Search Results for "lightning"
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Showing 39 open source projects for "lightning"
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lightning AI
The most intuitive, flexible, way for researchers to build models
...Find templates (Lightning Apps), modify them and publish your own. Lightning Apps can even be full standalone ML products! Run on your laptop for free! Download the code and type 'lightning run app'. Feel free to ssh into any machine and run from there as well. In research, we often have multiple separate scripts to train models, finetune them, collect results and more. -
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Agent Lightning
The absolute trainer to light up AI agents
...Agent-Lightning introduces a lightweight training pipeline that observes agents’ execution traces, converts them into structured data, and feeds them into training algorithms, enabling users to improve agent behaviors systematically. The project emphasizes minimalist integration, so you can drop this into existing systems without extensive rewrites, focusing instead on iterative performance improvement. -
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PyTorch Lightning
The lightweight PyTorch wrapper for high-performance AI research
Scale your models, not your boilerplate with PyTorch Lightning! PyTorch Lightning is the ultimate PyTorch research framework that allows you to focus on the research while it takes care of everything else. It's designed to decouple the science from the engineering in your PyTorch code, simplifying complex network coding and giving you maximum flexibility. PyTorch Lightning can be used for just about any type of research, and was built for the fast inference needed in AI research and production. ... -
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Lightly
A python library for self-supervised learning on images
...Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training through advanced filtering. We provide PyTorch, PyTorch Lightning and PyTorch Lightning distributed examples for each of the models to kickstart your project. Lightly requires Python 3.6+ but we recommend using Python 3.7+. We recommend installing Lightly in a Linux or OSX environment. With lightly, you can use the latest self-supervised learning methods in a modular way using the full power of PyTorch. ... -
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RoboSats
A simple and private bitcoin exchange
RoboSats is a privacy-preserving, peer-to-peer Bitcoin exchange that allows users to buy and sell Bitcoin using Lightning Network payments without requiring identity verification. It uses Tor by default and leverages a simple robot-based user interface to facilitate one-time trades between anonymous users. Each trade session is isolated, and no long-term accounts or KYC are needed, making RoboSats one of the most private Bitcoin P2P tools available. -
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Bitcart
Bitcart is a platform for merchants, users and developers
An open-source platform for managing cryptocurrency payments and stores without intermediaries. -
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Fast3R
Fast3R: Towards 3D Reconstruction of 1000+ Images in One Forward Pass
...It represents a next-generation feedforward 3D reconstruction model capable of producing dense point clouds and camera poses for hundreds to thousands of images or video frames in a single inference pass—eliminating the need for slow, iterative structure-from-motion pipelines. Built on PyTorch Lightning and extending concepts from DUSt3R and Spann3r, Fast3R unifies multi-view geometry, depth estimation, and camera registration within a single transformer-based architecture. It outputs high-quality 3D scene representations from unordered or sequential views, scaling to large datasets and varied camera intrinsics. The repository includes pretrained models, Gradio-based demos, and modular APIs for direct integration into research or production workflows. -
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NVIDIA NeMo Framework
Scalable generative AI framework built for researchers and developers
...NeMo 2.0 introduces a Python-based configuration system, replacing YAML with more flexible, programmable configs that can be versioned and composed for different experiments. The framework builds on PyTorch Lightning–style modular abstractions, so training scripts are composed from reusable components for data loading, models, optimizers, and schedulers, which simplifies experimentation and adaptation. NeMo is designed to scale: with tools like NeMo-Run, users can orchestrate large-scale experiments across thousands of GPUs. -
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CyberScraper 2077
A Powerful web scraper powered by LLM | OpenAI, Gemini & Ollama
CyberScraper 2077 is not just another web scraping tool – it's a glimpse into the future of data extraction. Born from the neon-lit streets of a cyberpunk world, this AI-powered scraper uses OpenAI, Gemini and LocalLLM Models to slice through the web's defenses, extracting the data you need with unparalleled precision and style. -
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TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 80+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! Automatic accumulation over multiple batches. Automatic synchronization between multiple devices. Metric arithmetic. Similar to torch.nn, most metrics have both a module-based and a functional version. ... -
11
Transcrypt
Python 3.7 to JavaScript compiler
...There's a simple parallel between the Python and the JavaScript code. In combination with the use of source maps, this enables efficient debugging. Also, code can be tested from the command prompt using stubs. Lightning-fast JavaScript 6 code: call caching, for-loop optimization, in-line JavaScript etc. Integrated static typechecking and minification at the tip of a command-line switch. Also runs on top of node.js. Extensive documentation with many code examples. Apache 2.0 license. Pip-install and go! Seamless integration with the universe of high-quality web-oriented JavaScript libraries, rather than the desktop-oriented Python ones. -
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MiniMax-M1
Open-weight, large-scale hybrid-attention reasoning model
...It is built on the MiniMax-Text-01 foundation and keeps the same massive parameter budget, but reworks the attention and training setup for better reasoning and test-time compute scaling. Architecturally, it combines Mixture-of-Experts layers with lightning attention, enabling the model to support a native context length of 1 million tokens while using far fewer FLOPs than comparable reasoning models for very long generations. The team emphasizes efficient scaling of test-time compute: at 100K-token generation lengths, M1 reportedly uses only about 25 percent of the FLOPs of some competing models, making extended “think step” traces more feasible. ... -
