Tags: #deep-learning

A unified framework providing state-of-the-art machine learning models for text, vision, audio, and multimodal tasks, optimized for both inference and training.

A comprehensive framework from ModelScope for efficiently fine-tuning, evaluating, and deploying over 1000 large language models and multimodal large models using advanced techniques.

PEFT is a state-of-the-art library for Parameter-Efficient Fine-Tuning, drastically reducing the computational and storage costs of adapting large pretrained models.

A high-throughput and memory-efficient open-source engine designed for fast, easy, and cost-effective serving of large language models.

A unified and efficient framework for fine-tuning over 100 large language models (LLMs) and vision-language models (VLMs) with both CLI and Web UI.

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 blazing-fast, lightweight inference engine from Alibaba, powering high-performance on-device LLMs and Edge AI.

A free and open-source framework designed for efficient and flexible fine-tuning of large language models.

XTuner V1 is a next-generation training engine specifically designed for ultra-large-scale Mixture-of-Experts (MoE) models, offering superior efficiency and scalability.

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

A universal viewer for neural network, deep learning, and machine learning models, supporting a wide array of formats.

An open standard and format for machine learning models, enabling interoperability across AI frameworks and streamlining the path from research to production.

A comprehensive learning platform offering structured knowledge, interview questions, and paper analysis for Large Language Models (LLMs).

A meticulously curated list of battle-tested, production-proven open-source AI models, libraries, infrastructure, and developer tools.

A comprehensive MLOps platform for managing, orchestrating, and scaling the entire machine learning and deep learning lifecycle.

Deep Lake is an AI data runtime and database optimized for deep learning, offering multimodal data storage, querying, vector search, and streaming for LLM and deep learning applications.

A comprehensive, cloud-native, one-stop platform for machine learning, deep learning, and large language model development, covering the entire MLOps lifecycle.

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 meticulously curated list of battle-tested, production-proven open-source AI models, libraries, infrastructure, and developer tools.

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.

OpenVINO is an open-source toolkit designed to optimize and deploy deep learning models for efficient AI inference across diverse hardware platforms, from edge to cloud.

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

A comprehensive repository offering C++ and Python code examples for computer vision, deep learning, and AI research, complementing articles on LearnOpenCV.com.

A comprehensive toolkit providing best practices and implementations for building, evaluating, and operationalizing recommendation systems.

Colossal-AI makes training and deploying large AI models cheaper, faster, and more accessible through advanced distributed training techniques.

A high-performance, scalable JAX-based open-source library for training large language models on Google Cloud TPUs and GPUs.

A no-code GUI and framework for easily fine-tuning state-of-the-art large language models (LLMs).

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 comprehensive Chinese technical book and associated course materials providing a systematic framework and roadmap for understanding Large Language Models, authored by leading experts.

A toolkit for quantizing (compressing) Large Language Models (LLMs) with hardware acceleration across various GPUs and CPUs, integrating with popular inference frameworks.

A deep learning toolkit for training custom multimodal large language models focused on speech, language, audio, and music processing.

A comprehensive code repository accompanying a hands-on course for mastering Huggingface Transformers, covering fundamental concepts to advanced fine-tuning and deployment techniques.

AirLLM optimizes large language model inference memory, enabling 70B LLMs on a single 4GB GPU without quantization, and 405B Llama3.1 on 8GB VRAM.

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.

LongLoRA is an efficient fine-tuning method and associated models/datasets designed to extend the context window of Large Language Models (LLMs) for processing longer inputs.

A series of high-performance, cost-efficient large language models (LLMs) designed for general-purpose usage and advanced reasoning.

A comprehensive collection of recipes and code for training various reward models crucial for Reinforcement Learning from Human Feedback (RLHF) in large language models.

An open-source framework providing code, models, and insights for stable Reinforcement Learning from Human Feedback (RLHF) training in Large Language Models, focusing on the PPO algorithm and reward modeling.

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 simple command-line tool for generating artistic images from text descriptions using OpenAI's CLIP and Siren neural networks.

A state-of-the-art open-source multilingual text-to-speech system offering natural, expressive, and emotionally rich voice generation.

Clone a voice in 5 seconds to generate arbitrary speech in real-time using a three-stage deep learning framework.

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

vLLM-Omni is an efficient, flexible, and easy-to-use framework extending vLLM to serve omni-modality models (text, image, video, audio) with high throughput and an OpenAI-compatible API.

A unified, reproducible, and efficient multimodal evaluation toolkit for large language models across text, image, video, and audio tasks.

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

An open-source implementation for efficiently fine-tuning Alibaba Cloud's Qwen-VL series of multimodal large language models using HuggingFace and Liger-Kernel.

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

A series of video foundation models and large-scale datasets designed for comprehensive multimodal video understanding and generation.

AutoRound is an advanced quantization toolkit for Large Language Models (LLMs) and Vision-Language Models (VLMs), enabling high-accuracy, ultra-low-bit inference across diverse hardware.

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

An AI voice synthesis library offering instant, accurate, and flexible voice cloning with multi-lingual support.

A deep learning toolkit for Text-to-Speech, offering pretrained models, training tools, and dataset utilities.

An open-source, multi-voice, and prompt-controlled text-to-speech engine capable of generating speech with diverse emotions in English and Chinese.

StyleTTS 2 is a text-to-speech model that achieves human-level speech synthesis by leveraging style diffusion and adversarial training with large speech language models.

MetaVoice-1B is an open-source, 1.2B parameter foundational model for highly expressive, human-like text-to-speech synthesis and zero-shot voice cloning.

TensorFlowTTS provides real-time, state-of-the-art speech synthesis architectures based on TensorFlow 2, supporting multiple languages and optimized for fast inference and deployment on various devices.

An open-source implementation of Microsoft's VALL-E X, enabling zero-shot multilingual text-to-speech synthesis and voice cloning with emotion control.

VITS is an end-to-end text-to-speech model that generates highly natural-sounding audio with diverse rhythms, outperforming traditional two-stage TTS systems.

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

A modularized multimodal large language model designed for OCR-free document understanding.

The first end-to-end multimodal large language model (MM-LLM) that perceives input and generates output in arbitrary combinations (any-to-any) of text, image, video, and audio.

A family of powerful multi-modal large language models (MLLMs) designed to advance AI's understanding and generation capabilities across various data types.

A comprehensive interview preparation guide for AIGC and AI algorithm/development roles, covering a wide range of AI technologies and career insights.

A comprehensive repository offering expert-level tutorials, guides, and courses on various Generative AI technologies, primarily focusing on Stable Diffusion and its ecosystem.

A lightweight C++ toolkit for deploying over 100 diverse AI models with multiple inference engines.

Create dynamic videos by smoothly transitioning between text prompts using Stable Diffusion's latent space exploration.

Official SeedVR2 Video Upscaler for ComfyUI, enabling high-quality video and image upscaling, also runnable as a standalone CLI.

An open-source deep learning tool that enables users to recognize and swap faces in images and videos.

A one-step image-to-image translation framework leveraging Stable Diffusion Turbo for rapid generation across various tasks like sketch-to-image and day-to-night transformations.

SUPIR is an AI-driven project focused on developing practical algorithms for photo-realistic image restoration and upscaling in real-world scenarios.