DALL·E: Introducing outpainting
OpenAI launches outpainting for DALL·E, enabling users to extend generated images beyond their original boundaries. Feature now available on the platform.
18 articles
OpenAI launches outpainting for DALL·E, enabling users to extend generated images beyond their original boundaries. Feature now available on the platform.
Hugging Face introduces OpenRAIL, an open and responsible AI licensing framework. The project aims to balance open access to models with ethical and legal safeguards, defining responsible use conditions.
OpenAI improves its AI systems' ability to learn from human feedback and assist humans in evaluating AI. Goal: build a sufficiently aligned AI system to solve all other alignment problems.
Hugging Face launches a protein visualization tool on Spaces. The interface enables exploration of 3D structures and molecular properties directly in the browser.
Hugging Face releases a comprehensive guide for pre-training BERT using Transformers and Habana Gaudi accelerators. The tutorial covers data preparation, model configuration, and performance optimization on specialized hardware.
Hugging Face introduces Diffusers, a library for implementing and using diffusion models like Stable Diffusion. The tool simplifies access to image generation models with a modular API and pre-configured pipelines.
Hugging Face publishes a deployment guide for Vision Transformer (ViT) model on Google's Vertex AI. The tutorial covers integration, configuration, and production deployment of a computer vision model on Google's cloud infrastructure.
Hugging Face Optimum integrates Vision Transformers on Graphcore processors. The guide details optimization of vision models for IPU inference, with performance benchmarks and reproducible code.
Hugging Face releases a guide on 8-bit matrix multiplication for large-scale transformers using transformers, accelerate, and bitsandbytes libraries. Quantization technique reduces memory footprint and accelerates inference with minimal precision loss.
Hugging Face outlines its TensorFlow philosophy: prioritizing accessibility, native integration of pre-trained models, and open ecosystem. Focus on democratizing ML and interoperability with PyTorch.
Hugging Face introduces Skops, a library for saving, loading and sharing scikit-learn models with versioning and traceability. Native integration with Hugging Face Hub to enable collaboration and reproducibility.
Hugging Face publishes a deployment guide for Vision Transformer (ViT) model on Kubernetes using TensorFlow Serving. Covers containerization, inference configuration, and production orchestration.
OpenAI releases an improved content moderation tool free for API developers. The Moderation endpoint replaces the previous filter with enhanced detection capabilities.
Hugging Face releases a comprehensive guide for training and fine-tuning Sentence Transformers models. The tutorial covers fine-tuning techniques, embedding optimization, and integration with the Hugging Face platform to deploy high-performance text representation models.
Article on Proximal Policy Optimization (PPO), a foundational reinforcement learning algorithm used to train AI models. PPO improves stability and efficiency of reinforcement learning compared to earlier methods.
Hugging Face launches Private Hub, a platform enabling enterprises to build ML models in a private, secure environment. Provides access to open-source models, fine-tuning tools, and dedicated infrastructure without public exposure.
Nyströmformer approximates self-attention in linear time and memory using the Nyström method. This technique reduces the quadratic complexity of standard transformers, enabling processing of longer sequences with lower computational requirements.
Hugging Face comments on the U.S. National AI Research Resource (NAIRR) interim report. The organization emphasizes the need for democratized access to compute resources and data for AI research, while advocating for policies supporting open-source and collaboration.