Muse-Glimmer

About

What Muse-Glimmer is

Read our How to Run Muse Glimmer Guide! Muse Glimmer Authors: Meta Superintelligence Lab Model Release Date: August 2026 License: Apache 2.0 Muse Glimmer is a 30-billion-parameter causal language model with a dedicated perception encoder, distilled from Muse Spark and purpose-built for autonomous agentic tasks on consumer hardware. The model integrates multi-step reasoning, reliable tool use, multimodal understanding, and failure recovery into a single model that runs locally without requiring cloud infrastructure or network access. Building effective agents requires key capabilities working together to achieve the user’s goals. Muse Glimmer is trained and evaluated on these capabilities: End-to-end Agentic Task Completion. Muse Glimmer achieves strong success rates on full-task benchmarks including DeepSearch QA, MCP-Atlas, 𝛕3\-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. Failure Recovery. When a tool call fails or returns an unexpected result, the model diagnoses the error and retries rather than halt. Multimodal Input and Reasoning. Through a dedicated perception encoder, the model accepts interleaved text and images. This enables agents to interpret screenshots, charts, and documents alongside conversation. Scaffold Compatibility. Muse Glimmer works across OpenClaw, Hermes Agent, and other agentic orchestration patterns. Controllable Effort. The model supports different reasoning strengths to select the right balance between quality and speed. Multilingual. Muse Glimmer is trained on data from more than 100 languages. Muse Glimmer-30B Model Overview Optimized for Local Deployments Muse Glimmer was optimized for local deployment, and designed to run at practical speeds on consumer hardware without sacrificing quality. Fitting the Model on Your Device. We use quantization techniques to compres

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys muse-glimmer for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (muse-glimmer below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"muse-glimmer","messages":[{"role":"user","content":"Hello"}]}'

Details

Languages, data & research

Papers

Licence

Open weights

Sources

Weights & code

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