Curated with by@mohithkumar808

№ 0036GitHub

Muse Glimmer interactive local agent lab

Cobus Greyling's companion repo for Muse Glimmer 30B pairs a long-form intro with an offline-first interactive lab for exploring agent loops, benchmarks and memory envelopes before downloading the weights.

Open source github.com
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cobusgreyling/muse-glimmerREADME ↗

Introducing Muse Glimmer: An Open Agentic Model That Runs on Your Device

Muse Glimmer is Meta Superintelligence Labs’ open-weight 30-billion-parameter agentic multimodal model optimized for always-on local agent workflows. It runs on consumer hardware (Mac / PC with a performant GPU or large unified memory) after 4-bit quantization (under 20 GB for the LM), with tool calling, failure recovery, controllable reasoning, and a 2B perception encoder for images and video frames.

This repository is a search-friendly companion: a long-form intro (BLOG.md) plus an offline-first interactive lab so you can feel agent loops, benchmarks, and memory envelopes before you download the weights.

Keywords: Muse Glimmer, Meta AI, open agentic model, local LLM, on-device AI, Apache 2.0, function calling, OpenClaw, llama.cpp, GGUF, multimodal agents, DFlash, SWE-Bench, MCP Atlas

The one-liner

An open agentic model that runs on your device — Apache 2.0 weights, dense 30B (2B perception + 28B decoder), ~4-bit under ~20 GB, trained for tool loops, failure recovery, and controllable reasoning.
Model Muse Glimmer 30B (dense multimodal)
Weights meta-models/Muse-Glimmer-30B
License Apache 2.0
Released 10 August 2026
This repo Offline-first lab + optional live OpenAI-compatible chat

30-second start

git clone https://github.com/cobusgreyling/Muse-Glimmer.git
cd Muse-Glimmer

python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python app.py

Open **http://127.0.0.1:7

Muse Glimmer interactive local agent lab on github.com

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