✷ The archive · 11 labs · 871 dispatches
The labs
No more opening a dozen official blogs every morning. First-hand releases from OpenAI, Anthropic, DeepMind and the rest, each with its substance pulled out.
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Hugging Face Sentence Transformers Training Guide (historical reference)
Hugging Face announced an outdated tutorial for training and fine-tuning Sentence Transformers models, explaining model architecture, dataset formats, loss functions, and how to upload models,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.
Sempre Health Case Study: Accelerating NLP Pipelines with Hugging Face Expert Acceleration Program
Sempre Health utilized the Hugging Face Expert Acceleration Program to deploy an NLP pipeline that automatically handles over 20% of inbound patient messages, significantly improving business scalability.
Hugging Face Deep RL Course Unit 2 Part 1: Introduction to Q-Learning Concepts
Hugging Face released the first part of its Deep Reinforcement Learning course, explaining value‑based methods, the Bellman equation, and Monte‑Carlo vs. Temporal‑Difference learning as groundwork for Q‑Learning.
Hugging Face Fellowship Program Announcement
Hugging Face has launched the Fellowship Program to support and empower key contributors to the open-source machine learning ecosystem through resources, recognition, and tailored support.
Machine Learning Experts Interview with Sasha Luccioni – Highlights on Ethical AI and Climate Impact
Hugging Face interviewed research scientist Sasha Luccioni, who discussed her work on ethical AI, carbon‑footprint measurement, and applying machine learning to climate and societal challenges.
Gradio 3.0 Release Notes
Hugging Face has released Gradio 3.0, featuring a complete frontend redesign and the introduction of Gradio Blocks for building complex, custom ML web apps in Python.
Hugging Face Student Ambassador Program 2022
Hugging Face has launched its first Student Ambassador Program to help achieve its goal of teaching machine learning to 5 million people by 2023.
Hugging Face Director of Machine Learning Insights: SaaS Edition
Hugging Face interviews four ML directors from Salesforce, Amplitude, ZoomInfo, and Repustate to discuss the practical challenges, common pitfalls, and future opportunities of integrating machine learning into SaaS products.
Optimum 1.2 Inference Release Adds ONNX Runtime Accelerated Pipelines
Hugging Face announced Optimum 1.2, which adds inference support and Transformers pipeline integration via ONNX Runtime, enabling faster, quantized, and optimized model serving.
Hugging Face Series C Funding Announcement
Hugging Face raised $100 million in Series C funding to accelerate the democratization of AI through open-source research, products, and collaborative machine learning.
fastai Integration with Hugging Face Hub
Hugging Face has integrated the fastai library, allowing practitioners to upload and download fastai Learner objects directly to and from the Hugging Face Hub using a single line of Python.
Hugging Face Deep Reinforcement Learning Introduction
Hugging Face released an introductory tutorial for a free Deep Reinforcement Learning class, covering core RL concepts, the RL framework, exploration‑exploitation trade‑off, policy‑based vs value‑based methods, and how deep neural networks integrate into RL.
Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel
Hugging Face integrates PyTorch Fully Sharded Data Parallel (FSDP) into the Accelerate library, enabling the training of massive models by sharding optimizer states, gradients, and parameters across GPUs with optional CPU offloading.
Opinion Classification with Kili and HuggingFace AutoTrain
Hugging Face demonstrates how to build an active learning pipeline for text classification using Kili for data annotation and AutoTrain for automated model training.
Hugging Face Machine Learning Director Insights – Industry Perspectives
Hugging Face published a series of interviews with six Machine Learning Directors who share how ML is transforming media, pharmaceuticals, science, logistics, marketing, and energy, while highlighting common challenges and future opportunities.