✷ The archive · 11 labs · 3,062 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.
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.
OpenAI Video PreTraining (VPT) for Minecraft
OpenAI introduced Video PreTraining (VPT), a semi-supervised imitation learning method that enables agents to learn complex behaviors in Minecraft by training on massive unlabeled video datasets of human play.
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.
OpenAI Evolution through Large Models (ELM) Research
OpenAI introduces Evolution through Large Models (ELM), a method that uses LLMs to improve genetic programming by approximating human-like code mutations to generate functional programs in novel domains.
Anthropic Softmax Linear Units (SoLU) Research
Anthropic introduces Softmax Linear Units (SoLU), an architectural change to MLP activation functions that increases the fraction of interpretable neurons without sacrificing model performance.
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.
AI-written critiques help humans notice flaws
OpenAI research demonstrates that AI-generated critiques help human evaluators identify significantly more flaws in summaries, suggesting a scalable path for human supervision of complex AI tasks.
OpenAI Techniques for Training Large Neural Networks
OpenAI outlines the primary parallelism strategies—data, pipeline, tensor, and expert parallelism—and memory-saving techniques used to orchestrate GPU clusters for training massive AI models.
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.
OpenAI, Cohere, and AI21 Labs Best Practices for Deploying Language Models
OpenAI, Cohere, and AI21 Labs have released a joint set of preliminary best practices for the responsible development and deployment of large language models to mitigate risks and prevent misuse.
OpenAI Research: Teaching Models to Express Their Uncertainty in Words
OpenAI researchers demonstrated that GPT-3 can be trained to express calibrated uncertainty about its answers in natural language without relying on model logits.
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.
OpenAI Codex Applications and Integration
OpenAI Codex is a natural language-to-code system based on GPT-3 that powers over 70 applications, including GitHub Copilot, to increase developer productivity by converting English instructions into code.
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.
Anthropic Research: Scaling Laws and Interpretability of Learning from Repeated Data
Anthropic researchers found that repeating a small fraction of training data can severely degrade LLM performance by consuming model capacity for memorization and damaging generalization structures like induction heads.
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.
OpenAI DALL·E 2 Research Preview Update
OpenAI is expanding access to DALL·E 2 to waitlisted users at a rate of 1,000 people per week following a limited research preview that generated over 3 million images.
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.
OpenAI Leadership Team Update
OpenAI announced executive role changes for Greg Brockman, Brad Lightcap, Mira Murati, and Chris Clark to align leadership with the company's growth and strategic milestones.
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.
Anthropic Series B Funding for AI Safety and Research
Anthropic has raised $580 million in Series B funding to build large-scale experimental infrastructure aimed at improving the steerability, interpretability, and robustness of computationally intensive AI models.
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.
Getting Started with Transformers on Habana Gaudi
Hugging Face and Habana Labs have partnered to accelerate Transformer model training, offering up to 40% better price-performance on Habana Gaudi accelerators compared to GPU-based Amazon EC2 instances.
Supercharging Customer Service with Machine Learning
Hugging Face demonstrates how to automate the filtering of unsatisfied customer feedback using a fine-tuned DeBERTa-v3-base model and the Transformers and Datasets libraries.
Hugging Face Education Initiative 2022 Launch
Hugging Face announced a multi‑track education program in April 2022 aimed at teaching machine learning to 5 million people by the end of 2023, providing free resources for learners, instructors, and the broader public.
Hugging Face Hub Carbon Emissions Tracking
Hugging Face has introduced tools to track, report, and filter machine learning models based on their CO2 emissions to improve environmental transparency in AI.
Measuring Goodhart's Law in AI Alignment
OpenAI explores how optimizing proxy objectives in AI models can lead to a decline in true objective performance, a phenomenon known as Goodhart's Law, and proposes best-of-n sampling as a method for analysis.
Hierarchical Text-Conditional Image Generation with CLIP Latents
OpenAI introduces a two-stage generative model that uses a prior to create CLIP image embeddings from text and a decoder to generate images from those embeddings, improving diversity and enabling zero-shot image manipulation.
Lewis Tunstall interview – Hugging Face Machine Learning Experts
Hugging Face announced a detailed interview with Machine Learning Engineer Lewis Tunstall, highlighting his work on the Transformers library, the new “NLP with Transformers” book, and the expanding Hugging Face Course.
Habana Labs and Hugging Face Partnership for Transformer Model Training
Habana Labs and Hugging Face have integrated Habana's SynapseAI software suite with the Hugging Face Optimum library to accelerate transformer model training on Habana Gaudi processors.
Anthropic: Training a Helpful and Harmless Assistant with RLHF
Anthropic demonstrates that Reinforcement Learning from Human Feedback (RLHF) improves language model performance across most NLP evaluations while ensuring the assistant remains helpful and harmless.
Hugging Face Transformers Design Philosophy
Hugging Face utilizes a "single model file" policy for the Transformers library, intentionally eschewing the DRY (Don't Repeat Yourself) principle to prioritize readability, community contributions, and the stability of static ML models.
Decision Transformer Integration in Hugging Face Transformers
Hugging Face has integrated the Decision Transformer, an offline reinforcement learning method that treats RL as a conditional-sequence modeling problem, into the transformers library and Hugging Face Hub.