Glimmer 30B. Source

As of the catalog check, this version was listed as available. Source

Open weights means the model weights are distributed for use under their stated terms. It does not establish a hosted API, an open-source license, or a hardware requirement.

What are Muse Glimmer 30B's context and output limits?

The documented context window is 131,072 tokens. Source

A separate maximum-output value is not established in this record. The context or input figure should not be used as an output allowance.

Context, input and output limits describe different constraints. Do not add the figures together to infer a larger window. Check the endpoint's accounting for conversation history, system instructions, tools, media and generated tokens; a documented maximum is not a guarantee of answer quality or a measured usable capacity.

What dates are documented for Muse Glimmer 30B?

Knowledge cutoff: January 4, 2026. Source This labels the documented knowledge boundary; it is not a promise that every earlier fact is known or correct.

Release date: August 2026. Source This is a release milestone, not a knowledge or training cutoff. The source labels this “Model Release Date.”

Retrieval or web tools can supply newer information at run time. That does not change the model's recorded training or knowledge dates, and this record does not establish that a particular endpoint includes those tools.

What can Muse Glimmer 30B receive and generate?

Documented inputs: text and image. Source

Documented outputs: text. Source

Input support and output generation are separate capabilities. Reading an image does not establish image generation; a text output listing does not establish speech output. File sizes, formats and media limits depend on the documented implementation.

What license is recorded for Muse Glimmer 30B?

The recorded license label is Apache-2.0. Source

Read the linked license and associated use terms for this exact version before redistribution, commercial deployment or fine-tuning. A label alone does not establish every permission, restriction or obligation, and it does not determine the terms of a separate hosted service.

What is documented about the Muse Glimmer 30B checkpoint?

This evidence refers to meta-models/Muse-Glimmer-30B at recorded revision cec94fcfd6512f2cfe708138c32533b346ea4f69, checked October 23, 2026. Repository metadata

Safetensors metadata records 29,776,626,688 stored parameters. This is a stored-weight count, not necessarily the number activated per token, a download size or a GPU-memory requirement. Repository metadata

Recorded architecture class: MuseGlimmerForConditionalGeneration. Repository metadata

Repository task tag: image-text-to-text. A task tag identifies a distribution category; it does not measure performance on that task. Repository metadata

Listed library: transformers. Check that library's version and model support before loading the weights. Repository metadata

Repository access gate: false. This records the repository's gate setting when checked; licensing and use terms still apply. Repository metadata

What the configuration file says

The pinned config.json section text_config contains these values. Pinned configuration

max_position_embeddings
131,072
sliding_window
2,048
torch_dtype
bfloat16
num_attention_heads
32
num_key_value_heads
2
num_hidden_layers
52
hidden_size
6,656

Configuration values describe the checkpoint and its implementation. In particular, max_position_embeddings, sliding_window and rope_scaling do not independently establish an API context limit, tested usable length or hardware requirement. Runtime, quantization, cache, batch size and input length affect deployment behavior.

Read the retrieved model card for setup and qualifications. Model card

The documented record

Specifications, with their sources.

A missing value remains unknown. Input limits, output limits and the total context window are separate measurements.

Publisher
MetaSource ↗
Family
Muse GlimmerSource ↗
Version
meta-models/Muse-Glimmer-30BSource ↗
Availability
AvailableSource ↗
Access
Open weightsSource ↗
Inputs
Text · ImageSource ↗
Outputs
TextSource ↗
Context window
131,072 tokensSource ↗
Maximum input
Not documented
Maximum output
Not documented
Knowledge cutoff
Jan 4, 2026Knowledge cutoff · day precisionSource ↗
Training-data cutoff
Not documented
Training completed
Not documented
Release date
Aug 2026Model Release Date · month precisionSource ↗
License
Apache-2.0Source ↗
Input price / 1M tokens
Not documented
Output price / 1M tokens
Not documented

Read this with the numbers.

Model card states 131,072+ context; baseline is shown, not an invented hard maximum. Assistant speculative-decoding helper is not a separate user-facing model. Publisher repository evidence (2026-10-23): Publisher task label: image-text-to-text. Published architecture metadata: MuseGlimmerForConditionalGeneration. Repository library label: transformers. Published safetensors metadata counts 29,776,626,688 stored parameters; this is not an active-parameter count for mixture-of-experts models. Published text_config configuration: max_position_embeddings=131072; sliding_window=2048; dtype=bfloat16; 52 hidden layers. Checkpoint configuration is not a verified hosted serving limit; context and output limits remain separate documented fields.

