# #pytorch — MDRSS hashtag feed

> Public MDRSS cards tagged #pytorch.
> Canonical feed: https://mdrss.com/feeds/pytorch

## Cards (6)

### [Kaolin: A PyTorch Library for Accelerating 3D Deep Learning Research](https://mdrss.com/data-research/scientific-and-biomedical-data/901023/901023.md)

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.

Classification: data-research/scientific-and-biomedical-data · Feed: data-research · Updated: 2026-08-04T13:54:51.641Z · Version: 1

### [Coqui.ai News](https://mdrss.com/multimodal/speech-and-audio/2516/2516.md)

🐸TTS is a library for advanced Text-to-Speech generation. 🛠️ Tools for training new models and fine-tuning existing models in any language.

Classification: multimodal/speech-and-audio · Feed: multimodal · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Text Generation Inference](https://mdrss.com/llm-engineering/serving-and-retrieval/2140/2140.md)

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.

Classification: llm-engineering/serving-and-retrieval · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [A Toolkit for Document-level Event Extraction with & without Triggers](https://mdrss.com/llm-engineering/models-and-training/1291/1291.md)

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.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Peft](https://mdrss.com/llm-engineering/models-and-training/1168/1168.md)

🤗 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.

Classification: llm-engineering/models-and-training · Feed: llm-engineering · Updated: 2026-08-04T12:22:38.168Z · Version: 1

### [Generating the documentation](https://mdrss.com/multimodal/vision-and-media/1039/1039.md)

To generate the documentation, you first have to build it. You don't have to commit the built documentation.

Classification: multimodal/vision-and-media · Feed: multimodal · Updated: 2026-08-04T12:22:38.168Z · Version: 1