Kaolin packages reusable building blocks from NVIDIA 3D research into a cohesive PyTorch API — continuously improving representation-agnostic physics simulation, fast conversions between representations, quaternion math, batched mesh and splat containers, I/O, visualization and m. Use it to build a structured path from fundamentals to hands-on practice.
🐸TTS is a library for advanced Text-to-Speech generation. 🛠️ Tools for training new models and fine-tuning existing models in any language.
A Rust, Python and gRPC server for text generation inference. Used in production at Hugging Face to power Hugging Chat, the Inference API and Inference Endpoints.
This project aims at building a universal toolkit for extracting events automatically from documents 📄 (long texts). The details can be found in our paper: Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Yuan, Min Zhang.
🤗 PEFT State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters.
To generate the documentation, you first have to build it. You don't have to commit the built documentation.
Kaolin packages reusable building blocks from NVIDIA 3D research into a cohesive PyTorch API — continuously improving representation-agnostic physics simulation, fast conversions between representations, quaternion math, batched mesh and splat containers, I/O, visualization and m. Use it to build a structured path from fundamentals to hands-on practice.
🐸TTS is a library for advanced Text-to-Speech generation. 🛠️ Tools for training new models and fine-tuning existing models in any language.
A Rust, Python and gRPC server for text generation inference. Used in production at Hugging Face to power Hugging Chat, the Inference API and Inference Endpoints.
This project aims at building a universal toolkit for extracting events automatically from documents 📄 (long texts). The details can be found in our paper: Tong Zhu, Xiaoye Qu, Wenliang Chen, Zhefeng Wang, Baoxing Huai, Nicholas Yuan, Min Zhang.
🤗 PEFT State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters.
To generate the documentation, you first have to build it. You don't have to commit the built documentation.