Search Results for "frameworks" - Page 22
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Showing 1301 open source projects for "frameworks"
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Infinity
Low-latency REST API for serving text-embeddings
Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. Infinity is developed under MIT License. Infinity powers inference behind Gradient.ai and other Embedding API providers. -
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Pants Build System
The Pants Build System
Pants 2 is a fast, scalable, user-friendly build system for codebases of all sizes. It's currently focused on Python, Go, Java, Scala, Kotlin, Shell, and Docker, with support for other languages and frameworks coming soon. A lot of effort has gone into making Pants easy to adopt, easy to use and easy to extend. We're super excited to bring Pants' distinctive features to Go, Java, Python, Scala, Kotlin, and Shell users. Pants requires very minimal BUILD file metadata/boilerplate. It uses a combination of static analysis and sensible defaults to infer most of that information on the fly. ... -
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LLaMA-Mesh
Unifying 3D Mesh Generation with Language Models
LLaMA-Mesh is a research framework that extends large language models so they can understand and generate 3D mesh data alongside text. The system introduces a method for representing 3D meshes in a textual format by encoding vertex coordinates and face definitions as sequences that can be processed by a language model. By serializing 3D geometry into text tokens, the approach allows existing transformer architectures to generate and interpret 3D models without requiring specialized visual... -
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NVIDIA Generative AI Examples
Generative AI reference workflows
NVIDIA GenerativeAIExamples is an open-source repository that provides practical reference implementations and example workflows for building generative AI applications using NVIDIA’s software ecosystem. The project is designed to help developers accelerate the development of AI applications by providing ready-to-run pipelines, notebooks, and tools that demonstrate how to integrate large language models into real-world systems. The repository includes examples covering topics such as... -
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MAI-UI
Real-World Centric Foundation GUI Agents
...Developed by Tongyi-MAI (Alibaba’s research initiative), the MAI-UI models are multimodal agents trained to understand user instructions and corresponding screenshots, grounding those instructions to on-screen elements and generating sequences of GUI actions such as taps, swipes, text input, and system commands. Unlike traditional UI frameworks, MAI-UI emphasizes realistic deployment by supporting agent–user interaction (clarifying ambiguous instructions), integration with external tool APIs using MCP calls, and a device–cloud collaboration mechanism that dynamically routes computation to on-device or cloud models based on task state and privacy constraints. -
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Blik
Self hosted & open source anonymous 360 review software
Blik is an open source, self-hosted platform for anonymous 360-degree feedback and performance review cycles, built with a strong focus on privacy, transparency, and developer-friendly deployment. It allows organizations to run review cycles where employees get anonymous feedback from peers, managers, and direct reports, without vendor lock-in or per-user licensing costs. The system is built using the Django framework, with a PostgreSQL backend, and is packaged for easy deployment via... -
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Playground Cheatsheet for Python
Playground and cheatsheet for learning Python
...Because it is organized in bite-sized chunks, it’s ideal for beginners or people refreshing their Python skills who want to revisit syntax and common patterns before moving into larger frameworks or applications. It also supports usage as a reference: if you forgot how a list comprehension works or how decorators behave, you can quickly open the relevant script. -
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SuperDuperDB
Integrate, train and manage any AI models and APIs with your database
Build and manage AI applications easily without needing to move your data to complex pipelines and specialized vector databases. Integrate AI and vector search directly with your database including real-time inference and model training. Just using Python. A single scalable deployment of all your AI models and APIs which is automatically kept up-to-date as new data is processed immediately. No need to introduce an additional database and duplicate your data to use vector search and build on... -
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Deep Lake
Data Lake for Deep Learning. Build, manage, and query datasets
Deep Lake (or Deeplake, formerly known as Activeloop Hub) is a data lake for deep learning applications. Our open-source dataset format is optimized for rapid streaming and querying of data while training models at scale, and it includes a simple API for creating, storing, and collaborating on AI datasets of any size. It can be deployed locally or in the cloud, and it enables you to store all of your data in one place, ranging from simple annotations to large videos. Deep Lake is used by... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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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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mcp-use
A solution to build and deploy MCP agents and applications
...It simplifies authentication, access control, audit logging, observability, sandboxed runtime environments, and deployment workflows, whether self-hosted or managed, making MCP development production-ready. With integrations for popular frameworks like LangChain (Python) and LangChain.js (TypeScript), mcp-use accelerates the creation of tool-enabled AI agents. -
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Protenix
A trainable PyTorch reproduction of AlphaFold 3
Protenix is an open-source, trainable PyTorch reimplementation of AlphaFold 3, developed by ByteDance with the goal of democratizing high-accuracy protein structure prediction for computational biology and drug-discovery research. Protenix provides a complete pipeline for turning protein sequences (with optional MSA / sequence alignment) or structural inputs (e.g. PDB/CIF) into full 3D atomic-level structure predictions. It supports both “full” models and lightweight variants such as... -
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Shumai
Fast Differentiable Tensor Library in JavaScript & TypeScript with Bun
