Welcome to Open Source AI: Run Your Own Models Locally https://t.co/XtkDdgeBOP
— Hugging Face (@huggingface) 25 juin 2026
Welcome to Open Source AI: Run Your Own Models Locally
@huggingface
-
Welcome to Open Source AI: Run Models Locally
By
–
-
Ideogram Releases Latest v4 Image Model with Open Weights
By
–
Ideogram just released their latest and best v4 image model open weights
— Hugging Face (@huggingface) 3 juin 2026
State of the art and open weights go well together 🤗
Model: https://t.co/DUcL7BBH7D
Demo: https://t.co/fIc26kF6Ky https://t.co/aw1S88Vx00 https://t.co/iD0FWyIgVsIdeogram just released their latest and best v4 image model open weights State of the art and open weights go well together Model: https://
huggingface.co/ideogram-ai/id
eogram-4-nf4
…
Demo: https://
huggingface.co/spaces/multimo
dalart/ideogram4
… -
Hugging Face Hub Reaches One Million Open Datasets Milestone
By
–
We've just hit 1M open datasets on the Hugging Face Hub 🎉
— Hugging Face (@huggingface) 12 mai 2026
Open models need open data. Today we hit that milestone, together with the most incredible community in AI! 🤗
Onwards to the next million 🚀 pic.twitter.com/PV6knP3XlJWe've just hit 1M open datasets on the Hugging Face Hub Open models need open data. Today we hit that milestone, together with the most incredible community in AI! Onwards to the next million
-
Converting 30k arXiv papers to Markdown using SOTA OCR
By
–
New blog post: converting 30k @arxiv papers to Markdown using SOTA OCR models to enable chat with paper functionality
— Niels Rogge (@NielsRogge) 7 avril 2026
Includes:
> leveraging an open OCR model (Chandra 2 by @datalabto)
> running on GPU infra – @huggingface Jobs
> using Codex with a SKILL.md pic.twitter.com/jrpin9oq5uNew blog post: converting 30k @arxiv papers to Markdown using SOTA OCR models to enable chat with paper functionality Includes: > leveraging an open OCR model (Chandra 2 by @datalabto) > running on GPU infra – @huggingface Jobs > using Codex with a SKILL.md
-
Microsoft Open Sources Harrier: Top Multilingual Embedding Model
By
–
I’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard.
— Jordi Ribas (@JordiRib1) 7 avril 2026
Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across… pic.twitter.com/asMVydbijhI’m pleased to share that our search team has open sourced an embedding model called Harrier that is currently ranking #1 on the multilingual MTEB-v2 benchmark leaderboard. Harrier delivers SOTA performance on retrieval quality, semantic matching, and contextual analysis across workloads, supporting more than 100 languages and handles long inputs up to 32K. It is built for the next generation semantic search for Bing and our web grounding (RAG) service for AI agents, which already powers nearly every major AI chatbot today. As you can see in the leadership board, our Harrier model is currently ahead of other excellent models based on Gemini, Gemma, Llama, Qwen, and more. I’m grateful for the hard work of our team to get to this top ranking, and I’m excited to see all the healthy competition in the space, which should ultimately lead to more innovations that will benefit everyone. Learn more: msft.it/6019QNB0b
-
OpenMed dataset added to Hugging Face platform
By
–
just added openmed data on @huggingface, what else
-
Hugging Face and SAIR Foundation Collaborate on AI Science Competitions
By
–
We’re excited to announce our collaboration with @huggingface. Through SAIR competitions, we aim to provide open data, benchmarks, tools, and models, and expand the frontier of AI x Science through collective contributions from the community.
— SAIR (@SAIRfoundation) 6 avril 2026
SAIR on Hugging Face:… pic.twitter.com/FfhDeJBdQYWe’re excited to announce our collaboration with @huggingface. Through SAIR competitions, we aim to provide open data, benchmarks, tools, and models, and expand the frontier of AI x Science through collective contributions from the community. SAIR on Hugging Face: huggingface.co/SAIRfoundatio…
→ View original post on X — @huggingface, 2026-04-06 15:56 UTC
-
Building Open-Source Frontier Agents Dataset Initiative
By
–
RT @ClementDelangue: We keep saying we want open-source frontier agents. Fine. Then let’s build the dataset. @badlogicgames, creator of P…
→ View original post on X — @huggingface, 2026-04-06 15:37 UTC
-
Gemma 4 Blog: Comprehensive Guide with Inference and Fine-tuning
By
–
Just now reading through the Gemma 4 blog Safe to say the @huggingface team is goated Lots of usage examples, guides on inference and fine-tuning, highly recommend! huggingface.co/blog/gemma4
→ View original post on X — @huggingface, 2026-04-06 11:35 UTC
-
TurboQuant-GPU: 5x KV Cache Compression for Any GPU
By
–
pip install turboquant-gpu 5.02x KV cache compression for ANY GPU (RTX, H100, A100, B200) – works over @huggingface transformers – dead-simple API: compress + generate in 3 lines – 3-bit Lloyd-Max fused KV compression (0.98 cosine similarity) – outperforms MXFP4 (3.76x) and NVFP4 (3.56x) on compression Ran Mistral-7B: 1,408 KB → 275 KB KV cache (5.02x) Quickstart: github.com/DevTechJr/turboqu… Written in cuTile (CUDA 12, 13) with PyTorch fallbacks
→ View original post on X — @huggingface, 2026-04-05 19:30 UTC