Search Results for "mac framework" - Page 29
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Showing 1279 open source projects for "mac framework"
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GenAI Agents
Implementations for various Generative AI Agent techniques
GenAI Agents is a large, tutorial-driven repository that teaches you how to design, build, and experiment with generative AI agents. It spans a spectrum from simple conversational bots and basic question-answering agents to complex multi-agent systems that coordinate on research, education, business workflows, and creative tasks. The implementations leverage modern frameworks such as LangChain, LangGraph, AutoGen, PydanticAI, CrewAI, and more, showing how each can be wired into realistic... -
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Brokk
Brokk brings code intelligence to AI
Brokk is a code intelligence assistant framework designed to let large language models (LLMs) understand code semantically (not just as raw text) so that they can work effectively on large codebases that don’t fit wholly in a prompt context. It helps bridge the gap between LLMs and real-world engineering code by offering tooling to index, analyze, query, and augment code context, so that AI can meaningfully reason about existing code, suggest edits, and navigate across projects. Modular... -
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OpenAI Swarm
Educational framework exploring multi-agent orchestration
Swarm focuses on making agent coordination and execution lightweight, highly controllable, and easily testable. It accomplishes this through two primitive abstractions; Agents and handoffs. An Agent encompasses instructions and tools, and can at any point choose to hand off a conversation to another Agent. These primitives are powerful enough to express rich dynamics between tools and networks of agents, allowing you to build scalable, real-world solutions while avoiding a steep learning... -
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PaddleNLP
Easy-to-use and powerful NLP library with Awesome model zoo
PaddleNLP It is a natural language processing development library for flying paddles, with Easy-to-use text area API, Examples of applications for multiple scenarios, and High-performance distributed training Three major features, aimed at improving the modeling efficiency of the flying oar developer's text field, aiming to improve the developer's development efficiency in the text field, and provide rich examples of NLP applications. Provide rich industry-level pre-task capabilities... -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic... -
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Thinc
A refreshing functional take on deep learning
Thinc is a lightweight deep learning library that offers an elegant, type-checked, functional-programming API for composing models, with support for layers defined in other frameworks such as PyTorch, TensorFlow and MXNet. You can use Thinc as an interface layer, a standalone toolkit or a flexible way to develop new models. Previous versions of Thinc have been running quietly in production in thousands of companies, via both spaCy and Prodigy. We wrote the new version to let users compose,... -
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Errbot
Chatbot daemon that connects to your favorite chat services
Errbot is a chatbot, a daemon that connects to your favorite chat service and brings your tools into the conversation. The goal of the project is to make it easy for you to write your own plugins so you can make it do whatever you want, a deployment, retrieving some information online, trigger a tool via an API, troll a co-worker, etc. Errbot is being used in a lot of different contexts, chatops (tools for devops), online gaming chatrooms like EVE, video streaming chatrooms like... -
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ML.NET
Open source and cross-platform machine learning framework for .NET
With ML.NET, you can create custom ML models using C# or F# without having to leave the .NET ecosystem. ML.NET lets you re-use all the knowledge, skills, code, and libraries you already have as a .NET developer so that you can easily integrate machine learning into your web, mobile, desktop, games, and IoT apps. ML.NET offers Model Builder (a simple UI tool) and ML.NET CLI to make it super easy to build custom ML Models. These tools use Automated ML (AutoML), a cutting edge technology that... -
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1D Visual Tokenization and Generation
This repo contains the code for 1D tokenizer and generator
The 1D Visual Tokenization and Generation project from ByteDance introduces a novel “one-dimensional” tokenizer designed for images: instead of representing images with large grids of 2D tokens (as in many prior generative/image-modeling systems), it compresses images into as few as 32 discrete tokens (or more, optionally) — thereby achieving a very compact, efficient representation that drastically speeds up generation and reconstruction while retaining strong fidelity. This compact... -
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Matrix
Multi-Agent daTa geneRation Infra and eXperimentation framework
Matrix is a distributed, large-scale engine for multi-agent synthetic data generation and experiments: it provides the infrastructure to run thousands of “agentic” workflows concurrently (e.g. multiple LLMs interacting, reasoning, generating content, data-processing pipelines) by leveraging distributed computing (like Ray + cluster management). The idea is to treat data generation as a “data-to-data” transformation: each input item defines a task, and the runtime orchestrates asynchronous,... -
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Agent Stack
Deploy and share agents with open infrastructure
