arXiv 2610.24628 · cs.CL · 2026-10-17
Muse Glimmer 30B: An On-Device Agentic Model
Arash Ebrahim Abtahizadeh, Muna Aghamelu, Adil Ahmed, Juan C. Angeles Acuna, Alexis Audran-Reiss, Asaf Avrahamy, Yoram Bachrach, Can Balioglu and 321 more
Meta
In latest · #360 Added on 2026-10-26
One-line takeaway
Muse Glimmer is an open-source 30B on-device agentic model with text and image input, quantized to about 4-bit for single consumer-GPU inference, claiming state-of-the-art agentic results for its size, released under Apache 2.0.
Muse Glimmer 是开源的 300 亿参数端侧智能体模型,支持文本和图片输入,量化到约 4 比特后可在单张消费级 GPU 上运行,在同尺寸工具调用和多步规划评测上领先,采用 Apache 2.0 许可。
Read on arXiv PDF Hugging Face discussion
Topics & disciplines
More on Agents
- Raven: The Harness of Harnesses for Composable Agentic Intelligence ▲748
- False Frontiers: Diagnosing and Mitigating Co-Cheating in Self-Evolving Search Agents ▲642
- Omni-IO Skills: Harnessing Your Agent Omni-Native ▲485
- BizAgentBench: Unveiling Hidden Challenges in Deploying Long-Horizon Agents for Real-World Business ▲388
- Player-1: Learning to Play from Synthetic Experience ▲347
More on LLMs
- Jeva: System One Models from First Principles 2026-10-13 · ▲371
- Post-Training Leaves Behavioral Shadows on Unrelated Decisions 2026-09-24 · ▲273
- WeVisOne-VL: Unified Visual Perception from Objects to Pixels 2026-10-22 · ▲247
- Thinking Inertia: LLMs Keep Thinking When Told Not To 2026-10-08 · ▲241
- OmniEdu: Open Foundation Models for Learning and Teaching 2026-09-19 · ▲240
More from Meta
- Native Action-Prior Learning from Videos for World Action Models 2026-10-02
- Sharpening Tax in Post-Training 2026-10-01
- World Action Modeling with Progressive Visual Planning 2026-10-01
- Context Language Models 2026-09-29
About this data
Metadata from the arXiv API (verified); upvotes from Hugging Face Daily Papers; citations from Semantic Scholar. Last refreshed 2026-10-26.
The takeaway is written by AIMap from the abstract; the abstract itself is not reproduced — read the original.