✷ 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.
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.
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.
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.
Machine Learning Experts: An Interview with Margaret Mitchell
Dr. Margaret Mitchell discusses the critical role of ethical AI, the development of Model Cards for transparency, and the necessity of diversity and inclusion in machine learning development.
Hugging Face AI Research Residency Program Announcement
Hugging Face has launched a 9-month AI Research Residency Program designed to help aspiring researchers develop machine learning techniques and publish open-source work alongside the Hugging Face Science Team.
Fine-Tuning SegFormer for Semantic Segmentation with Custom Datasets
Hugging Face provides a technical guide on fine-tuning the SegFormer model using the Transformers library to perform pixel-level image classification for custom use cases like autonomous sidewalk navigation.
Image search with Hugging Face datasets
Hugging Face demonstrates how to build an image search application by combining the datasets library, CLIP embeddings, and FAISS for efficient similarity search.
Accelerate BERT Inference with Hugging Face Transformers and AWS Inferentia
Hugging Face provides a guide on using AWS Inferentia and the Neuron SDK to accelerate BERT inference, achieving 5-6ms latency on Amazon SageMaker.
Guiding Text Generation with Constrained Beam Search in Hugging Face Transformers
Hugging Face introduced constrained beam search to the Transformers library, allowing users to force specific words or phrases into generated text while maintaining linguistic coherence.
BERT 101: Understanding the Bidirectional Encoder Representations from Transformers
BERT is a bidirectional Transformer-based model developed by Google AI Language that achieves state-of-the-art performance across 11+ common NLP tasks by leveraging unsupervised pre-training on massive datasets.
Fine-Tuning Vision Transformer (ViT) for Image Classification with Hugging Face Transformers
Hugging Face provides a guide on fine-tuning the Vision Transformer (ViT) model for image classification using the datasets and transformers libraries, demonstrating the process with the beans dataset.
Getting Started with Sentiment Analysis using Python
Hugging Face provides a comprehensive guide on implementing sentiment analysis using Python, leveraging pre-trained models from the Hugging Face Hub, the Trainer API for fine-tuning, and the no-code AutoNLP tool.
Automatic Speech Recognition for Large Files with Wav2Vec2 in Transformers
Hugging Face implements a striding technique leveraging the Connectionist Temporal Classification (CTC) architecture of Wav2Vec2 to enable high-quality ASR on arbitrarily long audio files and live inference.
Hugging Face Hub Search API Updates
Hugging Face has introduced new programmatic search features to the huggingface_hub library, including ModelSearchArguments and ModelFilter, to simplify how users find models and datasets without leaving their IDE.
Stable-Baselines3 Integration with Hugging Face Hub
Hugging Face has integrated Stable-Baselines3, allowing users to host, share, and load PyTorch Deep Reinforcement Learning models directly from the Hugging Face Hub.
Hugging Face Infinity CPU Performance Case Study
Hugging Face Infinity achieves up to 800% higher throughput and millisecond latency on Intel Ice Lake Xeon CPUs compared to vanilla Transformers, enabling cost-effective, real-time Transformer deployments on CPU infrastructure.
Boosting Wav2Vec2 with n-grams in Hugging Face Transformers
Hugging Face has integrated the pyctcdecode library into the Transformers library, enabling Wav2Vec2 models to be boosted with n-gram language models to significantly reduce spelling errors and Word Error Rate (WER).
Deploying GPT-J 6B on Amazon SageMaker with Hugging Face Transformers
Hugging Face provides a method to deploy EleutherAI's GPT-J 6B model on Amazon SageMaker, reducing model load times from over three minutes to under eight seconds using torch.save.
Active Learning with AutoNLP and Prodigy
Hugging Face demonstrates how to build an active learning pipeline for Named Entity Recognition (NER) by combining the AutoNLP automated training framework with the Prodigy annotation tool.
Hugging Face Acquires Gradio
Hugging Face has acquired Gradio to integrate easy-to-build machine learning demos and GUIs into its ecosystem, expanding ML accessibility to non-technical users.
Perceiver IO: A Scalable, Fully-Attentional Model for Any Modality
Perceiver IO is a Transformer-based architecture that decouples compute from input size by using a latent space, enabling it to process text, images, audio, video, and point clouds without quadratic scaling issues.
Training CodeParrot from Scratch
Hugging Face introduces CodeParrot, a GPT-2 based model trained from scratch on a cleaned dataset of 20 million Python files to enable Python code auto-completion.
Hugging Face Snowball Fight ML-Agents Environment
Hugging Face has released Snowball Fight 1vs1, its first custom Deep Reinforcement Learning environment built with Unity ML-Agents and hosted on Hugging Face Spaces.
Hugging Face Optimum for Graphcore IPU Integration
Hugging Face has integrated the Optimum library with Graphcore Intelligence Processing Units (IPUs) to accelerate Transformer models, starting with an optimized BERT implementation.
