Meta is back with Muse Glimmer: local, agentic, multimodal, and open source
热度趋势
百分比基于当前可用热度信号，而非评论数或独立用户人数。
官方发布带来Hugging Face 模型更新信号，适合跟踪能力变化、生态影响和后续落地。
Meta 发布了 Muse Glimmer，这是一款专为本地、代理用例设计的新型多模态模型。它从 Muse 中提炼而来，拥有 30B 参数，并以 Apache 2.0 许可证发布，非常适合注重隐私的应用，例如编码、文档分析和个人助理。…
Great news from the OGs of open source LLMs! Muse Glimmer, released today, is Meta’s new multimodal model, especially designed for local agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it’s ideal deploying locally for privacy, reducing costs, or just hacking around. It’s intended for privacy-aware applications such as coding, document analysis, personal assistants, Claw- or Hermes-like setups.
To celebrate, we are shipping with Meta day-0 support in transformers, llama.cpp, vLLM, Inference Endpoints, and other libraries. We built a few cool things and explain our findings in this blog.
Check out the demos below for inspiration.
You can find Muse Glimmer on the Hugging Face Hub.
Benchmarks
Benchmark results
Scores are reported as published. Bold indicates the best result among the compared models; ↓ indicates lower is better.
Category Benchmark Muse Glimmer-30B High Reasoning Gemma4-31B Thinking Mode Qwen3.6-27B Thinking Mode
General Agentic MCP Atlas 75.5 54.2 62.5
General Agentic DeepSearch QA 74.6 61.7 71.1
General Agentic τ³-Banking 23.5 15.1 16.7
General Agentic WildClawBench 47.6 37.6 43.2
General Agentic GDPval-AA 953 811 1141
General Agentic GAIA2 43.3 36.4 40.0
General Agentic SkillsBench (With Skills) 44.3 32.4 46.6
General Agentic OSWorld-Verified 65.9 58.5 75.6
Agentic Coding SWE-Bench Pro 51.2 36.9 50.2
Agentic Coding SWE-Bench Verified 76.0 66.6 77.2
Agentic Coding TerminalBench 2.1 51.7 43.4 60.7
Agentic Coding SciCode 43.6 43.4 39.8
Multimodal Charxiv Reasoning 78.8 77.7 78.4
Multimodal ScreenSpot Pro 75.4 75.9 76.1
Multimodal OmniDocBench v1.5 75.8 72.5 77.8
Multimodal MMMU Pro 74 73 75