Hugging Face

Hugging Face (Twitter) RT @ClementDelangue: Cool reproduction of “Lora without regret” from @thinkymachines by @ben_burtenshaw in TRL

Hugging Face (Twitter) RT @johnschulman2: Really happy to see people reproducing the result that LoRA rank=1 closely matches full fine-tuning on many RL fine-tuning problems. Here are a couple nice ones: https://twitter.com/ben_burtenshaw/status/1974191312229577085https://twitter.com/zzlccc/status/1973612326747336767#m

Hugging Face (Twitter) RT @Alibaba_Qwen: 🚀 Qwen3-VL-30B-A3B-Instruct & Thinking are here! Smaller size, same powerhouse performance 💪—packed with all the capabilities of Qwen3-VL! 🔧 With just 3B active params, it’s rivaling GPT-5-Mini & Claude4-Sonnet — and often beating them across STEM, VQA, OCR, Video, Agent tasks, and more. And that’s not all: we’re also releasing an FP8 version, plus the FP8 of the massive Qwen3-VL-235B-A22B! Try it out and make your multimodal AI applications run faster!🧠🖼️ Qwen Chat: https://chat.qwen.ai/?models=qwen3-vl-30b-a3b Github&Cookbooks： https://github.com/QwenLM/Qwen3-VL/blob/main/cookbooks API: https://www.alibabacloud.com/help/en/model-studio/models#5540e6e52e1xx Blog： https://qwen.ai/blog?id=99f0335c4ad9ff6153e517418d48535ab6d8afef&from=research.latest-advancements-list ModelScope: https://modelscope.cn/collections/Qwen3-VL-5c7a94c8cb144b HuggingFace: https://huggingface.co/collections/Qwen/qwen3-vl-68d2a7c1b8a8afce4ebd2dbe

‌Hugging Face (Twitter) RT @charliebtan: 🚀 New dataset: ManyPeptidesMD https://huggingface.co/datasets/transferable-samplers/many-peptides-md 🤯 4.3 ms of MD across 21,700 peptides Huge thanks to @huggingface for hosting 🤗 With @majdi_has, @leonklein26, Saifuddin Syed, @dom_beaini, @mmbronstein, @AlexanderTong7, @k_neklyudov Read on 👇

Hugging Face (Twitter) RT @xenovacom: IBM just released Granite 4.0, their latest series of small language models! These models excel at agentic workflows (tool calling), document analysis, RAG, and more. 🚀 The "Micro" (3.4B) model can even run 100% locally in your browser on WebGPU, powered by 🤗 Transformers.js!

Hugging Face (Twitter) RT @MaziyarPanahi: just hit 4k followers on @huggingface! 🤗 couldn’t have done it without the incredible open-source AI community 💜 Grateful for your trust, support, and collaboration.

Hugging Face (Twitter) RT @calebfahlgren: .@pmarca: "My guess is we are going to live in a world in which most aggregate AI is going to be executed probably on smaller form factors and probably most of that is going to be open source" https://twitter.com/collision/status/1973473479061278737#m

Hugging Face (Twitter) RT @ClementDelangue: 🦾Great📷 milestone for open-source robotics: pi0 & pi0.5 by @physical_int are now on @huggingface, fully ported to PyTorch in @LeRobotHF and validated side-by-side with OpenPI for everyone to experiment with, fine-tune & deploy in their robots! As described by Physical Intelligence, π₀.₅ is a Vision-Language-Action model which represents a significant evolution from π₀ to address a big challenge in robotics: open-world generalization. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training. Generalization must occur at multiple levels: - Physical Level: Understanding how to pick up a spoon (by the handle) or plate (by the edge), even with unseen objects in cluttered environments - Semantic Level: Understanding task semantics, where to put clothes and shoes (laundry hamper, not on the bed), and what tools... Перейти на оригинальный пост

Hugging Face (Twitter) RT @reach_vb: Pretty cool to see a MIT licensed 15B model competing w/ DeepSeek R1 - how are the vibes? 👀

Hugging Face (Twitter) RT @xeophon_: HERE THEY ARE https://twitter.com/xeophon_/status/1973740416005751285#m

Hugging Face (Twitter) What other features would you like to see in 𝚝𝚛𝚊𝚌𝚔𝚒𝚘, our experiment tracking library? https://twitter.com/TrackioApp/status/1973834043210018828#m

Hugging Face (Twitter) RT @VoyageAI: To evaluate embeddings and retrieval, we need more benchmarks beyond MTEB that are less vulnerable to overfitting. That’s why RTEB was just beta-launched! ⚖️ Both open and held-out datasets to prevent overfitting to evaluation sets. 🌍 Realistic datasets from critical enterprise domains like law, healthcare, code, and finance. 🔎 Only focus on retrieval applications with relevant large-scale datasets. Check out the blog and leaderboard on @huggingface and join the community in building a stronger, more reliable benchmark. Blog: mongodb.social/6013Ai5sz