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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.

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Muse Glimmer interactive local agent lab on github.com
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# Introducing Muse Glimmer: An Open Agentic Model That Runs on Your Device

<p align="center">
  <img src="assets/header.jpg" alt="Muse Glimmer — open agentic local AI model network visualization" width="100%" />
</p>

<p align="center">
  <strong>Meta Muse Glimmer 30B</strong> · open-weight · Apache 2.0 · on-device agents · multimodal<br/>
  Interactive lab + deep-dive guide for the local agentic model released 10 August 2026
</p>

<p align="center">
  <a href="https://huggingface.co/meta-models/Muse-Glimmer-30B">Weights</a> ·
  <a href="https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model">Research blog</a> ·
  <a href="https://huggingface.co/blog/muse-glimmer">HF day-0 guide</a> ·
  <a href="BLOG.md">Article in this repo</a>
</p>

---

**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&nbsp;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`](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&nbsp;GB, trained for tool loops, failure recovery, and controllable reasoning.

| | |
|--|--|
| **Model** | Muse Glimmer 30B (dense multimodal) |
| **Weights** | [meta-models/Muse-Glimmer-30B](https://huggingface.co/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

```bash
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
```

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