This article is a hands-on experience report from the author on deploying a Hugging Face Transformers pipeline to a serverless environment on Google Cloud…
This is a landmark technical tutorial published by the Hugging Face team in 2021, detailing how to fine-tune Meta AI's Wav2Vec2 model using the Hugging Face…
In the field of natural language processing (NLP), the core of standard Transformer models (such as BERT and GPT-2) is the self-attention mechanism. However…
Retrieval-Augmented Generation (RAG) is a powerful architecture that combines a "retriever" with a "generator." It enables language models to dynamically…
Hugging Face has announced a deep collaboration with Google Cloud, officially adding support for PyTorch/XLA within its ecosystem. The goal is to address the…
As the parameter scale of Transformer models (such as GPT, T5, etc.) grows exponentially, deep learning faces a severe "Memory Wall" challenge. With limited…
In this technical blog post, the Hugging Face team reveals in detail how they achieved up to 100x speedup in inference for Transformer models for customers of…
In the field of natural language processing (NLP), machine translation has always been a core challenge. Facebook AI Research (FAIR) achieved outstanding…
This classic blog post written by Hugging Face researcher Patrick von Platen takes a deep dive into the Transformer-based Encoder-Decoder model architecture…
This classic technical blog post written by Hugging Face takes an in-depth look at how to select and tune different "decoding methods" when performing…
This classic blog post from Hugging Face provides a detailed walkthrough of how to use their open-source ecosystem libraries — `transformers` and `tokenizers`…