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.
Hugging Face Accelerate Library Release
Hugging Face has released Accelerate, a PyTorch library that allows users to run raw training scripts on any device configuration, including multi-GPU and TPU, without rewriting boilerplate code.
Distributed Training of BART and T5 for Summarization via Hugging Face and Amazon SageMaker
Hugging Face and Amazon SageMaker have integrated to provide optimized Deep Learning Containers and a dedicated HuggingFace estimator to simplify distributed training of Transformers models like BART and T5.
Understanding BigBird's Block Sparse Attention
BigBird introduces block sparse attention to reduce the computational complexity of Transformers from quadratic to linear, enabling the processing of sequences up to 4096 tokens.
Amazon SageMaker and Hugging Face Partnership
Hugging Face and Amazon have partnered to integrate Hugging Face Transformers into Amazon SageMaker via dedicated Deep Learning Containers (DLCs) and a Python SDK extension to accelerate NLP model training and deployment.
Deploying a Serverless Transformers Pipeline on Google Cloud
Maxence Dominici demonstrates how to deploy a sentiment analysis pipeline using Hugging Face Transformers and Google Cloud Run for a low-volume, serverless microservice.
Fine-Tuning Wav2Vec2 for English ASR with Hugging Face Transformers
Hugging Face provides a detailed guide on fine-tuning the Wav2Vec2 pretrained speech model for English Automatic Speech Recognition (ASR) using Connectionist Temporal Classification (CTC) loss.
Hugging Face Reads: Long-range Transformers
Hugging Face analyzes four key architectures—Longformer, Compressive Transformer, Linformer, and Performer—designed to reduce the quadratic memory and time complexity of standard Transformer self-attention to linear complexity.
Hugging Face: Simple Considerations for Building Neural Networks
Hugging Face provides a framework for building and debugging neural networks by prioritizing data analysis, simple baselines, and rigorous implementation checks over blind hyperparameter tuning.
Retrieval Augmented Generation with Hugging Face Transformers and Ray
Hugging Face has integrated Ray into the Retrieval Augmented Generation (RAG) model's document retrieval mechanism to achieve a 2x speedup in retrieval calls and improve distributed fine-tuning scalability.
Hugging Face PyTorch / XLA TPU Integration
Hugging Face has integrated PyTorch / XLA to enable PyTorch users to train and scale transformer models on Cloud TPUs using the existing Hugging Face Trainer interface.
Hugging Face Transformers v4.2.0 TensorFlow Performance and Serving Updates
Hugging Face Transformers v4.2.0 introduces significant computational performance gains for TensorFlow models and streamlined deployment via TensorFlow Serving using the SavedModel format.
Hugging Face Transformers ZeRO Integration via DeepSpeed and FairScale
Hugging Face Transformers v4.2.0 introduces experimental support for DeepSpeed and FairScale's ZeRO optimizations, enabling the training of larger models with higher batch sizes and reduced GPU memory requirements.
Hugging Face Accelerated Inference API Optimization
Hugging Face achieved a 100x speedup in transformer inference for its Accelerated Inference API by combining high-level library optimizations, Rust-based tokenization, and hardware-specific compilation.
Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models – Hugging Face Blog Summary
Hugging Face’s blog post explains how to warm-start encoder-decoder models using pre‑trained BERT, RoBERTa, or GPT2 checkpoints, showing that this approach matches the performance of large pre‑trained seq2seq models while cutting training cost.
Porting fairseq WMT19 translation system to 🤗 Transformers
Hugging Face ported the fairseq WMT19 translation models (en‑ru, ru‑en, de‑en, en‑de) to the 🤗 Transformers library, allowing users to load and run these high‑quality translators with the standard Transformers API.
Hugging Face Transformers and Ray Tune Integration
Hugging Face Transformers 3.1 introduces an integration with Ray Tune, enabling users to easily implement advanced hyperparameter tuning algorithms like Population-Based Training and Bayesian Optimization.
Transformer-based Encoder-Decoder Models Hugging Face Blog Post 2020
Hugging Face’s 2020 blog post explains the transformer-based encoder-decoder architecture, detailing how it maps input sequences to variable-length outputs via encoder and decoder stacks, self-attention, cross-attention, and autoregressive generation, and shows how to use it with the 🤗Transformers library.
Hugging Face pytorch_block_sparse Release
Hugging Face has released pytorch_block_sparse, a library providing BlockSparseLinear modules to create smaller and faster language models by reducing memory consumption and improving computation efficiency over standard PyTorch sparse matrices.
Reformer: Pushing the Limits of Language Modeling with Memory-Efficient Transformers
The Reformer model, introduced by Hugging Face in July 2020, enables training on sequences up to half a million tokens using less than 8 GB of RAM by combining LSH self‑attention, local self‑attention, chunked feed‑forward layers, reversible residuals, and axial positional encodings.
Hugging Face Text Generation Decoding Methods Guide
Hugging Face provides a comprehensive overview of auto-regressive decoding strategies, including Greedy Search, Beam Search, and Sampling (Top-K and Top-p), to optimize open-ended language generation in Transformers.
Training a Language Model from Scratch with Transformers and Tokenizers
Hugging Face provides a comprehensive guide and demonstration on training a new language model from scratch using the Transformers and Tokenizers libraries, featuring the creation of EsperBERTo for the Esperanto language.