Hugging Face Introduces DOI Support for Models and Datasets
Hugging Face now lets users generate Digital Object Identifiers (DOIs) for Hub models and datasets, providing permanent, citable links that persist across versions.
Japanese Stable Diffusion release by rinna
rinna released Japanese Stable Diffusion, a Japanese‑language fine‑tuned version of Stable Diffusion that generates culturally appropriate images from Japanese prompts.
Hugging Face Zero-Shot Evaluation on the Hub
Hugging Face has introduced zero-shot evaluation for causal language models on the Hub, enabling users to benchmark models up to 66 billion parameters without writing code.
Hugging Face AutoTrain Image Classification
Hugging Face has added Image Classification to AutoTrain, enabling users to train custom image categorization models without writing code or configuring hyperparameters.
Hugging Face Accelerate: Running Large Models with PyTorch
Hugging Face Accelerate enables the execution of massive AI models on consumer hardware by leveraging PyTorch's meta device and sharded checkpoints to manage memory across GPUs, CPU RAM, and disk.
SetFit: Efficient Few-Shot Learning Without Prompts
Hugging Face introduces SetFit, a prompt-free framework for few-shot fine-tuning of Sentence Transformers that achieves high accuracy with minimal labeled data.
Hugging Face Ethics and Society Newsletter #1
Hugging Face introduces its Ethics and Society newsletter and outlines a decentralized, value-driven approach to operationalizing AI ethics through collaboration, transparency, and responsibility.
Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate
Hugging Face demonstrates sub‑millisecond per‑token generation for the 176B‑parameter BLOOM model using DeepSpeed‑Inference tensor parallelism and Accelerate pipeline parallelism on 8×80 GB A100 GPUs.
Diffusers 0.3 release adds image‑to‑image, textual inversion, inpainting, GPU optimizations, Mac MPS, ONNX support and new docs
Hugging Face announced Diffusers version 0.3, introducing image‑to‑image, textual inversion, experimental inpainting, smaller‑GPU optimizations, Mac MPS support, an ONNX exporter, expanded documentation, and a wave of community projects.
Training Decision Transformers for Offline Reinforcement Learning
Hugging Face provides a guide and implementation for training an offline Decision Transformer from scratch to solve the HalfCheetah environment using the transformers Trainer and a custom data collator.
Training Language Models with Megatron-LM
Hugging Face provides a guide on using NVIDIA's Megatron-LM framework to efficiently pre-train large language models on GPUs, including integration with the Transformers library.
OpenRAIL: Towards open and responsible AI licensing frameworks
Hugging Face announced OpenRAIL, a set of AI‑specific licenses that combine open access with use‑based restrictions to promote responsible deployment of machine‑learning models.
Visualize proteins on Hugging Face Spaces
Hugging Face provides a guide on integrating 3Dmol.js into Hugging Face Spaces via Gradio to enable 3D protein structure visualization in the browser.
Pre-training BERT with Hugging Face Transformers and Habana Gaudi
Hugging Face demonstrates how to pre-train BERT-base from scratch using Habana Gaudi DL1 instances on AWS, achieving a 25% cost reduction compared to NVIDIA V100 GPU-based training.
Stable Diffusion with Diffusers
Hugging Face announced how to run the text-to-image Stable Diffusion model using the Diffusers library, providing code, licensing details, and explanations of the model’s latent diffusion architecture.
Deploying Hugging Face ViT on Vertex AI
Hugging Face demonstrates how to deploy a Vision Transformer (ViT) model on Google Cloud's Vertex AI platform to achieve Kubernetes-level scalability with significantly less code.
Vision Transformers on Hugging Face Optimum Graphcore
Hugging Face and Graphcore enable the efficient fine-tuning of Vision Transformer (ViT) models on Intelligence Processing Units (IPUs) using the Optimum library, demonstrated through a multi-label chest X-ray classification task.
