If you scroll down from the countdown at https://huggingface.co/Qwen/Qwen3.8-27B, you see a big model card with a bunch of sections: Highlights, Model Overview, Quickstart, Best Practices, Citation, etc!
No benchmarks on this yet as far as I can tell. We'll still need to wait another 5.5 hours for those I reckon.
Edit: Ladies and gentlemen, the model is live. Let the testing begin!
Looks like the Ling team open weighted a much smaller version of the Ling-3.0-flash they open weighted a few days ago. It's 8B params with 1.3B active, and seems to fall between the 4B and 8-12B Qwen and Gemma models in terms of performance.
Should have a massive tokens/sec on most systems. I quite like tiny MoE's conceptually.
Edit: looks like the model card actually reports speeds: > With FP8, Ling-3.0-tiny reaches around 100-105 tokens/s on DGX Spark and 86-90 tokens/s on an M4 Pro MacBook, with approximately 8.34 GiB peak memory usage at an 8K context length.
The Ling-3.0-flash MoE is now open-weighted at 124B A5B params. I know the original announcements were before the Kimi K3, DeepSeek-V4-Flash and Qwen3.8 hype, but this model might still have a good niche for itself due to its sizing.
Discussion on the benchmarks are here: https://www.reddit.com/r/LocalLLaMA/comments/1v4mltt/benchmarks_antling30flash_a_hybridreasoning_moe/ from almost 2 weeks ago.
> The first autonomous agent cyberattack is an unprecedented event that deserves unprecedented transparency. Today we're sharing everything we can: a full technical timeline, an interactive replay, and how we used an open model to defend ourselves, so defenders everyvwhere can learn from it and prepare for what's next.
By Clement Delangue, CEO of Hugging Face on LinkedIn