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
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。
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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.
Via Hacker News
Tags: ai, generative-ai, llama, local-llms, llms, llm, meta, llm-release
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