...It allows seamless integration of machine learning, deep learning, and custom differentiable programs into web-based or server-side environments without relying on Python frameworks. The library supports matrix operations, gradient computation, and tensor conversions with intuitive APIs and near-native speed, thanks to Bun’s low-overhead FFI bindings. It can automatically leverage GPU acceleration on Linux (via CUDA) and CPU computation on macOS. -
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Physical Symbolic Optimization (Φ-SO)
Physical Symbolic Optimization
Physical Symbolic Optimization (Φ-SO) - A symbolic optimization package built for physics. Symbolic regression module uses deep reinforcement learning to infer analytical physical laws that fit data points, searching in the space of functional forms. -
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imgclsmob Deep learning networks
Sandbox for training deep learning networks
...It includes implementations of models used for tasks such as image classification, object detection, semantic segmentation, and pose estimation. The repository also contains scripts that help train models, evaluate performance, and convert trained networks between different frameworks. Several deep learning frameworks are supported, allowing researchers to experiment with architectures in different environments. The project is frequently used by developers who want to study modern convolutional neural network designs and compare their performance across datasets. -
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OSWorld
Benchmarking Multimodal Agents for Open-Ended Tasks
OSWorld is an open-source synthetic world environment designed for embodied AI research and multi-agent learning. It provides a richly simulated 3D world where multiple agents can interact, perform tasks, and learn complex behaviors. OSWorld emphasizes multi-modal interaction, enabling agents to process visual, auditory, and symbolic data for grounded learning in a simulated world. -
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OpenCV
Open Source Computer Vision Library
The Open Source Computer Vision Library has >2500 algorithms, extensive documentation and sample code for real-time computer vision. It works on Windows, Linux, Mac OS X, Android, iOS in your browser through JavaScript. Languages: C++, Python, Julia, Javascript Homepage: https://opencv.org Q&A forum: https://forum.opencv.org/ Documentation: https://docs.opencv.org Source code: https://github.com/opencv Please pay special attention to our tutorials!... -
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django-split-settings
Organize Django settings into multiple files and directories
Organize Django settings into multiple files and directories. Easily override and modify settings. Use wildcards in settings file paths and mark settings files as optional. Managing Django’s settings might be tricky. There are severals issues which are encountered by any Django developer along the way. First one is caused by the default project structure. Django clearly offers us a single settings.py file. It seams reasonable at the first glance. And it is actually easy to use just after the... -
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Maestro Framework
A framework for Claude Opus to intelligently orchestrate subagents
Maestro Framework is a Python framework for orchestrating AI subagents across complex tasks. It breaks a user objective into smaller subtasks, assigns those subtasks to worker models, and refines the results into a final output. The original workflow used Claude Opus and Haiku, while newer variants support Claude 3.5 Sonnet, GPT models, Gemini, Cohere, Groq, LM Studio, and Ollama through different scripts and LiteLLM support. It can maintain context between subtasks so later steps can build... -
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TAME LLM
Traditional Mandarin LLMs for Taiwan
...These models are designed to support applications such as conversational AI, knowledge retrieval, and domain-specific reasoning in fields like manufacturing, law, healthcare, and electronics. The training pipeline leverages high-performance computing infrastructure and frameworks such as NVIDIA NeMo and Megatron to enable large-scale model training. Taiwan-LLM aims to improve language understanding and generation for Traditional Mandarin users by incorporating region-specific datasets and evaluation benchmarks. -
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GHunt
Offensive Google framework
GHunt (v2) is an offensive Google framework, designed to evolve efficiently. It's currently focused on OSINT, but any use related with Google is possible. It will automatically use venvs to avoid dependency conflicts with other projects. First, launch the listener by doing ghunt login and choose between 1 of the 2 first methods. Put GHunt on listening mode (currently not compatible with docker) Paste base64-encoded cookies. Enter manually all cookies. The development of this extension has... -
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REST APIs with Flask and Python
Projects and e-book for our course, REST APIs with Flask and Python
A full course to teach you how to use Flask and Python to make REST APIs using multiple Flask extensions and PostgreSQL. Learn Flask, Docker, PostgreSQL, and more. Build professional-grade REST APIs with Python. No more outdated tutorials. Use Python 3.10+ and the latest versions of every Flask extension and library. Run your apps in Docker, host your code with Git, write documentation with Swagger, and test your APIs while developing. Learn how to perform user authentication using JWTs and... -
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Arianne RPG
Arianne is an engine to develop multiplayer online games like Stendhal
Arianne is a multiplayer online engine to develop turn based and real time games, providing a simple way of creating the game server rules and clients like our MORPG Stendhal. Our engine Marauroa uses Java and SQL for hosting hundreds of players on a solo host. Stendhal is a fun friendly and free multiplayer online adventure game with a huge and rich world. With hundreds of NPCs and different quests, features like the Travel log and achievement system keep track of progress. Thousands of... -
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NeuralProphet
A simple forecasting package
NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in... -
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AutoGroq
Revolutionizes the way users interact with Autogen
AutoGroq is a groundbreaking tool that revolutionizes the way users interact with Autogen™ and other AI assistants. By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency. AutoGroq was born out of the realization that the traditional approach to building AI agents was backwards. Instead of creating agents in anticipation...
