Meta Muse Glimmer 30B · open-weight · Apache 2.0 · on-device agents · multimodal
Interactive lab + deep-dive guide for the local agentic model released 10 August 2026
Weights · Research blog · HF day-0 guide · Article in this repo
--- **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`](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](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 ``` Open **http://127.0.0.1:7870** Or: ```bash ./run.sh ``` ### Docker ```bash docker compose up --build # → http://127.0.0.1:7870 ``` No GPU. No API key. Scenario playback, benchmarks, and memory sizing work **offline**. ### Optional live chat (llama.cpp / Hugging Face) Full guide: **[docs/LIVE.md](docs/LIVE.md)** · Publish checklist: **[PUBLISH.md](PUBLISH.md)** **A) On-device with llama.cpp** ```bash # Terminal 1 — serve GGUF (needs llama.cpp installed) ./scripts/serve-llamacpp.sh # Terminal 2 — wire lab + run ./scripts/configure-live.sh llamacpp ./run.sh # → Live chat tab → Probe endpoint → Send ``` **B) Hugging Face Inference Endpoint** ```bash export HF_ENDPOINT_URL="https://XXXX.region.cloud.endpoints.huggingface.cloud" export HF_TOKEN="hf_…" ./scripts/configure-live.sh hf-endpoint ./scripts/probe-live.sh # lab must be running for full probe ./run.sh ``` Or hand-edit `.env` (see [`.env.example`](.env.example)). Leave `OPENAI_MODEL` empty to auto-pick from `GET /v1/models`. --- ## What the lab shows | Tab | What you learn | |-----|----------------| | **Agent loops** | Multi-step local agents: tools, **failure recovery**, coding fix, multimodal tool call, reasoning low vs high | | **Benchmarks** | Interactive scoreboard vs Gemma4-31B & Qwen3.6-27B (published launch numbers) | | **Memory** | Footprint calculator — why ~4-bit + 24–32 GB is the practical envelope | | **Live chat** | Optional `/v1/chat/completions` against *your* Muse Glimmer endpoint | ### Scenario highlights 1. **Home Assistant dashboard** — discover → 401 → recover token → build → deploy 2. **Coding fix** — red test → patch → green 3. **Multimodal tools** — image city → `weather.get` → clothing advice 4. **Reasoning A/B** — same architecture question, low vs high effort --- ## Why Muse Glimmer matters Most “local models” are chatty generalists squeezed onto a laptop. **Muse Glimmer** is aimed at a harder job: - **Always-on agents** that own multi-step work on-device - **Privacy** — contracts, code, home automation stay local - **Cost** — no per-token meter for every tool hop - **Latency** — no round-trip to a distant region for every thought Meta’s claim (launch materials): strong agentic scores in the ~27–31B class, with deliberate training for **function calling**, **long horizons**, and **diagnose-and-retry** when tools fail — not just next-token fluency. Details and nuance: **[BLOG.md](BLOG.md)**. --- ## Architecture (cheat sheet) ```text ┌─────────────────────────────────────────────────────────┐ │ Muse Glimmer 30B (dense multimodal) │ │ │ │ Perception Encoder (~2B ViT-style) │ │ │ images / video frames │ │ ▼ │ │ Text decoder (~28B) │ │ · 52 layers · (SWA×3 + Full)×13 │ │ · GQA (16 Q per KV) · Q-K norm │ │ │ │ Optional: DFlash speculative drafter (block proposals)│ └─────────────────────────────────────────────────────────┘ │ ▼ ~4-bit quant → LM ≲ 20 GB 24–32 GB device envelope (KV + vision + drafter headroom) ``` --- ## API | Method | Path | Description | |--------|------|-------------| | `GET` | `/` | Lab UI | | `GET` | `/api/health` | Demo / live status | | `GET` | `/api/model-card` | Specs, hub links, capabilities | | `GET` | `/api/scenarios` | Scenario list | | `GET` | `/api/scenarios/{id}` | Full agent transcript fixture | | `GET` | `/api/benchmarks` | Published comparison rows | | `GET` | `/api/prompts` | Example prompts | | `POST` | `/api/footprint` | Memory envelope estimate | | `GET` | `/api/live/probe` | Reachability + models + tiny completion | | `GET` | `/api/live/models` | List models from configured endpoint | | `POST` | `/api/chat` | Live OpenAI-compatible chat (optional) | --- ## Project layout ```text Muse-Glimmer/ ├── README.md ├── BLOG.md # Long-form intro article ├── PUBLISH.md # Standalone repo publish checklist ├── docs/LIVE.md # llama.cpp + HF live wiring ├── app.py # FastAPI lab ├── scripts/ │ ├── configure-live.sh # .env presets (llamacpp | hf-endpoint | openrouter) │ ├── serve-llamacpp.sh # start llama OpenAI server │ └── probe-live.sh # health-check live endpoint ├── requirements.txt ├── run.sh ├── .env.example ├── assets/header.jpg ├── data/ │ ├── model-card.json │ ├── agent-scenarios.json │ └── prompts.json └── static/ # UI ``` --- ## Run Muse Glimmer itself (not just the lab) | Path | Link / note | |------|-------------| | Full weights | [Hugging Face](https://huggingface.co/meta-models/Muse-Glimmer-30B) | | GGUF | [meta GGUF](https://huggingface.co/meta-models/Muse-Glimmer-30B-GGUF) · [Unsloth](https://huggingface.co/unsloth/Muse-Glimmer-30B-GGUF) | | Research blog | [research.meta.ai](https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model) | | HF day-0 guide | [huggingface.co/blog/muse-glimmer](https://huggingface.co/blog/muse-glimmer) | | transformers / llama.cpp / vLLM | Day-0 support called out in HF blog | | Cloud | Together · Fireworks · OpenRouter · HF Inference Endpoints | ```python # transformers sketch (needs sufficient VRAM / offload) from transformers import AutoProcessor, AutoModelForMultimodalLM MODEL_ID = "meta-models/Muse-Glimmer-30B" processor = AutoProcessor.from_pretrained(MODEL_ID) model = AutoModelForMultimodalLM.from_pretrained( MODEL_ID, dtype="auto", device_map="auto" ) ``` --- ## Stack FastAPI · Uvicorn · vanilla HTML/CSS/JS · offline JSON fixtures · optional httpx → OpenAI-compatible API --- ## Disclaimer This lab is an **independent educational companion**, not an official Meta product. Agent timeline transcripts are **illustrative fixtures** for teaching agent behavior. Benchmark numbers are taken from public launch materials (Hugging Face / Meta, August 2026). Model weights remain under Meta’s **Apache 2.0** release; this repo’s code is MIT. --- ## License - Lab code & fixtures: [MIT](LICENSE) - Muse Glimmer weights: Apache 2.0 (Meta)