Hugging Face

Hugging Face (Twitter) RT @ClementDelangue: Looks like we're going to welcome two more Hugging Faces to the family next year. My wife is a hero! 💛💛💛@ReginaSil2

Hugging Face (Twitter) RT @unwind_ai_: ByteDance just dropped an OCR model that reads documents just like humans. This 0.3B model analyzes page layout first, then parses elements in parallel. 100% open-source.

Hugging Face (Twitter) RT @mfranz_on: Consider taking this Robotics Course from HuggingFace. The syllabus is impressive, covering topics from robotics foundations to reinforcement learning. It's an excellent way to start with robotics and build your first robot.

Hugging Face (Twitter) RT @cerebras: Due to popular demand, we've released more REAP models! @Zai_org ➡️GLM4.6-FP8 REAP@25% ➡️GLM4.6-FP8 REAP@30% ➡️GLM4.6-FP8 REAP@40% REAP is a one-shot pruning technique developed and open sourced by Cerebras. It compresses MoEs by up to 50% with minimal loss in coding ability. Links👇

Hugging Face (Twitter) RT @xeophon_: Chinese DoorDash, the foundation model company??

‌Hugging Face (Twitter) RT @reach_vb: Chinese doordash dropping MIT license foundation video models??? “We introduce LongCat-Video, a foundational video generation model with 13.6B parameters, delivering strong performance across Text-to-Video, Image-to-Video, and Video-Continuation generation tasks.” https://huggingface.co/meituan-longcat/LongCat-Video

Hugging Face (Twitter) RT @nalidoust: Introducing Tahoe-x1 (Tx1) by @tahoe_ai. A 3-billion-parameter, single-cell foundation model that learns unified representations of genes, cells, and drugs, achieving state-of-the-art performance across cancer-relevant cell biology benchmarks, open-sourced on @huggingface. 🧵

Hugging Face (Twitter) RT @cgeorgiaw: Huuuuuuge drop from @tahoe_ai team on @huggingface!!!!!! Let’s go cure cancer 💪🏻💪🏻💪🏻https://twitter.com/nalidoust/status/1981760790551298524#m

Hugging Face (Twitter) RT @ClementDelangue: Very cool open-source work from @PyTorch on reinforcement learning environments (we helped a tiny bit)! Feels like early days on the topic with already exciting work from @PrimeIntellect@MechanizeWork@mercor_ai for example but exciting to make this topic as open-source and collaborative as possible. We'll obviously make sure that you can share and use environments on @huggingface to unleash the power of the community and let us know how we can help further!

Hugging Face (Twitter) RT @ErikKaum: There's been a crazy OCR mania for the last couple of days 👀 And you can 1-click deploy most of these models directly from the Inference Endpoints catalog 🔥

Hugging Face (Twitter) RT @lhoestq: HF Datasets: built for audio, images, videos... And now, PDFs 📕 Still loadable in one line of code: >>> load_dataset("username/my_dataset") What should we do next for OCR datasets ? 🤗

Hugging Face (Twitter) RT @rohanpaul_ai: ⚖️ Isaacus ,an Australian foundational legal AI startup, launched Kanon 2 Embedder. Its a SOTA legal embedding LLM, and unveiled the Massive Legal Embedding Benchmark (MLEB), an open-source benchmark for evaluating legal information retrieval performance across six jurisdictions. They claim Kanon 2 Embedder is 9% higher than OpenAI Text Embedding 3 Large, 6% higher than Google Gemini Embedding, and over 30% faster than both. Against Voyage 3 Large, it is more accurate and 340% faster while being smaller. MLEB spans 6 jurisdictions and 5 domains with 10 expert-curated datasets, covering decisions, statutes, regulations, contracts, and literature across retrieval, zero-shot classification, and question answering. On MLEB, legal-tuned models beat similar general models, and Kanon 2 is trained on millions of legal sources from 38 jurisdictions, taking 1st on 5 of 10 tasks. The foundation model behind the embedder also raises the... Перейти на оригинальный пост