Hugging Face

‌Hugging Face (Twitter) RT @toyxyz3:

Hugging Face (Twitter) RT @ngxson: Long-awaited feature has dropped! You can now edit GGUF metadata directly from Hugging Face, without having to download the model locally 🔥 Huge kudos to @mishig25 for implementing this! ❤️

Hugging Face (Twitter) RT @julien_c: Super happy to announce that University of Zurich (@UZH_en) just joined @huggingface Academia Hub 🇨🇭🎉 Their students and educators get a better access to collaboration and compute features (including ZeroGPU power) on the Hub. 🔥

Hugging Face (Twitter) RT @Tu7uruu: Just dropped on HF — NeuTTS Air Next-gen on-device TTS that matches cloud-level quality while staying fully open source. > Real-time speech synthesis on CPU/GPU > 3-second voice cloning, no cloud or data upload > Compact: under 200 MB, runs on mobile and edge devices > Multilingual and expressive > Developed by @neuphonicspeech , optimized for speed and fidelity

Hugging Face (Twitter) RT @ClementDelangue: Open AI! huggingface.co/openai

Hugging Face (Twitter) RT @m_olbap: Super excited to finally post this interactive resource! We maintain 1M+ Python LOC across 400+ model architectures in 🤗 Transformers. How do we keep it controlled and keep shipping models? With @LysandreJik, @pcuenq and @yonigoz we wrote down what makes it possible. Dive here!

Hugging Face (Twitter) RT @zsakib_: When I say HuggingFace in my head I say it with a French accent dropping the H 🤗

Hugging Face (Twitter) RT @ClementDelangue: Nvidia B200s are now available in @huggingface Inference Endpoints! The world needs more compute 😅😅😅

Hugging Face (Twitter) RT @rohanpaul_ai: A 7B model, tuned for forms and docs, beats giant models at pulling structured data. Beats GPT-4.1 on 1,000 extraction tasks, trained for $196. The team generated synthetic training data that preserves memory across chunks of a long file. That memory lets the model connect names, dates, and values that appear far apart. They fine-tuned with Low Rank Adaptation, changing only 0.53% of weights. They then used Group Relative Policy Optimization with a semantic reward and strict JSON checks. This setup accepts different surface wording if the meaning matches. On 1,000 held-out tasks it hit 0.573 mean reward and 89% valid JSON, trained for $196, ahead of GPT-4.1 and others. Result, a small focused model can outperform general models and cost much less. ---- Paper – arxiv. org/abs/2509.22906 Paper Title: "Extract-0: A Specialized Language Model for Document Information Extraction"

Hugging Face (Twitter) RT @Tu7uruu: Just dropped on HF: kani-tts-370m A lightweight open-source text-to-speech model that sounds great and runs fast! > 370M parameters — efficient and deployable on consumer GPUs > NanoCodec + LFM2-350M > Natural & expressive voice trained with modern neural TTS techniques > Fast inference: real-time on a single RTX 3060

Hugging Face (Twitter) RT @jietang: Finally, our open-source GLM-4.6 are trending no. 1 on HF. Thanks to all for the support. We are working on the next version and stay tuned!

Hugging Face (Twitter) RT @arena: 🚨 New Top Open Model Update! A relative newcomer to the Arena, @zai_org's GLM-4.6 takes the clear, undisputed #1 spot for Top Open Model. 🏆 It also ranks #4 overall, which is not an easy feat! The next top open model, DeepSeek R1 0528, has been the standing champion for months, now trailing nine points behind. Congrats to the @zai_org team on this achievement! 🙌https://twitter.com/Zai_org/status/1973034639708344767#m