Hugging Face

Hugging Face (Twitter) RT @xeophon_: why does the chinese doordash continue to drop models

Hugging Face (Twitter) RT @MaziyarPanahi: the top two trending models on @huggingface are both for OCR! document processing is a hot topic, kids! 😈

Hugging Face (Twitter) RT @NVIDIAAIDev: This is what 3 million downloads looks like. 🥳 We owe a huge thank you to the AI community for making Llama Nemotron Nano VL 8B a favorite. 🤗 Try now on @huggingface: nvda.ws/4nWmwbV

Hugging Face (Twitter) RT @jadechoghari: Stay tuned with @NVIDIARobotics folks, we’re expanding @LeRobotHF’s sim capabilities! I can train & teleop my SO-101 from real → sim, drop custom assets, and collect data from home (or HF office). finally making progress on the robotics dataset problem. Project launches soon 👀

Hugging Face (Twitter) RT @victormustar: Introducing: HuggingChat Omni 💫 Select the best model for every prompt automatically 🚀 - Automatic model selection for your queries - 115 models available across 15 providers Available now all Hugging Face users. 100% open source.

Hugging Face (Twitter) RT @ClementDelangue: The main breakthrough of GPT-5 was to route your messages between a couple of different models to give you the best, cheapest & fastest answer possible. This is cool but imagine if you could do this not only for a couple of models but hundreds of them, big and small, fast and slow, in any language or specialized for any task - all at inference time. This is what we're introducing with HuggingChat Omni, powered by over 100 open-source models including gpt-oss, deepseek, qwen, kimi, smolLM, gemma, aya and many more already! And this is just the beginning as there are over 2 millions open models not only for text but image, audio, video, biology, chemistry, time-series and more on @huggingface!

Hugging Face (Twitter) RT @Meituan_LongCat: 🎉 LongCat-Audio-Codec is officially OPEN SOURCED! 🚀 -an audio codec solution optimized specifically for Speech LLMs. Key Breakthroughs: 1. Dual Tokens: Semantic and Acoustic Tokens are extracted in parallel at a low frame rate (16.7Hz / 60ms).This ensures both efficient modeling and full information integrity. 2. Ultra-Efficiency: LongCat-Audio-Codec maintains high intelligibility even at an extremely low bitrate, such as 0.43 kbps. 3. Real-Time Ready: Features a low-latency streaming decoder architecture. Latency is controlled to the hundred-millisecond level for real-time interaction. The integration of super-resolution in the decoder further enhances audio quality without extra models! This solution lowers technical barriers and optimizes resource efficiency for mobile/embedded Speech LLM deployment. 🔗 Code: Github: https://github.com/meituan-longcat/LongCat-Audio-Codec Huggingface: https://huggingface.co/meituan-longcat/LongCat-Audio-Codec

Hugging Face (Twitter) RT @engineerrprompt: Some interesting insights on open models/repos - 1 million new open-source AI repos landed on @huggingface in only 90 days. - @nvidia , historically a hardware vendor, is now the single largest contributor of open AI models (Nemotron, Cosmos, Gr00t, BioNeMo, Canary). - Chinese labs have moved from followers to co-leaders: Alibaba’s @Alibaba_Qwen , @deepseek_ai , Baidu, Tencent, MiniMax, Z.AI, @ByteDanceOSS , @Kimi_Moonshot and Zhipu all ship updates that rival or beat Western models on public leaderboards. - DeepSeek alone has >100 k Hugging Face followers and is pushing iterative V3 drops. - Fine-tuning is democratized—hundreds of LoRA adapters appear daily, letting individuals tune foundation models with only hundreds of samples. - Europe’s footprint is shrinking: outside @MistralAI Magistral and Stability’s image models, almost no EU players are visible in the open-source explosion. - Daily download counts for top repos now... Перейти на оригинальный пост

‌Hugging Face (Twitter) RT @abidlabs: Why did we build yet another experiment tracking library? We built @TrackioApp because experiment tracking shouldn’t be complicated. Most tools are cloud-heavy, bloated, or hard to customize. Trackio is different: it’s lightweight, local-first, and free. Run it on your machine, store logs in SQLite, visualize experiments instantly with a clean dashboard, or deploy online if you want. Embed dashboards anywhere, from blogs to internal docs. The API mirrors popular logging libraries, so you can switch without rewriting your code. At under 5,000 lines of Python, Trackio is small, open-source, and designed for extensibility. Fork it, tweak it, add what matters to you. No limits, no lock-in, just fast, flexible experiment tracking for ML developers who want control. Give it a star ⭐:

‌Hugging Face (Twitter) RT @HuggingPapers: ByteDance just released Sa2VA on Hugging Face. This MLLM marries SAM2 with LLaVA for dense grounded understanding of images & videos, offering SOTA performance in segmentation, grounding, and QA. https://huggingface.co/ByteDance/Sa2VA-InternVL3-14B

Hugging Face (Twitter) RT @LeRobotHF: 🚀 New in LeRobot: Multi-GPU training is now supported! We’ve integrated 🤗 Accelerate into our training pipeline, making it simple to scale your experiments across multiple GPUs with just one command. Whether you’re fine-tuning policies or running large-scale robot learning, LeRobot now handles distributed training easily. 👉 PR: https://github.com/huggingface/lerobot/pull/2154 Let’s accelerate robot learning together ⚙️🤖

Hugging Face (Twitter) RT @vanstriendaniel: Not enough people know about/use PRs for datasets on @huggingface. For many dynamic datasets, this can be a good workflow for versioning datasets and improving them over time.