Large language models

Aligning language models to follow instructions

Boosting Wav2Vec2 with n-grams in 🤗 Transformers

Deploy GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker

WebGPT: Improving the factual accuracy of language models through web browsing

We’ve fine-tuned GPT-3 to more accurately answer open-ended questions using a text-based web browser.

Customizing GPT-3 for your application

Fine-tune with a single command.

Getting Started with Hugging Face Transformers for IPUs with Optimum

Fine-Tune XLSR-Wav2Vec2 for low-resource ASR with 🤗 Transformers

Solving math word problems

We’ve trained a system that solves grade school math problems with nearly twice the accuracy of a fine-tuned GPT-3 model. It solves about 90% as many problems as real kids: a small sample of 9-12 year olds scored 60% on a test from our dataset, while our system scored 55% on those same problems.

Large Language Models: A New Moore's Law?

Introducing Optimum: The Optimization Toolkit for Transformers at Scale

Hugging Face and Graphcore partner for IPU-optimized Transformers

Deep Learning over the Internet: Training Language Models Collaboratively

Evaluating large language models trained on code

Sentence Transformers in the Hugging Face Hub

Improving language model behavior by training on a curated dataset

Our latest research finds we can improve language model behavior with respect to specific behavioral values by fine-tuning on a small, curated dataset.

Few-shot learning in practice: GPT-Neo and the 🤗 Accelerated Inference API

Distributed Training: Train BART/T5 for Summarization using 🤗 Transformers and Amazon SageMaker

GPT-3 powers the next generation of apps

Over 300 applications are delivering GPT-3–powered search, conversation, text completion, and other advanced AI features through our API.

My Journey to a serverless transformers pipeline on Google Cloud

Fine-Tune Wav2Vec2 for English ASR in Hugging Face with 🤗 Transformers