Faster TensorFlow models in Hugging Face Transformers
Faster TensorFlow models in Hugging Face Transformers.
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Faster TensorFlow models in Hugging Face Transformers.
We’ve scaled Kubernetes clusters to 7,500 nodes, producing a scalable infrastructure for large models like GPT-3, CLIP, and DALL·E, but also for rapid small-scale iterative research such as...
Fit More and Train Faster With ZeRO via DeepSpeed and FairScale.
How we sped up transformer inference 100x for 🤗 API customers.
We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.
We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision. CLIP can be applied to any visual classification benchmark by simply...
It’s been a year of dramatic change and growth at OpenAI.
Leveraging Pre-trained Language Model Checkpoints for Encoder-Decoder Models.
Porting fairseq wmt19 translation system to transformers.
Hyperparameter Search with Transformers and Ray Tune.
Transformer-based Encoder-Decoder Models.
OpenAI has agreed to license GPT-3 to Microsoft for their own products and services.
Block Sparse Matrices for Smaller and Faster Language Models.
Generative language modeling for automated theorem proving.
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization.
Our third class of OpenAI Scholars presented their final projects at virtual Demo Day, showcasing their research results from over the past five months.
The Reformer - Pushing the limits of language modeling.
Announce that OpenAI is co-organizing two NeurIPS 2020 competitions with AIcrowd, Carnegie Mellon University, and DeepMind, using Procgen Benchmark and MineRL.
We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples....
We’re releasing an API for accessing new AI models developed by OpenAI.
Language models are few-shot learners.
We’re releasing an analysis showing that since 2012 the amount of compute needed to train a neural net to the same performance on ImageNet classification has been decreasing by a factor of 2 every...
We’re introducing Jukebox, a neural net that generates music, including rudimentary singing, as raw audio in a variety of genres and artist styles. We’re releasing the model weights and code,...
We’ve contributed to a multi-stakeholder report by 58 co-authors at 30 organizations, including the Centre for the Future of Intelligence, Mila, Schwartz Reisman Institute for Technology and...
We’re introducing OpenAI Microscope, a collection of visualizations of every significant layer and neuron of eight vision “model organisms” which are often studied in interpretability. Microscope...
How to generate text: using different decoding methods for language generation with Transformers.
How to train a new language model from scratch using Transformers and Tokenizers.
We are standardizing OpenAI’s deep learning framework on PyTorch.
Scaling laws for neural language models.
Dota 2 with large scale deep reinforcement learning.