Moonlight
Efficient, Open-Source LLMs from Moonshot AI
Moonlight launched on February 28, 2025 with 11 votes, #1,584 of 5,999 launches that month and more than 50% of that year's launches. The site was still online in September 2026, 1 year on.
What it does
Moonlight is the open-source 3B/16B MoE LLMs from Moonshot AI, trained with the Muon optimizer for ~2x compute efficiency compared to AdamW. Pretrained, instruct-tuned, and intermediate checkpoints available.
Muon is Scalable for LLM Training. Contribute to MoonshotAI/Moonlight development by creating an account on GitHub.
Recently, the Muon optimizer based on matrix orthogonalization has demonstrated strong results in training small-scale language models, but the scalability to larger models has not been proven. We identify two crucial techniques for scaling up Muon: (1) adding weight decay and (2) carefully adjusting the per-parameter update scale. These techniques allow Muon to work out-of-the-box on large-scale training without the need of hyper-parameter tuning. Scaling law experiments indicate that Muon achieves ∼ 2× computational efficiency compared to AdamW with compute optimal training. Based on these improvements, we introduce Moonlight , a 3B/16B-parameter Mixture-of-Expert (MoE) model trained with…from github.com
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