Hugging Face (Twitter)

RT @Xianbao_QIAN: 500+ hours of real world manipulation data, covering residential, kitchen, retail and office settings. A important step towards generalized manipulation models!

Great work Galaxea team!

https://huggingface.co/datasets/OpenGalaxea/Galaxea-Open-World-Dataset

Hugging Face (Twitter)

RT @DataChaz: This is wild.

A real-time webcam demo using SmolVLM from @huggingface and llama.cpp! 🤯

Running fully local on a MacBook M3.

Hugging Face (Twitter)

RT @Tu7uruu: Just dropped on HF! HunyuanVideo-Foley from Tencent AI Lab an end-to-end Text-Video-to-Audio (TV2A) model that turns silent videos into lifelike soundscapes

> 100k-hour curated TV2A dataset via automated pipeline
> Modality-balanced MMDiT: dual-stream audio-video fusion + text cross-attention
> REPA loss: aligns internal states with self-supervised audio features → higher fidelity & stability
> DAC-VAE audio codec: 48kHz, continuous latents, strong reconstruction across speech/music/sfx
> SOTA on Kling-Audio-Eval, VGGSound, and MovieGen-Audio-Bench (audio quality, semantic + temporal alignment)

Hugging Face (Twitter)

RT @TencentHunyuan: Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀

This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊

Key Innovations:

🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts.

🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements.

🔹High-Fidelity Audio: Using a Representation Alignment...

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Hugging Face (Twitter)

RT @pollenrobotics: Two Reachy 2 setting and clearing the table, all in real time teleoperation!

Shot in a single take with all the successes... and a small fail👀
One example of what Reachy 2 can do: efficient, versatile object manipulation, with the precision needed for delicate or fragile tasks

Hugging Face (Twitter)

RT @reach_vb: 🚨 Apple just released FastVLM on Hugging Face - 0.5, 1.5 and 7B real-time VLMs with WebGPU support 🤯

> 85x faster and 3.4x smaller than comparable sized VLMs
> 7.9x faster TTFT for larger models
> designed to output fewer output tokens and reduce encoding time for high resolution images

Bonus: works in REALTIME directly in your browser powered by transformers.js and WebGPU 🔥

Try it out on the demo below 👇

Hugging Face (Twitter)

RT @xenovacom: NEW: Apple releases FastVLM and MobileCLIP2 on Hugging Face! 🤗

The models are up to 85x faster and 3.4x smaller than previous work, enabling real-time VLM applications! 🤯

It can even do live video captioning 100% locally in your browser (zero install). Huge for accessibility!

Hugging Face (Twitter)

RT @RisingSayak: Lovely time presenting at #AIDev Amsterdam today ❤️

We explored some 📹 models (Wan, LTX, etc.), their existing capabilities, and limitations.

I am glad that the attendees found my presentation to be an enjoyable experience 🫡

Find the slides here ⬇️
bit.ly/open-vid-gen

Hugging Face (Twitter)

RT @Xianbao_QIAN: Meituan just open sourced their new MoE LLM LongCat on @huggingface

It's exciting to see new players! The model looks very interesting too with technical report.

https://huggingface.co/meituan-longcat/LongCat-Flash-Chat

Hugging Face (Twitter)

RT @NielsRogge: GLM-4.5 is beating Claude-4 Opus on the Berkeley Function Calling benchmark while costing 70x less

Hugging Face (Twitter)

RT @eliebakouch: The technical report of @Meituan_LongCat LongCat-Flash is crazy good and full of novelty.
The model is a 560B passive ~27B active MoE with adaptive number of active parameters depending on the context thanks to the Zero-Computational expert.

1) New architecture
> Layers have 2 Attention blocks and both FFN and MoE, that way you can overlap the 2 all-to-all coms. (also it's only 28 layers but you have to take into account the 2 attention blocks).
> They add the zero-computational expert that tokens can choose and do nothing, kinda like a "sink" for easy tokens.
> For load balancing, they have a dsv3-like aux loss free to set the average real/fake expert per token. They apply a decay schedule to this bias update. They also do loss balance control.

2) Scaling
> They made changes to MLA/MoE to have variance alignment at init. The gains are pretty impressive in Figure 5, but i don't know to what extent this has impact later on.
> Model...

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Hugging Face (Twitter)

RT @Meituan_LongCat: 🚀 LongCat-Flash-Chat Launches!

▫️ 560B Total Params | 18.6B-31.3B Dynamic Activation
▫️ Trained on 20T Tokens | 100+ tokens/sec Inference
▫️ High Performance: TerminalBench 39.5 | τ²-Bench 67.7

🔗 Model: https://huggingface.co/meituan-longcat/LongCat-Flash-Chat
💻 Try Now: longcat.ai

Hugging Face (Twitter)

RT @HuggingPapers: ByteDance Seed and Stanford introduce Mixture of Contexts (MoC) for long video generation, tackling the memory bottleneck with a novel sparse attention routing module.

It enables minute-long consistent videos with short-video cost.

‌Hugging Face (Twitter)

RT @AdinaYakup: Hunyuan-MT-7B 🔥 open translation model released by @TencentHunyuan

https://huggingface.co/collections/tencent/hunyuan-mt-68b42f76d473f82798882597

✨ Supports 33 languages, including 5 ethnic minority languages in China 👀
✨ Including a translation ensemble model: Chimera-7B
✨ Full pipeline: pretrain > CPT > SFT > enhancement > ensemble refinement > SOTA performance at similar scale

Hugging Face (Twitter)

RT @multimodalart: a mysterious new button appeared on the @huggingface Spaces Nano Banana app 👀