Hugging Face Data Measurements Tool
Hugging Face has released the Data Measurements Tool, an open-source Python library and no-code interface designed to help developers analyze, curate, and compare ML datasets for more responsible AI development.
Accelerating PyTorch Distributed Fine-Tuning with Intel Technologies
Hugging Face demonstrates how to accelerate PyTorch training by distributing fine-tuning jobs across a cluster of Intel Xeon Scalable CPU servers using the Intel extension for PyTorch and oneCCL.
Fine-Tuning XLS-R for Low-Resource Automatic Speech Recognition
Hugging Face provides a technical guide on fine-tuning XLS-R, a cross-lingual speech representation model, for low-resource Automatic Speech Recognition (ASR) using the Transformers library.
Scaling up BERT-like model Inference on modern CPU - Part 2
Hugging Face explores software-level optimizations for BERT-like models on Intel Ice Lake Xeon CPUs, demonstrating how memory allocators, parallelization libraries, and Bayesian optimization can significantly reduce inference latency.
Hugging Face Course Part 2 and Community Event Launch
Hugging Face announced the release of Part 2 of the Hugging Face Course on November 15, 2021, accompanied by a community event featuring technical talks and hands-on projects.
Large Language Models: A New Moore's Law?
Hugging Face critiques the trend of exponentially increasing model sizes, such as the 530B parameter Megatron-Turing NLG, and advocates for pragmatic, efficient alternatives like distillation and fine-tuning.
Hugging Face Sentence Embedding Models with 1B Training Pairs
Hugging Face developed state-of-the-art general-purpose sentence embedding models by training on up to 1 billion sentence pairs using JAX/Flax and TPU infrastructure.
Hugging Face: The Age of Machine Learning As Code
Hugging Face advocates for treating machine learning as a software engineering discipline by adopting MLOps, leveraging the general-purpose Transformer architecture, and prioritizing production deployment over sandbox experiments.
Fine-tuning CLIP for Remote Sensing and Satellite Imagery
A team of researchers fine-tuned OpenAI's CLIP model using the RSICD dataset and other satellite imagery to significantly improve text-to-image retrieval for remote sensing applications.
Hugging Face Spaces and Gradio Integration
Hugging Face has integrated Gradio into Spaces, allowing users to easily host and showcase machine learning model demos using the Inference API or custom model checkpoints.
Hosting Models and Datasets on Hugging Face Spaces using Streamlit
Hugging Face Spaces integrates with Streamlit to allow users to quickly build and host interactive demos for machine learning models and data visualizations.
Hugging Face Summer 2021 Update
Hugging Face announced a series of Hub enhancements, including the Spaces Beta for ML demo hosting, TensorBoard integration, and the launch of the Optimum library for hardware acceleration.
Hugging Face Optimum Release
Hugging Face has introduced Optimum, an open-source toolkit designed to optimize Transformer models for production performance across various hardware platforms.
Hugging Face and Graphcore Partnership for IPU-Optimized Transformers
Hugging Face and Graphcore have partnered to integrate Intelligence Processing Units (IPUs) into the Hugging Face ecosystem via the Hardware Partner Program and the Optimum library to accelerate Transformer model deployment.
Hugging Face DeDLOC: Collaborative Training of Language Models over the Internet
Hugging Face introduces DeDLOC, a distributed training method that enables volunteers to collaboratively pretrain large language models over the internet by adapting to varying network and hardware constraints.
spaCy Integration with Hugging Face Hub
Hugging Face has integrated spaCy into the Hugging Face Hub, allowing users to share, discover, and deploy spaCy pipelines via a unified platform.
Deploy Hugging Face models easily with Amazon SageMaker
Hugging Face and Amazon SageMaker have introduced new Inference Deep Learning Containers (DLCs) and an Inference Toolkit to simplify the deployment of Transformer models to production-ready endpoints.
Sentence Transformers Integration in the Hugging Face Hub
Hugging Face has integrated Sentence Transformers into the Hub, providing over 90 pretrained models for 100+ languages and new interactive widgets for feature extraction and sentence similarity.
Few-Shot Learning with GPT-Neo and Hugging Face Accelerated Inference API
Hugging Face explores the application of few-shot learning using the open-source GPT-Neo model and the Accelerated Inference API to enable task generalization without extensive labeled data.
Using & Mixing Hugging Face Models with Gradio 2.0
Gradio 2.0 enables machine learning developers to load and deploy Hugging Face models as GUIs with a single line of code, supporting both parallel and serial model composition.
Scaling-up BERT Inference on CPU (Part 1)
Hugging Face explores hardware-level optimizations for BERT inference on modern CPUs, demonstrating that throughput can be scaled linearly by using multiple independent model instances bound to specific physical cores via NUMA-aware affinity.