machine-learning
EFfective Field theORy surrogaTe.:
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Forecasting at scale.
Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
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Welcome to the Coding Train with Daniel Shiffman! A community dedicated to learning creative coding with beginner-friendly tutorials and projects on YouTube and more.
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OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning.
OpenVision 2: A Family of Generative Pretrained Visual Encoders that removes the text encoder and contrastive loss, training with caption-only supervision.
S3GD is a highly optimized, PyTorch-compatible Triton implementation of the Smoothed SignSGD optimizer, meant for reinforcement learning post-training.
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Chronon is a data platform for serving for AI/ML applications.
Chronon is a platform that abstracts away the complexity of data computation and serving for AI/ML applications. Users define features as transformation of raw data, then Chronon can perform batch and streaming computation, scalable backfills, low-latency serving, guaranteed correctness and consistency, as well as a host of observability and monitoring tools.
It allows you to utilize all of the data within your organization, from batch tables, event streams or services to power your AI/ML projects, without needing to worry about all the complex orchestration that this would usually entail.
Open, Device Virtualization, VGPU, Heterogeneous AI Computing.
HAMi (Heterogeneous AI Computing Virtualization Middleware) formerly known as k8s-vGPU-scheduler, is an 'all-in-one' chart designed to manage Heterogeneous AI Computing Devices in a k8s cluster. It can provide the ability to share Heterogeneous AI devices and provide resource isolation among tasks.
A powerful Python package for integrating artificial intelligence with geospatial data analysis and visualization.
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Open-Source ML-Compatible Humanoid Platform for Loco-Manipulation.
ToddlerBot is a low-cost, open-source humanoid robot platform designed for scalable policy learning and research in robotics and AI.
This codebase includes low-level control, RL training, DP training, real-world deployment and basically EVERYTHING you need to run ToddlerBot in the real world!
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TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
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A Deep Learning Approach for Password Guessing.
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AI and inverse problems for a revolution in digital photography.
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💫 Industrial-strength Natural Language Processing (NLP) in Python.
spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products.
spaCy comes with pretrained pipelines and currently supports tokenization and training for 70+ languages. It features state-of-the-art speed and neural network models for tagging, parsing, named entity recognition, text classification and more, multi-task learning with pretrained transformers like BERT, as well as a production-ready training system and easy model packaging, deployment and workflow management. spaCy is commercial open-source software, released under the MIT license.
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The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
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PyTorch Single Controller.
Monarch is a distributed execution engine for PyTorch. Our overall goal is to deliver the high-quality user experience that people get from single-GPU PyTorch, but at cluster scale.
Powerful CPU+GPU Programming. Mojo is a pythonic language for blazing-fast CPU+GPU execution without CUDA. Optionally use it with MAX for insanely fast AI inference.
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The Data Layer for Agentic Enrichment and ML Features. The Virtual Feature Store. Turn your existing data infrastructure into a feature store.
Featureform is a virtual feature store. It enables data scientists to define, manage, and serve their ML model's features. Featureform sits atop your existing infrastructure and orchestrates it to work like a traditional feature store. By using Featureform, a data science team can solve the following organizational problems:
Standardized Serverless ML Inference Platform on Kubernetes. Highly scalable and standards based Model Inference Platform on Kubernetes for Trusted AI.
KServe provides a Kubernetes Custom Resource Definition for serving predictive and generative machine learning (ML) models. It aims to solve production model serving use cases by providing high abstraction interfaces for Tensorflow, XGBoost, ScikitLearn, PyTorch, Huggingface Transformer/LLM models using standardized data plane protocols.
computer vision and sports.
In sports, every centimeter and every second matter. That's why Roboflow decided to use sports as a testing ground to push our object detection, image segmentation, keypoint detection, and foundational models to their limits. This repository contains reusable tools that can be applied in sports and beyond.
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API.
A tiny Autograd engine (with a bite! :)). Implements backpropagation (reverse-mode autodiff) over a dynamically built DAG and a small neural networks library on top of it with a PyTorch-like API. Both are tiny, with about 100 and 50 lines of code respectively. The DAG only operates over scalar values, so e.g. we chop up each neuron into all of its individual tiny adds and multiplies. However, this is enough to build up entire deep neural nets doing binary classification, as the demo notebook shows. Potentially useful for educational purposes.
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A Flexible Framework for Experiencing Cutting-edge LLM Inference Optimizations.
KTransformers, pronounced as Quick Transformers, is designed to enhance your 🤗 Transformers experience with advanced kernel optimizations and placement/parallelism strategies.
