# Simon Willison 试用了 Muse Glimmer 模型，该模型声称具备端到端代理任务完成、可靠工具使用和多步推理能力，并分享了本地运行体验

- 来源：Simon Willison
- 发布时间：2026-08-11 07:56
- AIWatch 分数：58
- AIWatch 标记：未精选
- AIWatch 链接：https://aiwatch.icu/events/evt_01kzq2edxxwt3nzjatpnm18a6j
- 原文链接：https://simonwillison.net/2026/Aug/10/introducing-muse-glimmer/#atom-everything

## 精选理由

常规快讯，保留列表

## AI 摘要

Simon Willison 试用了 Muse Glimmer 模型，该模型声称具备端到端代理任务完成、可靠工具使用和多步推理能力，并分享了本地运行体验。

## 正文

Introducing Muse Glimmer

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:

All the pieces are there but they are pretty jumbled together.

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.

Via Hacker News

Tags: ai, generative-ai, llama, local-llms, llms, llm, meta, llm-release