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.
Get started
To download and run Muse Glimmer with Ollama, use:
ollama run muse-glimmer
Ollama’s MLX engine provides state-of-the-art performance on Apple Silicon, with support for DFlash and image input:
ollama run muse-glimmer:30b-mlx
Model information
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.
No comments:
Post a Comment