AI 뉴스

DALL·E 2 pre-training mitigations

In order to share the magic of DALL·E 2 with a broad audience, we needed to reduce the risks associated with powerful image generation models. To this end, we put various guardrails in place to prevent generated images from violating our content policy.

Announcing Evaluation on the Hub

Accelerate Large Model Training using DeepSpeed

Learning to play Minecraft with Video PreTraining

We trained a neural network to play Minecraft by Video PreTraining (VPT) on a massive unlabeled video dataset of human Minecraft play, while using only a small amount of labeled contractor data. With fine-tuning, our model can learn to craft diamond tools, a task that usually takes proficient human…

Getting Started With Embeddings

Convert Transformers to ONNX with Hugging Face Optimum

Evolution through large models

Intel and Hugging Face Partner to Democratize Machine Learning Hardware Acceleration

Director of Machine Learning Insights [Part 3: Finance Edition]

AI-written critiques help humans notice flaws

We trained “critique-writing” models to describe flaws in summaries. Human evaluators find flaws in summaries much more often when shown our model’s critiques. Larger models are better at self-critiquing, with scale improving critique-writing more than summary-writing. This shows promise for using …

Techniques for training large neural networks

Large neural networks are at the core of many recent advances in AI, but training them is a difficult engineering and research challenge which requires orchestrating a cluster of GPUs to perform a single synchronized calculation.

Deep Q-Learning with Space Invaders

The Annotated Diffusion Model

Best practices for deploying language models

Cohere, OpenAI, and AI21 Labs have developed a preliminary set of best practices applicable to any organization developing or deploying large language models.

Teaching models to express their uncertainty in words

Graphcore and Hugging Face Launch New Lineup of IPU-Ready Transformers

Introducing Pull Requests and Discussions 🥳

Powering next generation applications with OpenAI Codex

Codex is now powering 70 different applications across a variety of use cases through the OpenAI API.

Efficient Table Pre-training without Real Data: An Introduction to TAPEX

An Introduction to Q-Learning Part 2/2