Model identifiers and aliases 2
  • meta-models/Muse-Glimmer-30B-ExecuTorch-PTE
  • meta-models/Muse-Glimmer-30B-GGUF

Follow the evidence

Official sources.

  1. Muse Glimmer 30B official reference ↗huggingface.co
  2. Muse Glimmer 30B: official publisher repository metadata ↗huggingface.co
  3. Muse Glimmer 30B: publisher model card at revision cec94fcfd651 ↗huggingface.co
  4. Muse Glimmer 30B: published checkpoint configuration at revision cec94fcfd651 ↗huggingface.co

From the Headless Thrones newsroom

Editorial about Muse Glimmer 30B

No published articles are linked to this exact model yet. New coverage will appear here as it is published.

Your starting point

Muse Glimmer 30B

Meta · Open weights

Browse all models ↗
Inputs
Text · ImageSource ↗
Outputs
TextSource ↗
Context window
131,072 tokensSource ↗
Knowledge cutoff
Jan 4, 2026Knowledge cutoff · day precisionSource ↗

A place to begin comparing

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A record you can follow

The source belongs beside the fact.

Official sources

Model records cite the developer’s documentation, release notes and model cards. A listing alone is not a tested service or a recommendation.

Dates mean different things

Knowledge cutoff, training-data cutoff, training completion and release date are separate fields. A model’s name or repository upload date does not establish any of them.

Limits need context

Some publishers use rounded K/M token figures. Deployment, reasoning settings and regional access can change usable limits. Read the source before choosing.

Coverage and last check

Independent editorial model library assembled from primary vendor documentation and official publisher model cards. This is a dated, auditable inventory of the sources reviewed, not a claim to every model worldwide. First-party language-model inventory for OpenAI, Anthropic, Google Gemini/Gemma, xAI, Meta and Reflection. Includes vendor-listed legacy/retired identities with explicit statuses; official pretrained and instruction-tuned checkpoints remain distinct. Excludes embeddings, moderation/guard models, image/audio/video-only generation, community fine-tunes, speculative draft helpers, format/runtime/quantization duplicates, and PaliGemma dataset-specific benchmark fine-tunes. Availability means documented vendor API or downloadable official weights, not guaranteed account eligibility. Unknown specifications and dates remain null. Rounded K/M limits preserve vendor granularity; exact integers are used only where documented. No OpenRouter facts are used. Primary-source inventory of language-capable models from Alibaba/Qwen, DeepSeek, Moonshot, MiniMax, Z.ai, ByteDance Seed, Tencent, Baidu, Xiaomi, StepFun, 01.AI, Shanghai AI Laboratory, Baichuan and OpenBMB. Includes full reachable official public weight inventories plus documented hosted API models and explicit retirements. Embeddings, reward/classifier/router/tokenizer/draft-head components, standalone OCR/document converters and image/video/audio-only generators are excluded; multimodal language models retained. Official packaging variants are folded into aliases where a canonical listing was identifiable; remaining research/base/SFT variants retain exact identities. Not a guarantee every historical/private/regional model is available. Unknown specifications remain null; K/M API display quantities use decimal normalization where explicitly noted, rather than implying verified exact serving ceilings. Model-name dates and repository creation timestamps are never used as release/training dates. Open weights does not mean unrestricted or OSI-open-source licensing. Some model-card bodies were unreachable; their public inventory metadata is retained and disclosed. Official Mistral, Cohere, AI21, NVIDIA Nemotron, Microsoft Phi/MAI, Amazon Nova, IBM Granite, AllenAI OLMo/Molmo, TII Falcon, Nous Research, Arcee and Essential AI model inventory checked 2026-10-23. Includes base, instruction, reasoning, coding and text-producing multimodal models, including legacy API versions. Excludes embeddings/rerankers/reward and safety classifiers, image/audio/video-only generation, robotics-only models, duplicate quantization/runtime packages, adapters, intermediate checkpoint experiments, and third-party mirrors. Arcee coverage focuses on its named product families rather than exploratory merge repositories. Repository listing means listed, not independently verified API availability. K/M figures are nominal decimal normalizations when the source supplies shorthand; deployment-specific exact token limits may differ. Unverified values remain null. This is a source-backed inventory, not a complete claim-level independent editorial review. Supplemental official Liquid AI, Reka and Inception generative language catalog. Excludes media-only generators and decision/classification-only models. No assumed training dates.

Catalog edition updated . Source links are provided on each model record.

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