Tags: #pytorch

A deep learning framework that simplifies PyTorch development by automating boilerplate engineering code, enabling scalable training from CPU to multi-node GPUs with minimal code changes.

A comprehensive, step-by-step guide and codebase for building a ChatGPT-like Large Language Model from scratch using PyTorch.

A KVCache-centric disaggregated architecture for high-performance LLM serving, powering leading AI services.

A Kubernetes-native platform for scalable distributed AI model training and large language model (LLM) fine-tuning across various frameworks.

A PyTorch library enabling accessible large language models by dramatically reducing memory consumption through k-bit quantization for both inference and training.

Ludwig is a low-code, declarative deep learning framework designed to simplify the building, training, and deployment of custom AI models, including LLMs and neural networks.

A comprehensive and curated collection of deep learning study notes, integrating content from leading educators like Andrew Ng, Li Mu, and TuDui, covering CV, NLP, and Large Language Models.

A comprehensive 12-week, 24-lesson curriculum designed to introduce beginners to the fundamentals of Artificial Intelligence.

A PyTorch-based framework to adapt Meta AI's Segment Anything Model (SAM) for improved performance on challenging downstream computer vision tasks using adapters and prompts.

A unified library for parameter-efficient and modular transfer learning, extending HuggingFace Transformers with various adapter methods.

A comprehensive collection of PyTorch implementations for over 60 deep learning papers, accompanied by detailed side-by-side notes for enhanced understanding.

A PyTorch library implementing LoRA (Low-Rank Adaptation) to efficiently fine-tune large language models by significantly reducing trainable parameters and storage requirements.

A modular PyTorch library for state-of-the-art diffusion models, enabling easy generation of images, audio, and more.

A PyTorch implementation of Dreamfusion, enabling text-to-3D and image-to-3D content generation using NeRF and Stable Diffusion.

A fast, minimal PyTorch port of DALL·E Mini, optimized for efficient text-to-image generation inference.

A PyTorch implementation of Google's Imagen, a state-of-the-art text-to-image neural network that surpasses DALL-E2 in synthesis quality.

A PyTorch implementation of OpenAI's DALL-E 2, enabling advanced text-to-image synthesis through a diffusion-based neural network architecture.

An open-source PyTorch implementation of OpenAI's DALL-E, a text-to-image transformer, including CLIP for generation ranking.

A scalable generative AI framework for building, customizing, and deploying models focused on Large Language Models, Multimodal, and Speech AI (ASR, TTS).

Silero Models offers a collection of pre-trained, end-to-end text-to-speech models designed for simplicity, speed, and natural-sounding speech generation.

A powerful open-source toolkit for real-time voice cloning and arbitrary speech generation from text.

A PyTorch implementation of BitNet, enabling highly efficient 1-bit transformers for large language models.

A modular PyTorch-based framework from Facebook AI Research for state-of-the-art vision and language multimodal AI research.

A high-performance, comprehensive face recognition library built on PaddlePaddle and PyTorch.

UER-py is an open-source PyTorch-based framework for pre-training and fine-tuning NLP models, offering modularity, extensibility, and a comprehensive model zoo.

DiffSinger is an official PyTorch implementation of a singing voice synthesis (SVS) and text-to-speech (TTS) system, leveraging a shallow diffusion mechanism for high-quality audio generation.

A deep learning library for advanced Text-to-Speech generation, offering high-quality speech synthesis with pretrained models and multi-language support.