Congratulations to the Ling team @AntLingAGI on the release of Ling-3.0-flash! 🎉 A 124B MoE model activating just 5.1B parameters per token—with hybrid-linear attention and native 256K context—is an exciting contribution to production-scale agent systems. We also applaud the team’s announce-first, open-source-next release approach. Separating the announcement from the weight release lets the model team freeze the final checkpoint, configuration, tokenizer, and serving semantics, while giving open-source inference projects a stable window for correctness testing, performance tuning, Docker builds, and recipe validation. The community also gets a clear timeline instead of an ambiguous “coming soon.” This is not a step back from day-0 support—it is a more sustainable way to deliver day-0 support for the artifacts users will actually run. vLLM’s open-source support for Ling-3.0-flash is coming soon and will be available when the model weights are open-sourced. We hope more model vendors adopt this release pattern in the future! Congrats again—we can’t wait to see what the community builds! 🚀