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We..."},"evidence_pages":[{"url":"https://huggingface.co/moonshotai/Moonlight-16B-A3B-Instruct/raw/main/README.md","final_url":"https://huggingface.co/moonshotai/Moonlight-16B-A3B-Instruct/raw/main/README.md","title":"moonshotai/Moonlight-16B-A3B-Instruct model card","http_status":200,"content_type":"text/plain; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-11T02:50:46.548541+00:00","bytes":8355,"raw_path":"a8324027b1b8105196b0816ff328249f2079b213c0a15aefb3ab4054b7d88389.md","content_hash":"4d74df70b1d105726ff2a6944fce443a8aee57671c5edaf1af5870a37d11c91b","excerpt_chars":1200,"truncated":true,"excerpt":"--- license: mit library_name: transformers --- <div align=\"center\"> <a href=\"https://github.com/MoonshotAI/Moonlight\"><img width=\"80%\" src=\"figures/banner.png\"></a> </div> <!-- # Muon is Scalable For LLM Training --> <div align=\"center\"> <a href=\"https://github.com/MoonshotAI/Moonlight/blob/master/Moonlight.pdf\" ><img src=\"figures/logo.png\" height=\"16\" width=\"16\" style=\"display: inline-block; vertical-align: middle; margin: 2px;\"><b style=\"display: inline-block;\"> Tech Report</b></a> | <a href=\"https://huggingface.co/moonshotai/Moonlight-16B-A3B\"><img src=\"https://huggingface.co/front/assets/huggingface_logo-noborder.svg\" height=\"16\" width=\"16\" style=\"display: inline-block; vertical-align: middle; margin: 2px;\"><b style=\"display: inline-block;\"> HuggingFace</b></a> | <a href=\"#\"><img src=\"figures/megatron.png\" height=\"16\" width=\"16\" style=\"display: inline-block; vertical-align: middle; margin: 2px;\"><b style=\"display: inline-block;\">Megatron(coming soon)</b></a> </div> Abstract Recently, the [Muon optimizer](https://github.com/KellerJordan/Muon) has demonstrated strong results in training small-scale language models, but the scalability to larger models has not been proven. 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