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RAGEN is the first open-source reproduction of DeepSeek-R1 on AGENT training.
Depth Pro: Sharp Monocular Metric Depth in Less Than a Second.
Python package for the evaluation of odometry and SLAM
🤗 LeRobot: Making AI for Robotics more accessible with end-to-end learning
ROS2 Serial communication interface
Distribute and run LLMs with a single file.
An open source quadruped robot pet framework for developing Boston Dynamics-style four-legged robots that are perfect for STEM, coding & robotics education, IoT robotics applications, AI-enhanced r…
Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything
A ROS implementation of ORB_SLAM2
Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities
Chat with your documents on your local device using GPT models. No data leaves your device and 100% private.
PyTorch3D is FAIR's library of reusable components for deep learning with 3D data
Instruct-tune LLaMA on consumer hardware
Code and documentation to train Stanford's Alpaca models, and generate the data.
🎉 Repo for LaWGPT, Chinese-Llama tuned with Chinese Legal knowledge. 基于中文法律知识的大语言模型
AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.
Official PyTorch implementation of "VITON-HD: High-Resolution Virtual Try-On via Misalignment-Aware Normalization" (CVPR 2021)
A Reinforcement Learning Software Toolbox for Robotics
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
Perform data science on data that remains in someone else's server
A latent text-to-image diffusion model
Integration between ROS (1 and 2) and Gazebo simulation
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
A playbook for systematically maximizing the performance of deep learning models.