KTransformers is a flexible, Python-centric framework designed with extensibility at its core. By implementing and injecting an optimized module with a single line of code, users gain access to a Transformers-compatible interface, RESTful APIs compliant with OpenAI and Ollama, and even a simplified ChatGPT-like web UI.
Artificial Neural Engine Machine Learning Library.
ANEMLL (pronounced like "animal") is an open-source project focused on accelerating the porting of Large Language Models (LLMs) to tensor processors, starting with the Apple Neural Engine (ANE).
Look At Your Data 👀.
Data quality is the most important factor in machine learning success. Hyperparam brings exploration and analysis of massive text datasets to the browser.
I made my AI think harder by making it argue with itself repeatedly. It works stupidly well.
CoRT makes AI models recursively think about their responses, generate alternatives, and pick the best one. It's like giving the AI the ability to doubt itself and try again... and again... and again.
A machine learning framework for Node.js, based on MLX.
A self-contained, lightweight and OOB research platform for modern ML.
Boson is a lightweight, fully containerized, and feature-rich machine learning research platform. It centralizes essential tools to help teams keep projects lean, organized, and reproducible—while reducing overhead and boosting productivity. Think Databricks/Sagemaker but local and free.
Boson enables engineers and researchers to iterate faster without getting bogged down by infrastructure or tooling complexity.
ML Pipelines From Another Planet.Build out-of-this-world ML pipelines.
Run-anywhere computational framework for Python that simplifies and accelerates ML workflows and development. xorq is a deferred computational framework for building, running, and serving pandas groupby-apply style pipelines common in ML workflows. xorq is built on top of Ibis and Apache DataFusion.
JobSet: a k8s native API for distributed ML training and HPC workloads
JobSet is a Kubernetes-native API for managing a group of k8s Jobs as a unit. It aims to offer a unified API for deploying HPC (e.g., MPI) and AI/ML training workloads (PyTorch, Jax, Tensorflow etc.) on Kubernetes.
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A Datacenter Scale Distributed Inference Serving Framework.
NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities.
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A Friendly Federated AI Framework.
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language.
Free & Open-Source AI Voice Generator.
A powerful, browser-based AI voice generator that lets you create natural-sounding voices without installing anything.
Use it directly in your browser or self-host it for your own applications with OpenAI API compatibility!
WAGMIOS is a self-hosted container management system with AI-powered automation. It enables you to efficiently manage your containers with W.I.L.L.O.W, an AI assistant that optimizes your workflow.
Evolving agents is a production-grade environment for orchestrating, evolving, and managing AI agents.
A production-grade framework for creating, managing, and evolving AI agents with intelligent agent-to-agent communication. The framework enables you to build collaborative agent ecosystems that can semantically understand requirements, evolve based on past experiences, and communicate effectively to solve complex tasks.
The Platform for Building Stateful Agents. Build agents with infinite context and human-like memory, that can learn from data and improve with experience. Letta (formerly MemGPT) is a framework for creating LLM services with memory.
👾 Letta is an open source framework for building stateful LLM applications. You can use Letta to build stateful agents with advanced reasoning capabilities and transparent long-term memory. The Letta framework is white box and model-agnostic.
Finding the Scaling Laws of Agents. The first and the best multi-agent framework.
🐫 CAMEL is an open-source community dedicated to finding the scaling laws of agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.
The framework enables multi-agent systems to continuously evolve by generating data and interacting with environments. This evolution can be driven by reinforcement learning with verifiable rewards or supervised learning.
superglue is an open-source server that sits as a layer between complex APIs and your application. With superglue, you always get the data that you want in the format that you expect. Fetch data from JSON and XML APIs, as well as CSV and Excel files in seconds.
The Open-Source LLM Evaluation Framework.
DeepEval is a simple-to-use, open-source LLM evaluation framework, for evaluating and testing large-language model systems. It is similar to Pytest but specialized for unit testing LLM outputs. DeepEval incorporates the latest research to evaluate LLM outputs based on metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., which uses LLMs and various other NLP models that runs locally on your machine for evaluation.
The TypeScript framework for agents & workflows with react-like components. Lightning fast dev loop. Easy to learn. Easy to extend.
Build complex AI applications with React-like components. GenSX is a simple typescript framework for building agents and workflows with reusable React-like components. GenSX takes a lot of inspiration from React, but the programming model is very different - it’s a Node.js framework designed for data flow.
Zonos-v0.1 is a leading open-weight text-to-speech model trained on more than 200k hours of varied multilingual speech, delivering expressiveness and quality on par with—or even surpassing—top TTS providers.