Hugging Face integrates LLM.int8() 8-bit matrix multiplication into Transformers and Accelerate
Hugging Face released an integration of the LLM.int8() 8-bit quantization technique into the Transformers and Accelerate libraries, enabling inference of very large language models with no performance degradation and up to 2× memory savings.
Hugging Face's TensorFlow Philosophy
Hugging Face outlines its technical approach to TensorFlow integration, emphasizing deep alignment with Keras and XLA to optimize performance and deployment for transformer models.
Skops library enables scikit-learn model hosting, documentation, and collaboration on Hugging Face Hub
Hugging Face announced Skops, a library that lets users package, document, and push scikit-learn models to the Hub with automatic model cards and inference widgets.
Deploying Hugging Face ViT on Kubernetes with TF Serving
Hugging Face provides a technical guide on scaling Vision Transformer (ViT) deployments using Docker and Kubernetes with TensorFlow Serving to enable production-grade autoscaling and resource management.
Hugging Face Sentence Transformers Training Guide (historical reference)
Hugging Face announced an outdated tutorial for training and fine-tuning Sentence Transformers models, explaining model architecture, dataset formats, loss functions, and how to upload models, while directing users to newer training APIs.
Hugging Face Deep RL Class Unit 8: Proximal Policy Optimization (PPO) Explained
Hugging Face introduced a detailed walkthrough of Proximal Policy Optimization (PPO), showing how clipping the policy‑update ratio stabilizes training and providing a from‑scratch PyTorch implementation tested on CartPole‑v1 and LunarLander‑v2.
Hugging Face Private Hub Introduction
Hugging Face introduced the Private Hub (now Enterprise Hub), a secure, compliant environment for companies to collaborate on machine learning models, datasets, and spaces using a unified toolset.
Nyströmformer: Approximating self-attention in linear time and memory via the Nyström method
Nyströmformer reduces the time and memory complexity of standard self-attention from O(n^2) to O(n) by using the Nyström method to approximate the softmax matrix via sampled landmarks.
Hugging Face Recommendations for the U.S. National AI Research Resource
Hugging Face submitted a response to the White House and National Science Foundation advocating for the National AI Research Resource (NAIRR) to prioritize ethical expertise, standardized documentation, and accessible tooling to democratize AI research.
Hugging Face Datasets Audio and Vision Documentation Update
Hugging Face has expanded the 🤗 Datasets library documentation to include dedicated guides and tools for audio and vision datasets, simplifying the process of loading and processing multi-modal data.
Faster Text Generation with TensorFlow and XLA
Hugging Face has enabled XLA compilation for text generation in the transformers library using TensorFlow, resulting in speedups of up to 100x and performance that often exceeds PyTorch.
Deploying TensorFlow Vision Models in Hugging Face with TF Serving
Hugging Face provides a guide on deploying TensorFlow vision models, such as the Vision Transformer (ViT), using TensorFlow Serving to expose them as REST or gRPC endpoints.
Advantage Actor Critic (A2C) Explained
Hugging Face explains Advantage Actor Critic (A2C), a hybrid reinforcement learning architecture that reduces variance in policy gradient estimation by combining policy-based and value-based methods.
Dynamic Adversarial Data Collection (DADC) for MNIST – Hugging Face tutorial
Hugging Face announced a step‑by‑step tutorial for Dynamic Adversarial Data Collection (DADC) on MNIST, showing how to gather human‑generated adversarial examples and retrain models to improve robustness.
BLOOM 176B Training Technology Overview
Hugging Face released a detailed breakdown of how the 176‑billion‑parameter BLOOM model was trained using 384 A100 GPUs, Megatron‑DeepSpeed 3D parallelism, and BF16 mixed‑precision, highlighting the hardware, software stack, and engineering challenges.
Building a Playlist Generator with Sentence Transformers
Hugging Face demonstrates how to create a semantic search-based playlist generator using Sentence Transformers for lyric embeddings and Gradio Blocks for the user interface.
