Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚ - exo-explore/exo

Browse and filter Open VLM leaderboard data by model name, size, and type. View detailed evaluation results across various datasets.

An OpenAI API compatible API for chat with image input and questions about the images. aka Multimodal. - matatonic/openedai-vision

Foundation models have become general-purpose assistants, exhibiting diverse capabilities across numerous domains through training on web-scale data. It remains challenging to precisely...

scikit-learn cross validators for iterative stratification of multilabel data - trent-b/iterative-stratification

Daily newsletter categorizing & easily explaining LLMs research papers as they published.

Codebase for Berkeley Humanoid Lite. Contribute to HybridRobotics/Berkeley-Humanoid-Lite development by creating an account on GitHub.

Introducing Continuous Thought Machines

Convolutions are a type of operation that can be used to learn representations from images. They involve a learnable kernel sliding over the image and performing element-wise multiplication with the input. The specification allows for parameter sharing and…

We’re on a journey to advance and democratize artificial intelligence through open source and open science.

We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-reflections when...

uv is an extremely fast Python package and project manager, written in Rust.

The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval - parthsarthi03/raptor

The code used to train and run inference with the ColVision models, e.g. ColPali, ColQwen2, and ColSmol. - illuin-tech/colpali

Diffusion-based language models offer a compelling alternative to autoregressive (AR) models by enabling parallel and controllable generation. Among this family of models, Masked Diffusion Models...

Vision Transformers (ViTs) have shown remarkable performance and scalability across various computer vision tasks. To apply single-scale ViTs to image segmentation, existing methods adopt a...

Self-supervised visual foundation models produce powerful embeddings that achieve remarkable performance on a wide range of downstream tasks. However, unlike vision-language models such as CLIP,...

The ability to predict future outcomes given control actions is fundamental for physical reasoning. However, such predictive models, often called world models, remains challenging to learn and are...