EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment AnythingEfficientSAM EfficientSAM: Leveraged Masked Image Pretraining for Efficient Segment Anything News [Dec.6 2023] EfficientSAM demo is available on the Hugging Face Space (huge thanks to all the HF team for their support). [Dec.5 2023] We release the torchscript version of EfficientSAM and share a colab. Online Demo & Examples Online demo and examples can be found in the project page. EfficientSAM Instance Segmentat…
Optimum-NVIDIA Optimized inference with NVIDIA and Hugging Face Optimum-NVIDIA delivers the best inference performance on the NVIDIA platform through Hugging Face. Run LLaMA 2 at 1,200 tokens/second (up to 28x faster than the framework) by changing just a single line in your existing transformers code. Installation You can use a Docker container to try Optimum-NVIDIA today. Images are available on the Hugging Face Docker Hub. docker pull huggingface/optimum-nvidia An Optimum-NVIDIA…
Build in-app AI chatbots 🤖, and AI-powered Textareas ✨, into react web apps. The Open-Source Copilot Platform In-app chatbots, and AI-enabled TextArea. Explore the docs » Join our Discord · Website · Report Bug · Request Feature Questions? Book a call with us » 🌟 <CopilotPortal />: Build in-app AI chatbots that can "see" the current app state + take act…
Examples in the MLX frameworkMLX Examples This repo contains a variety of standalone examples using the MLX framework. The MNIST example is a good starting point to learn how to use MLX. Some more useful examples include: Transformer language model training. Large scale text generation with LLaMA or Mistral. Parameter efficient fine-tuning with LoRA. Generating images with Stable Diffusion. Speech recognition with OpenAI's Whisper. Contributing Check out the contribution guidelines for mo…
MLX: An array framework for Apple siliconMLX Quickstart | Installation | Documentation | Examples MLX is an array framework for machine learning on Apple silicon, brought to you by Apple machine learning research. Some key features of MLX include: Familiar APIs: MLX has a Python API that closely follows NumPy. MLX also has a fully featured C++ API, which closely mirrors the Python API. MLX has higher-level packages like mlx.nn and mlx.optimizers with APIs that closely follow PyTorch to simpl…
Notes on books I read, talks I watch, articles I study, and papers I loveLearning Notes Taking notes on books I read, talks I watch, articles I study, and papers I love – recalling them right afterward by creating short summaries – helps a lot in my learning process. Here you'll find some of those little pieces. If you are looking for an easy way to consume these notes, please check out keyvanakbary.github.io/learning-notes/. Books 99 Bottles of OOP by Sandi Metz and Katrina Owen, 2016. An…
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🔮 SuperDuperDB: Bring AI to your database: Integrate, train and manage any AI models and APIs directly with your database and your data. Bring AI to your favorite database! Docs | Blog | Use-Cases | Live Notebooks | Community Apps | Slack | Youtube English | 中文 What is SuperDuperDB? 🔮 SuperDuperDB is an open-source framework for integrating AI directly with your existing databases, including streaming inference, scalable model training, and vector s…