Meta is back with Muse Glimmer: local, agentic, multimodal, and open source!

Meta’s Muse Glimmer is a 30-billion-parameter open-source multimodal model, released under Apache 2.0 and positioned for privacy-conscious local agentic applications such as coding, document analysis, personal assistants, and tool-using workflows. Distilled from Muse, it combines a 2B vision encoder with a 28B text decoder, supports image and silent-video understanding, object detection, multimodal tool calling, and optional DFlash speculative decoding intended to accelerate structured generation such as code. Published benchmark results compare it favorably with Gemma and Qwen models across several agentic, coding, multimodal, reasoning, and safety evaluations, though results vary by task. The release includes day-one support across Transformers, llama.cpp, vLLM, Hugging Face Inference Endpoints, and TRL fine-tuning, with examples for local and managed deployment on multiple accelerator platforms. Demonstrations emphasize using the model with agents such as OpenClaw or Hermes to locate or create quantized local versions, deploy itself to cloud endpoints, benchmark and optimize serving configurations, and research Hugging Face Hub resources through connected tools.

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