Our model enables highly natural speech generation from text prompts when given a speaker embedding or audio prefix, and can accurately perform speech cloning when given a reference clip spanning just a few seconds. The conditioning setup also allows for fine control over speaking rate, pitch variation, audio quality, and emotions such as happiness, fear, sadness, and anger. The model outputs speech natively at 44kHz.
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BirdNET-Analyzer is an open source tool for analyzing bird calls using machine learning models. It can process large amounts of audio recordings and identify (bird) species based on their calls.
The fast, Open Source and easy-to-use solver. Solve planning and scheduling problems with OptaPlanner.
A fast, easy-to-use, open source AI constraint solver for software developers
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build ml models in natural language and minimal code.
Create machine learning models with minimal code by describing what you want them to do in plain words. You explain the task, and the library builds a model for you, including data generation, feature engineering, training, and packaging.
For better or for worse, LLMs are here to stay. We all read content that they produce online, most of us interact with LLM chatbots, and many of us use them to produce content of our own.
In a series of five- to ten-minute lessons, we will explain what these machines are, how they work, and how to thrive in a world where they are everywhere.
You will learn when these systems can save you a lot of time and effort. You will learn when they are likely to steer you wrong. And you will discover how to see through the hype to tell the difference. ?
AI by Hand ✍️ Exercises in Excel
A Systems View of LLMs on TPUs.
This book aims to demystify the art of scaling LLMs on TPUs. We try to explain how TPUs work, how LLMs actually run at scale, and how to pick parallelism schemes during training and inference that avoid communication bottlenecks.
Open Universal Machine Intellingence. E2E Foundation Model Research Platform. Everything you need to build state-of-the-art foundation models, end-to-end.
Oumi is a fully open-source platform that streamlines the entire lifecycle of foundation models - from data preparation and training to evaluation and deployment. Whether you're developing on a laptop, launching large scale experiments on a cluster, or deploying models in production, Oumi provides the tools and workflows you need.
State-of-the-art Machine Learning for PyTorch, TensorFlow, and JAX.
🤗 Transformers provides APIs and tools to easily download and train state-of-the-art pretrained models. Using pretrained models can reduce your compute costs, carbon footprint, and save you the time and resources required to train a model from scratch.
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ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator.
ONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc.
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Easy, fast, and cheap LLM serving for everyone.
vLLM is a fast and easy-to-use library for LLM inference and serving.
Originally developed in the Sky Computing Lab at UC Berkeley, vLLM has evloved into a community-driven project with contributions from both academia and industry.
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- How to serve LLMs with vLLM and OVHcloud AI Deploy @ OVHcloud.
- Episode 616: From Boston to bootc @ Linux Unplugged.
- What is vLLM @ RedHat.
- Faire tourner un LLM localement sur votre ordinateur @ Quoi de neuf les devs ? :fr:.
- Inside vLLM: Anatomy of a High-Throughput LLM Inference System @ Aleksa Gordić blog.
- vLLM : Maîtriser l'Inference Haute Performance pour les LLM @ DevSecOps :fr:.
Open Repository of Web Crawl Data.
Common Crawl maintains a free, open repository of web crawl data that can be used by anyone.
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15 trillion tokens of the finest data the 🌐 web has to offer.
The 🍷 FineWeb dataset consists of more than 15T tokens of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the 🏭 datatrove library, our large scale data processing library.
🍷 FineWeb was originally meant to be a fully open replication of 🦅 RefinedWeb, with a release of the full dataset under the ODC-By 1.0 license. However, by carefully adding additional filtering steps, we managed to push the performance of 🍷 FineWeb well above that of the original 🦅 RefinedWeb, and models trained on our dataset also outperform models trained on other commonly used high quality web datasets (like C4, Dolma-v1.6, The Pile, SlimPajama, RedPajam2) on our aggregate group of benchmark tasks.
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Partner of Accounting Leaders. Generative AI platform for intelligent accounting.
The preferred partner of accounting leaders.
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The Annual Conference on Neural Information Processing Systems.
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Run AI with an API. Run and fine-tune open-source models. Deploy custom models at scale. All with one line of code.
Thousands of models contributed by our community. All the latest open-source models are on Replicate. They’re not just demos — they all actually work and have production-ready APIs.
AI shouldn’t be locked up inside academic papers and demos. Make it real by pushing it to Replicate.
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A fast tool to read text-based files in a repository or directory, chunk them, and serialize them for LLM consumption.