The Xet team at Hugging Face is working on improving the efficiency of the Hub’s storage architecture to make it easier and quicker for users to store and update data and models. As Hugging Face hosts nearly 11PB of    

The Hugging Face platform has many datasets and pre-trained models that make using and training state-of-the-art machine learning models increasingly accessible. However, it can be hard to scale AI tasks because AI datasets are often large (100s GBs to TBs) and using Hugging Face transformers for model inference can sometimes be computationally expensive. Dask, a Python library for distributed computing, can handle out-of-core computing (processing data that doesn’t fit in memory) by breaking datasets into manageable chunks. This makes it […]

We’ve been hard at work over the past few months, and we are excited to now announce the stable release of Gradio 5.  With Gradio 5, developers can build production-ready machine learning web applications that are performant, scalable, beautifully designed, accessible, and follow best web security practices, all in a few lines of Python. To give Gradio 5 a spin, simply type    

We audited Gradio 5 so that your machine learning apps are safe! In the past few years, Gradio (>6 million monthly Pypi installs) has become the default way to build machine learning web applications in Python. In just a    

AMD has just unveiled its 5th generation of server-grade EPYC CPU based on Zen5 architecture – also known as Turin. It provides a significant boost in performance, especially with a higher number of core count reaching up to 192    

Our friends at Unsloth shared an issue regarding gradient accumulation yesterday that is affecting the transformers Trainer. The initial report comes from @bnjmn_marie (kudos to him!). Gradient accumulation is supposed to be mathematically equivalent to full batch training; however, losses did not match between training runs where the setting was toggled on and off. Where does it stem from? Inside the modeling code of each model, transformers offers a “default” loss function that’s the most typically    

This is going to be the shortest blog post ever. Question: Llama 3.2 landed two weeks ago on Hugging Face / Transformers. When will it be available in Keras? Answer: It has been working from day 1 😀. There is nothing to wait for. Yes, Keras Llama3 can be loaded from any standard (i.e. safetensors) Hugging    

Speech-to-Speech (S2S) is an exciting new project from Hugging Face that combines several advanced models to create a seamless, almost magical experience: you speak, and the system responds with a synthesized voice. The project implements a cascaded pipeline leveraging models available through the Transformers library on the Hugging Face hub. The pipeline consists of the following components: Voice Activity Detection (VAD) Speech to Text (STT) Language Model (LM) Text to Speech (TTS) What’s more, S2S has multi-language support! It currently […]

Stable Diffusion 3.5 is the improved variant of its predecessor, Stable Diffusion 3. As of today, the models are available on the Hugging Face Hub and can be used with 🧨 Diffusers. The release comes with two checkpoints: A large (8B) model A large (8B) timestep-distilled model enabling few-step inference In this post, we will focus on how to use Stable Diffusion 3.5 (SD3.5) with Diffusers, covering both inference and training. Table Of Contents

After more than a year of development, we’re excited to announce the release of 🤗 Transformers.js v3! Highlights include: Installation You can get started by installing Transformers.js v3 from NPM using: npm i @huggingface/transformers Then, importing the library with