Jingya Huang

MiniMax (official) @MiniMax_AI

More reference control. Fewer denoising steps. More places to run H3. The latest community contributions span 8-step reference-conditioned video + audio, 4-step distillation, sparse attention, and reference-token compression - alongside serving and workflow integrations across GPU servers, TPUs, and Apple Silicon. What matters is what this enables: more affordable experimentation, finer creative control, and more choices about where and how video generation runs. Open models become more useful when the community can improve the methods, adapt the runtime, and build workflows around them. Credit to @haoailab @yisun0618 @lanfeng491270 @guocheng_qian @Alissonerdx @ddalcu @tazmannner @HaochengXiUCB @huggingface @Jhuaplin, and the PTD, DMAD, SparkH3, TorchTPU and FlashML teams - including Zhengming Yu and zechengtang on GitHub. 11 additions to the H3 ecosystem index. Thank you to everyone building, testing, and sharing.

Sayak Paul @RisingSayak

We secretly worked on bringing TorchTPU support to Diffusers with @Jhuaplin 🔥 It needed us to first ship TP support so that large models could fit in TPU chips. Then a bunch of small implementation-level changes. Check out the post to know more:

Brigitte 🤗 @BrigitteTousi

Hey @Adweek, it's 2023 and you're still playing into sexist stereotypes of women having to "juggle" motherhood and a career? Yikes. @SashaMTL is one of the most brilliant AI scientists out there. Do better.

Clémentine Fourrier 🍊 is off till Dec 2026 (🪂) @clefourrier

Ready for the *biggest update* of the Open LLM Leaderboard yet? We just spent A YEAR of GPU time to make it more interesting and fairer! 🤯 How? With @nathanhabib1011, we added 3 new evals from the great EleutherAI harness 💥 and re-ran 2000+ models! 🚀 So, what changes? 🧵

Philipp Schmid @_philschmid

Struggling to Access GPUs to deploy Generative AI models? Or want to optimize cost performance? We've got something for you! 🤗 We are happy to share support for Stable Diffusion XL on @awscloud Inferentia2 and Amazon SageMaker. 🧠 👉 🧶

Thomas Wolf @Thom_Wolf

There is a beautiful story that just happened in AI so let me share it for a lighter tone weekend post among all the doom stories in our AI field this week. It’s a story of people on three continents building and sharing in the open a new small efficient and state-of-the-art AI model. It started a couple of months ago when a new team in the AI scene released their first model from their headquarters in Paris (France): Mistral 7B. Impressive model, small and very strong performances in the benchmarks, better than all previous models of this size. And open source! So you could build on top of it. Lewis in Bern (Switzerland) and Ed (in Lyon, in the South of France) both from the H4 team, a team of researchers in model fine-tuning and alignment were talking about it over a coffee, in one of these gatherings that often happen at Hugging Face to break the distance between people (literal distance as HF is a remote company). What about fine-tuning it using this new DPO method that a research team from Stanford in California just posted on Arxiv, says one? Hey, that’s a great idea, replies the other. We've just build a great code base (with Nathan, Nazneen, Costa, Younes and all the H4 team and TRL community) let's use it! The next day they start diving in the datasets openly shared on the HF hub and stumble upon two interesting large and good quality fine-tuning datasets recently open-sourced by OpenBMB, a Chinese team from Tsinghua: UltraFeedback and UltraChat. A few rounds of training experiments confirm the intuition, the resulting model is super strong, by far the strongest they have ever seen in their benchmarks from Berkeley and Stanford (LMSYS and Alpaca). Join Clementine, the big boss of the open evaluation leaderboard. Her deep dive into the model capabilities confirms the results: impressive performance. But the H4 team also hosts a famous faculty member, Pr. Sasha Rush, Associate Professor at Cornell University in his daytime, hacker at HF in his nighttime. Joining the conversation, he proposes to quickly draft a research paper to organize and share all the details with the community. A few days later, the model, called Zephyr (a wind like Mistral), paper, and all details are shared with the world. Quickly other companies, everywhere in the world starts to use it. LlamaIndex, a famous data framework and community, shares how the model blew their expectations on real-life use-case benchmarks, while researchers and practitioners discuss the paper and work on the Hugging Face hub. All this happened in just a few weeks catalyzed by open access to knowledge, models, research, and datasets released all over the world (Europe, California, China) and by the idea that people can build upon one another work in AI to bring real-world value with efficient and open models. Stories like this are numerous everywhere around us and make me really proud of the AI community and see how we can build amazingly useful things together. [the video is just me reading this Friday post hahah]