MODEL PROFILE

Muse-Glimmer-30B-GGUF

meta-models · Muse-Glimmer-30B-GGUF

**Authors:** Meta Superintelligence Lab **Model Release Date:** August 2026 **License:** Apache 2.0 > [!NOTE] > This repo contains GGUF conversions of `meta-models/Muse-Glimmer-30B` for llama.cpp: two quantized text builds, a perception encoder for image input, and a DFlash drafter for speculative decoding. > Everything you need to install, download and serve them is on this page. No bf16 GGUF is published here — use the base repo if you need full precision. Muse Glimmer is a 30-billion-parameter causal language model with a dedicated perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks on consumer hardware. The model integrates multi-step reasoning, reliable tool use, multimodal understanding, and failure recovery into a single model that runs locally without requiring cloud infrastructure or network access.

Visual QAlicense:apache-2.0
Context windowNot disclosed
Input → OutputText、Image → Text
Model weightsNot verified
Profile updated2026/11/2 19:16:27
OVERVIEW

Model overview

**Authors:** Meta Superintelligence Lab **Model Release Date:** August 2026 **License:** Apache 2.0 > [!NOTE] > This repo contains GGUF conversions of `meta-models/Muse-Glimmer-30B` for llama.cpp: two quantized text builds, a perception encoder for image input, and a DFlash drafter for speculative decoding. > Everything you need to install, download and serve them is on this page. No bf16 GGUF is published here — use the base repo if you need full precision. Muse Glimmer is a 30-billion-parameter causal language model with a dedicated perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks on consumer hardware. The model integrates multi-step reasoning, reliable tool use, multimodal understanding, and failure recovery into a single model that runs locally without requiring cloud infrastructure or network access.

Key features

特点摘要待依据来源补齐,不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

PRICING & COST

Source prices & cost analysis

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Estimate monthly base token costs

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

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Frequently asked questions

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。