Meta trained the model with distillation from a larger teacher, longer agent-heavy data, supervised fine-tuning, on-policy distillation, and reinforcement learning. Quantization compresses the model to under 20 GB, while a DFlash drafter enables speculative decoding for faster local generation.
Muse Glimmer accepts interleaved text and images, supports controllable reasoning effort, handles failure recovery, and is trained across more than 100 languages. It is useful for private offline assistants, local coding tools, desktop automation, and developers who want an open agent model without depending entirely on cloud inference.
