GoKawiilToday, we're introducing Muse Glimmer, the next model from Meta Superintelligence Labs, and open sourcing the model weights under a permissive Apache 2.0 license.
Muse Glimmer is a 30-billion-parameter model optimized for always-on local agent workflows. It’s small enough to run on a Mac or PC with a single consumer GPU, enabling use cases that range from local agents and function calling, to local coding, and LLM-as-a-judge evaluation. Muse Glimmer delivers strong performance on key agentic use cases and benchmarks compared with leading models in its size category.
Foundation models have achieved remarkable capabilities across reasoning, code generation, and tool use — yet most deployments still depend on cloud infrastructure and network access. Running models locally enables you to use AI anywhere, anytime, with or without an internet connection. This is increasingly viable: the open source community has shown that smaller models, when trained effectively, can approach frontier-level performance on targeted tasks. Muse Glimmer is optimized for these local use cases.
Keeping with our long tradition of sharing fundamental AI research, we're releasing Muse Glimmer open weights today on Hugging Face, along with developer documentation to help you start building and running your own agents. Muse Glimmer is built to work with the tools developers already use. Optimized integrations on llama.cpp, MLX, and ExecuTorch will land in the coming days, so you can go from download to working agent in minutes.
How We Trained Muse Glimmer
An agent that manages your schedule, drafts your messages, organizes your files, and learns how you work needs deep access to personal context. It also needs several capabilities working in concert: long-horizon execution, precise tool calling, multimodal understanding, long-context memory, and instruction following.
We designed Muse Glimmer to balance capability against the memory and compute constraints of local hardware. This required a compact architecture, a novel distillation recipe that transfers agentic reasoning from a much larger teacher model, and inference optimizations — including quantization — to meet latency expectations. We achieved this in the following phases:
Pre-Training. We trained Muse Glimmer on Muse Spark's outputs using logit distillation, leveraging a similar data mix as the teacher.
We trained Muse Glimmer on Muse Spark's outputs using logit distillation, leveraging a similar data mix as the teacher. Mid-Training. We trained the model on longer-context, more agent-heavy data with richer reasoning traces, alongside organic data.
We trained the model on longer-context, more agent-heavy data with richer reasoning traces, alongside organic data. Post-Training. We combined supervised fine-tuning with a mix of on-policy distillation and reinforcement learning across general, reasoning, coding, and agentic domains.
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