---
title: "Meta 发布开源模型 Muse Glimmer，专注 Agent 任务完成"
source: "Simon Willison 博客（Atom）"
source_tier: "t2"
url: "https://simonwillison.net/2026/Aug/10/introducing-muse-glimmer/#atom-everything"
permalink: "https://feed.astralor.com/items/itm_2f46c475cd9d5b223831f8e8"
published: "2026-08-10T23:56:03.000Z"
score: 79
channel: "tech"
tags: ["Agent","Meta","Muse Glimmer","开源模型","本地 LLM","视觉语言模型"]
---

# Meta 发布开源模型 Muse Glimmer，专注 Agent 任务完成

## AI 点评

> 这个模型最让我兴奋的不是参数规模，而是 Apache 2.0 许可和本地可运行的组合——它意味着开发者可以真正把 Agent 技术握在自己手里，而不是被平台绑定。对于正在做工具调用和长任务规划方向的人，Muse Glimmer 值得立刻拉下来跑一轮。

## 速览

Meta 发布全新 30B 开源模型 Muse Glimmer，基于 Apache 2.0 许可，重回开放权重阵营。该模型针对端到端 Agent 任务完成优化，在 DeepSearch QA、MCP-Atlas、Tau-Bench 和 SWE-Bench 等完整任务基准上表现突出，支持可靠工具调用、长程多步推理及视觉输入。Simon Willison 试用 18.16 GB 量化版，在 32GB 内存机器上可流畅运行，并验证了其代码库探索与图像描述能力。

## 关键点

- Muse Glimmer 是一个 30B 参数的全新开源模型，采用 Apache 2.0 许可，比 Llama 的授权更宽松。
- 针对端到端 Agent 任务完成优化，在 DeepSearch QA、MCP-Atlas、Tau-Bench、SWE-Bench 等基准上表现优异。
- 支持可靠工具调用、长程多步推理和视觉理解，适合构建本地代理。
- 18.16 GB 量化版可在 32GB 内存设备上运行，作者已用 LM Studio 和 llm-coding-agent 插件完成实测。

## 全文

Introducing Muse Glimmer Meta are back in the open weights game! Muse Glimmer is a brand new 30B model under a clean Apache 2.0 license (a step up from the janky Llama licenses of old). They claim to have optimized it for exactly the kind of things I'm looking for in a local model: End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕-Bench and SWE-Bench, which measure its ability to work within scaffolds, write and debug code, and resolve multi-turn requests from start to finish. Reliable Tool Use. The model handles a wide range of function calls, invoking tools with precise schemas throughout extended workflows. Multi-Step Reasoning. Muse Glimmer chains reasoning over long horizons, sustaining coherent plans across complex, extended workflows. [...] Here's a pelican which I generated using LM Studio's 18.16 GB version of the model: I also tried it out with my llm-coding-agent plugin, running against a fresh checkout of Datasette with the prompt: how does auth work? Here's the response, at the end of a long transcript showing all of the tool calls it made to explore the codebase. I ran this using llm-lmstudio with this patch applied to upgrade it for compatibility with LLM 0.32. I really like this size of model, because if a machine has 32 GB of RAM or more (mine has 128GB) it leaves plenty of space for running other applications at the same time. Glimmer is a vision model, so I asked it to describe this image: llm -m lmstudio/meta/muse-glimmer -a https://static.inaturalist.org/photos/714731804/large.jpg 'describe image' Here's what I got back: The photograph shows a rocky, breakwater-style shoreline on an overcast day with a smooth, gray body of water and a faint dock/pier line in the soft-focused background. In the foreground two large brown pelicans, Pelecanus occidentalis, are perched on the jumbled gray-white stones. They have the species’ characteristic long, down-curved yellow-orange bills with a large throat pouch, long slender necks and mottled brown-gray plumage on the back and wings. The pelican on the left is turned slightly toward the camera and appears to be preening or resting its bill against its chest; a pale, whitish patch is visible on the crown and nape and a small crest of feathers is raised. The pelican on the right faces mostly forward/right, its head up, bill pointing down and to the right, with the same pale head markings and the barred, darker wing feathers clearly visible. Scattered among the rocks around the two pelicans are several much smaller dark birds — gulls/tern-like birds in muted brown-gray plumage. One dark bird sits on the far left on a rock, another brownish bird stands to the right of the right-hand pelican, a grayish bird with a reddish bill is in the lower right foreground, and a further small dark bird is at the extreme right edge of the frame. The overall light is flat and diffused, giving the water and sky a muted, almost monochromatic palette that contrasts with the textured rock and the detailed feathering of the pelicans. The composition places the two big birds as the dominant subjects, framed against the calm water and the low, rocky perch. Via Hacker News Tags: ai, generative-ai, llama, local-llms, llms, llm, vision-llms, meta, llm-release

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_2f46c475cd9d5b223831f8e8