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MiniMax-01
Large-language-model & vision-language-model based on Linear Attention
MiniMax-01 is the official repository for two flagship models: MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model built on top of it. MiniMax-Text-01 uses a hybrid attention architecture that blends Lightning Attention, standard softmax attention, and Mixture-of-Experts (MoE) routing to achieve both high throughput and long-context reasoning. It has 456 billion total parameters with 45.9 billion activated per token and is trained with advanced parallel strategies such as LASP+, varlen ring attention, and Expert Tensor Parallelism, enabling a training context of 1 million tokens and up to 4 million tokens at inference. ... -
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LitGPT
20+ high-performance LLMs with recipes to pretrain, finetune at scale
LitGPT is a collection of over 20 high-performance large language models (LLMs) accompanied by recipes to pretrain, finetune, and deploy them at scale. It provides implementations without abstractions, making it beginner-friendly while offering advanced features like flash attention and support for various precision levels. LitGPT is designed to run efficiently across multiple GPUs or TPUs, catering to both small-scale and large-scale deployments. -
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PyTorch Forecasting
Time series forecasting with PyTorch
...Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities. The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box. -
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Flower
Flower: A Friendly Federated Learning Framework
...Different machine learning frameworks have different strengths. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand. -
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NovaSR
A lightning fast audio upsampler
NovaSR is an extremely lightweight and high-performance audio upsampling model that transforms low-quality 16 kHz audio into clearer, high-fidelity 48 kHz audio with remarkable speed and efficiency. At only about 50 KB in size, the model is orders of magnitude smaller than typical audio super-resolution networks, yet it achieves high quality and realtime performance thanks to its compact architecture and efficient convolutional design. NovaSR is especially valuable for post-processing tasks... -
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EdgeDB
A next-generation graph-relational database
The world's favorite graph-relational database is back for an action-packed sequel. Tune in for a series of lightning talks breaking down what's new and what's next for EdgeDB. EdgeDB is an open-source database designed as a spiritual successor to SQL and the relational paradigm. It aims to solve some hard design problems that make existing databases unnecessarily onerous to use. Powered by the Postgres query engine under the hood, EdgeDB thinks about schema the same way you do: as objects with properties connected by links. ... -
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AReal
Lightning-Fast RL for LLM Reasoning and Agents. Made Simple & Flexible
AReaL is an open source, fully asynchronous reinforcement learning training system. AReal is designed for large reasoning and agentic models. It works with models that perform reasoning over multiple steps, agents interacting with environments. It is developed by the AReaL Team at Ant Group (inclusionAI) and builds upon the ReaLHF project. Release of training details, datasets, and models for reproducibility. It is intended to facilitate reproducible RL training on reasoning / agentic tasks,... -
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Semantic Router
Superfast AI decision making and processing of multi-modal data
...Combining LLMs with deterministic rules means we can be confident that our AI systems behave as intended. Cramming agent tools into the limited context window is expensive, slow, and fundamentally limited. Semantic Router enables lightning-fast and cheap tool usage that can scale to many thousands of tools. LLMs are slow, yet we use them for every decision in agentic use-cases. Semantic Router swaps slow LLM calls for superfast route decisions. -
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Arize Phoenix
Uncover insights, surface problems, monitor, and fine tune your LLM
Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve. -
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NVIDIA NeMo
Toolkit for conversational AI
...Every module can easily be customized, extended, and composed to create new conversational AI model architectures. Conversational AI architectures are typically large and require a lot of data and compute for training. NeMo uses PyTorch Lightning for easy and performant multi-GPU/multi-node mixed-precision training. Supported models: Jasper, QuartzNet, CitriNet, Conformer-CTC, Conformer-Transducer, Squeezeformer-CTC, Squeezeformer-Transducer, ContextNet, LSTM-Transducer (RNNT), LSTM-CTC. NGC collection of pre-trained speech processing models. -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
...It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic management domains. Finally, you can also create your own datasets. The package interfaces well with Pytorch Lightning which allows training on CPUs, single and multiple GPUs out-of-the-box. PyTorch Geometric Temporal makes implementing Dynamic and Temporal Graph Neural Networks quite easy - see the accompanying tutorial. Head over to our documentation to find out more about installation, creation of datasets and a full list of implemented methods and available datasets. -
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marqo
Tensor search for humans
...Marqo is a versatile and robust search and analytics engine that can be integrated into any website or application. Due to horizontal scalability, Marqo provides lightning-fast query times, even with millions of documents. Marqo helps you configure deep-learning models like CLIP to pull semantic meaning from images. It can seamlessly handle image-to-image, image-to-text and text-to-image search and analytics. Marqo adapts and stores your data in a fully schemaless manner. It combines tensor search with a query DSL that provides efficient pre-filtering. ... -
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Matcha-TTS
A fast TTS architecture with conditional flow matching
...The model is fully probabilistic, so it can generate diverse realizations of the same text while still sounding stable and intelligible. The repository provides an end-to-end TTS pipeline: a PyTorch/Lightning training stack, configuration files, pre-trained checkpoints, a command-line interface, and a Gradio app for interactive testing. Users can train on standard datasets like LJSpeech or plug in their own corpora, with helper tools for computing dataset statistics, extracting phoneme durations, and running multi-GPU training.