BLOOM: The World's Largest Open Multilingual Language Model
Hugging Face and the BigScience project have released BLOOM, a 176-billion parameter open-access multilingual language model trained in complete transparency across 46 natural languages and 13 programming languages.
Getting Started with Sentiment Analysis on Twitter
Hugging Face provides a guide on using the Inference API to perform automated sentiment analysis on Twitter data for both developers and non-coders.
Policy Gradient with PyTorch – Hugging Face Deep RL Class Unit 5
Hugging Face released a tutorial that walks through implementing the REINFORCE Monte‑Carlo policy‑gradient algorithm from scratch in PyTorch and evaluates it on CartPole‑v1, PixelCopter, and Pong.
Getting Started with Machine Learning using Sentence Transformers
Hugging Face provides a guide for beginners to start their first ML project using the Sentence Transformers library to create embeddings and perform semantic search.
Hugging Face Evaluation on the Hub
Hugging Face has introduced Evaluation on the Hub, a no-code tool powered by AutoTrain that allows users to evaluate any model on any dataset using any metric directly on the Hugging Face Hub.
Accelerate Large Model Training using DeepSpeed
Hugging Face demonstrates how to use the Accelerate library to integrate DeepSpeed's ZeRO optimizations, enabling larger batch sizes and the training of models that would otherwise exceed GPU memory.
Getting Started With Embeddings – Hugging Face tutorial
Hugging Face announced a step‑by‑step guide for creating, hosting, and querying vector embeddings using the Sentence‑Transformers library and the Hugging Face Hub, demonstrating a searchable FAQ system.
Convert Transformers to ONNX with Hugging Face Optimum
Hugging Face provides three methods to convert Transformers models to ONNX, ranging from the low-level torch.onnx API to the high-level Optimum library.
Intel and Hugging Face Partnership for Machine Learning Hardware Acceleration
Intel has joined the Hugging Face Hardware Partner Program to develop the Optimum Intel library, enabling streamlined hardware acceleration for training, fine-tuning, and inference of Transformer models on Intel platforms.
Hugging Face Director of Machine Learning Insights: Finance Edition
Experts from U.S. Bank, RBC, and Moody's Analytics discuss how machine learning is transforming finance through fraud detection and personalized products while navigating challenges like legacy systems and regulatory requirements.
Deep Q-Learning with Space Invaders
Hugging Face explains Deep Q-Learning (DQN), a reinforcement learning method that uses neural networks to approximate Q-values for high-dimensional state spaces like Atari games.
Annotated Diffusion Model – detailed walkthrough of DDPM implementation
Hugging Face released an annotated, step‑by‑step PyTorch implementation of Denoising Diffusion Probabilistic Models (DDPM), showing how to build, train, and sample from a diffusion model for image generation.
Graphcore and Hugging Face Expand IPU-Ready Transformer Models
Graphcore and Hugging Face have expanded the Hugging Face Optimum library to include 10 optimized transformer models across NLP, speech, and computer vision for Graphcore IPUs.
Hugging Face Hub Community Update: Pull Requests and Discussions
Hugging Face has introduced pull requests and discussions to the Hub, enabling community-wide collaboration on models, datasets, and Spaces without the need for repository forks.
TAPEX: Efficient Table Pre-training via Neural SQL Execution
TAPEX is a table pre-training approach that uses synthetic SQL execution pairs to achieve state-of-the-art results on table question answering and fact verification with significantly higher efficiency than previous methods.
Hugging Face Introduction to Q-Learning
Hugging Face provides a technical guide to Q-Learning, an off-policy value-based reinforcement learning method that uses a Q-table to determine optimal state-action pairs.
Hugging Face Multimodal Project Ethical Charter
Hugging Face has established an ethical charter for its multimodal learning project to integrate ethical principles, transparency, and fairness directly into the machine learning research lifecycle.