GitHub - meta-models/meta-oss-cookbook: All recipes for oss models from Meta Inc.

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Summary

A GitHub repository provides practical recipes to deploy and use Meta's Muse Glimmer, an open-weight 30B AI model designed for local agentic tasks on a single GPU.

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meta-models/meta-oss-cookbook

Source: https://github.com/meta-models/meta-oss-cookbook

Muse Glimmer Cookbook

Clone it, run it on the GPU you already own, and ship a working agent — one that plans, calls tools, and self-corrects — in a single sitting, fully offline.

Muse Glimmer is an open-weight model built for local agentic work on a single GPU. This cookbook is how you go from weights to a running agent.

What you build here

We built Muse Glimmer keeping 2 principles in mind:

  • Local-first: Inference runs on your own hardware, so nothing leaves your machine. No API keys, no gated access, no hosted dependency — everything works fully offline.
  • Agentic-first: Designed for the full tool-use loop — the model plans, calls a tool, feeds the result back, and self-corrects, taking multiple steps autonomously until it reaches the goal.

What you’ll have by the end

A local agent running on your own machine that completes a real multi-step task, offline, in a single sitting, starting from a fresh install.

Pick your path

You want toGo to
Run the model with a single commandquickstart/
Learn the tool-use loop (chat template, function calling, the agent loop)agentic-fundamentals/
Ship a flagship agent (structured output, reasoning control, a triage pipeline)recipes/
Serve Muse Glimmer (vLLM, Ollama, LM Studio, SGLang, llama.cpp, Unsloth, ExecuTorch)inference-server/
Deploy on specific partner hardware, and see which precisions it supportsplatform/
Call a hosted API instead of running the model yourself (needs a provider API key)hosted/

How every recipe is built

Each recipe follows the same contract so you always know what you’re getting:

  • Runs end to end, offline: Any network use is optional and flagged.
  • Recipe banner up top: Precision, model server, and the max VRAM we observed, before you run anything. We don’t publish numbers for hardware we haven’t run on — where a recipe hasn’t been measured, its banner says so instead of estimating.
  • Precision and server vary by recipe: Most are bf16 on vLLM; recipes/computer-use-web/ is a quantized GGUF on llama.cpp. Other combinations generally work; we just don’t re-verify every recipe against each one.
  • Copy-paste first: One command to run. The explanation comes after.
  • Ends with “make it yours”: The extension hook, plus troubleshooting.

About the model
FamilyLlama-derived dense decoder (Gemma2-style text stack with Muse Glimmer deltas).
Size30B dense decoder.
Suggested GPUFits a single 24–32 GB GPU when quantized (Q4/INT4, ~16–17 GB); bf16 needs ~60 GB (an 80 GB card, or sharded across several GPUs).
Context131072 tokens (128K).
ModalitiesText and image in, text out, plus tool calling — all supported today, through a dedicated ~1.8B perception encoder. Server support for image input varies; each inference-server/ page states where it stands. Video is not a supported input: the model card processes it as individual frames and is not explicitly optimized for it.
Tool formatAn XML-ish <atem:function_calls> block. See agentic-fundamentals/.
Chat framingChannel-scoped <|start|>role<|message|>…<|eot|> with a to=self reasoning channel.
LicenseSee the model card on HuggingFace.

Status

This cookbook is under active construction. Sections land incrementally, and each folder’s README states where it stands.

Contributions welcome. Every recipe follows the shared template in assets/RECIPE_TEMPLATE.md.

License

Please refer to the License

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