Agent Stack is an open infrastructure platform designed to take AI agents from prototype to production, no matter how they were built. It includes a runtime environment, multi-tenant web UI, catalog of agents, and deployment flow that seeks to remove vendor lock-in and provide greater autonomy. Under the hood it’s built on the “Agent2Agent” (A2A) protocol, enabling interoperability between different agent ecosystems, runtime services, and frameworks. The platform supports agents built in... -
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PyTorch/XLA
Enabling PyTorch on Google TPU
PyTorch/XLA is a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. You can try it right now, for free, on a single Cloud TPU with Google Colab, and use it in production and on Cloud TPU Pods with Google Cloud. Take a look at one of our Colab notebooks to quickly try different PyTorch networks running on Cloud TPUs and learn how to use Cloud TPUs as PyTorch devices. We are also introducing new TPU VMs for more transparent... -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. 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... -
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Jina
Build cross-modal and multimodal applications on the cloud
Jina is a framework that empowers anyone to build cross-modal and multi-modal applications on the cloud. It uplifts a PoC into a production-ready service. Jina handles the infrastructure complexity, making advanced solution engineering and cloud-native technologies accessible to every developer. Build applications that deliver fresh insights from multiple data types such as text, image, audio, video, 3D mesh, PDF with Jina AI’s DocArray. Polyglot gateway that supports gRPC, Websockets, HTTP,... -
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GLM-4.5V
GLM-4.6V/4.5V/4.1V-Thinking, towards versatile multimodal reasoning
GLM-4.5V is the preceding iteration in the GLM-V series that laid much of the groundwork for general multimodal reasoning and vision-language understanding. It embodies the design philosophy of mixing visual and textual modalities into a unified model capable of general-purpose reasoning, content understanding, and generation, while already supporting a wide variety of tasks: from image captioning and visual question answering to content recognition, GUI-based agents, video understanding,... -
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UI-TARS
UI-TARS-desktop version that can operate on your local personal device
UI-TARS is an open-source multimodal “GUI agent” created by ByteDance: a model designed to perceive raw screenshots (or rendered UI frames), reason about what needs to be done, and then perform real interactions with graphical user interfaces (GUIs) — like clicking, typing, navigating menus — across desktop, browser, mobile, or game environments. Rather than relying on rigid, manually scripted UI automation, UI-TARS uses a unified vision-language model (VLM) that integrates perception,... -
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DragonianVoice
C++ inference library for multiple SVC/TTS
DragonianVoice is a C++ inference library that unifies multiple speech synthesis, voice conversion, and singing voice synthesis models under a single, high-performance ONNX-based framework. It focuses on being a reusable native library rather than a full UI product, with bindings for C, C++, and C# so it can be embedded into other applications or engines. The project supports a wide range of model families: TTS models such as Tacotron2, VITS, EmotionalVITS, BERTVits2, GPT-SoVITS, SVC systems... -
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mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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GELab-Zero
GUI Exploration Lab. One of the best GUI agent solutions
GELab-Zero is an open-source “GUI Agent” framework aiming to automate interactions with graphical user interfaces (GUIs), combining both the agent model and all supporting infrastructure — including inference, input orchestration, and GUI automation logic — in a plug-and-play package that runs locally, without cloud dependencies. The idea is to let developers or users harness an AI agent that can simulate clicking, typing, reading UI elements, and interacting with apps in a human-like way... -
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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... -
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MIVisionX
Set of comprehensive computer vision & machine intelligence libraries
MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. AMD MIVisionX delivers highly optimized open-source implementation of the Khronos OpenVX™ and OpenVX™ Extensions along with Convolution Neural Net Model Compiler & Optimizer supporting ONNX, and Khronos NNEF™ exchange formats. The toolkit allows for rapid prototyping and deployment of optimized computer vision and machine learning... -
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FATE
An industrial grade federated learning framework
FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms,... -
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Evolutionary Computation Framework
C++ framework for application of any type of evolutionary computation.
ECF is a framework intended for application of any type of evolutionary computation (GA/GP, DE, Clonalg, ES, PSO, ABC, GAn, local search...). It offers simplicity for the end-user (parameterless usage, tutorial) and customization for experienced EC practicioners. -
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CRAB
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model
CRAB (Composable and Reusable Autonomous Bots) is a framework for building modular, reusable AI agents that can perform complex tasks in various domains. It focuses on creating AI-driven workflows that can be composed of multiple autonomous agents working together.
