Uploaded a dataset of 115,293 illustrated pages from the Encyclopaedia Britannica, 1st edition (1768) to 14th (1929) to the Hub huggingface.co/datasets/bigl…

Decision models now run on device in llama.cpp. Free, fast, private! llama serve -hf ggml-org/Kev-4B-GGUF

New in @huggingface Jobs docs: a Train Models page. A quick overview for you or your agent, covering Transformers, TRL, @UnslothAI, @axolotl_ai and Diffusers. Each example runs in minutes on a single A10G for around $0.10. huggingface.co/docs/hub/jobs…

I've been training OCR models for 2 years. The progress we've made in handwriting is astonishing - from barely readable to almost perfect transcription.

Hugging Face Jobs usage is stonking 📈 All thanks to agents: you just need the hf CLI installed and you have access to an insane amount of hardware. Asked mine to test hundreds of code snippets from Hub model pages: it launched 322 Jobs in 90 minutes, up to 25 at once, CPU to A100 (diffusers, transformers, sklearn, MLX, Keras...) Total bill: ~$4 🤯

1M trainings/month with TRL. 🤯 That’s what our telemetry is showing today. Next stop: 1M/week. 👀 Every model you post-train, is one more step toward a less centralized AI ecosystem. Let’s go! ✊

Announcing d1, our first decision model. It's the first model to outperform Jev on @huggingface's Decision Index. > wins on multilingual evals > more robust against prompt injection > handles longer inputs more effectively > built for fast, structured decision-making in software environments > Liquid API: console.liquid.ai > Available on OpenRouter soon

Shipped datatrove 0.10.1 🛠️ Mostly a bug-fix release plus CI and testing work (Hypothesis property tests, faster CI). github.com/huggingface/datat…

Who wants more speech training data? YODAS v3 is now available @huggingface It’s 1.1M hours - the biggest audio dataset ever. And the first at this scale with stereo audio at 48kHz, with timestamped transcripts and translations. 100+ langs hf.co/blog/espnet/yodasv3 CC-BY-3.0

Hugging Face is where much of the machine learning community publishes and finds its datasets, while DuckDB is the in-process analytical database that queries files like CSV and Parquet directly, with no server or warehouse to install or run. 🤗 🦆 Did you know that, since #DuckDB v0.10.3 (~May 2024), you can point a SELECT at a dataset on the Hugging Face Hub⁠, using the DuckDB `hf://` protocol, and query it, without downloading it first? This blog post covers how that integration works and the use cases it fits: duckdb.org/2026/09/25/huggin…

The Hub can be a firehose of new releases. I used @fastinoAI's GLiNER2.5-Decide to build a Space that tags every new model, dataset and Daily Paper on @huggingface into 48 topics, usually a minute or two after it changes. Runs on one CPU, no LLM. huggingface.co/spaces/davans…

Can now fine-tune an even better starter model using this recipe!

Introducing GLiNER2.5-Decide, our new 340M parameter open weight, encoder-based decision model. GLiNER2.5-Decide is built for fast, deterministic classification. The model evaluates a set of user-defined typed questions and rules, and jointly decodes their answers, returning structured decisions with probability distributions and confidence scores. We evaluated the model’s performance on Fast Decisions, an unseen, internally generated classification suite based on 17 datasets testing real-world use cases across routing, triage, classification, sentiment, and content understanding. Measured against similar decision models, GLiNER2.5-Decide leads in 9 of the 17 datasets, achieving the highest average score: - GLiNER2.5-Decide: 60.1% - SemIf: 56.4% - JevK5: 57.5% - Laya: 46.6% This performance makes the model a strong fit for use cases like tool calling, model routing, browser and computer use, and LLM-as-a-judge. GLiNER2.5-Decide’s lightweight encoder architecture makes it easy to fine-tune the model for specific tasks, while being efficient enough to run locally on consumer-grade CPUs or in air-gapped environments, giving users greater control over where their data goes and where the model runs. To make building and experimenting with GLiNER2.5-Decide as easy as possible, we're also offering hosted inference. You can now use our API to run inference and fine-tune GLiNER models on specialized tasks right inside your own coding agent: agent.fastino.ai As with previous models, we’re also releasing the model weights on @huggingface under the Apache 2.0 license: huggingface.co/fastino/GLiNE…

huggignface of robotics is huggingface we've just added lerobot episodes player on all lerobot datasets

huggingface_hub v1.33.0 is out 🤗 📤 Large Xet uploads: new Validating progress bar 🧩 hf skills add now covers every agent (Claude Code, Codex, Cursor…) 🐛 +12 bug fixes pip install -U huggingface_hub 🎬 Video made from the release notes… and yes, it's Opus 5.5-generated

It's very cool to see all the open Jev replications, but imo people aren't focusing enough on the not-so-secret sauce (the data). I made a @huggingface collection of some datasets that can be useful for Jev training and eval. huggingface.co/collections/d…