Search Results for "python framework" - Page 25
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Showing 1814 open source projects for "python framework"
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The Arcade Learning Environment
The Arcade Learning Environment (ALE) -- a platform for AI research
Arcade Learning Environment (ALE) is a widely used open-source framework that wraps hundreds of Atari 2600 games via an emulator and presents them as RL environments for AI agents. It decouples the game/emulation aspects from the agent interface, providing a clean API (C++, Python, Gymnasium) so researchers can focus on agent design rather than game plumbing. This environment suite has been central to many RL breakthroughs, including value-based agents, deep Q-nets, and general-agent benchmarking, because the Atari games span many genres and present diverse learning challenges (pixels, actions, delayed rewards). ... -
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Mesh R-CNN
code for Mesh R-CNN, ICCV 2019
Mesh R-CNN is a 3D reconstruction and object understanding framework developed by Facebook Research that extends Mask R-CNN into the 3D domain. Built on top of Detectron2 and PyTorch3D, Mesh R-CNN enables end-to-end 3D mesh prediction directly from single RGB images. The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. Unlike voxel-based or point-based approaches, Mesh... -
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Kaldi
kaldi-asr/kaldi is the official location of the Kaldi project
Kaldi is an open source toolkit for speech recognition research. It provides a powerful framework for building state-of-the-art automatic speech recognition (ASR) systems, with support for deep neural networks, Gaussian mixture models, hidden Markov models, and other advanced techniques. The toolkit is widely used in both academia and industry due to its flexibility, extensibility, and strong community support. Kaldi is designed for researchers who need a highly customizable environment to... -
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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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DualPipe
A bidirectional pipeline parallelism algorithm
DualPipe is a bidirectional pipeline parallelism algorithm open-sourced by DeepSeek, introduced in their DeepSeek-V3 technical framework. The main goal of DualPipe is to maximize overlap between computation and communication phases during distributed training, thus reducing idle GPU time (i.e. “pipeline bubbles”) and improving cluster efficiency. Traditional pipeline parallelism methods (e.g. 1F1B or staggered pipelining) leave gaps because forward and backward phases can’t fully overlap... -
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RecBole
A unified, comprehensive and efficient recommendation library
...We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. It can be installed from pip, conda and source, and is easy to use. We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation and sub-packages. -
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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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LLMStack
No-code multi-agent framework to build LLM Agents, workflows
LLMStack is a no-code platform for building generative AI agents, workflows and chatbots, connecting them to your data and business processes. Build tailor-made generative AI agents, applications and chatbots that cater to your unique needs by chaining multiple LLMs. Seamlessly integrate your own data, internal tools and GPT-powered models without any coding experience using LLMStack's no-code builder. Trigger your AI chains from Slack or Discord. Deploy to the cloud or on-premise. -
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ACI.dev
Open platform connecting AI agents to tools via unified MCP server
ACI is an open source platform designed to enable AI agents to interact with external tools through a unified and structured interface. It focuses on simplifying tool integration by connecting hundreds of pre-built services into agentic environments, allowing developers to avoid building custom API clients and authentication flows for each service. ACI provides intent-aware tool access, meaning agents can dynamically discover and use tools based on context rather than rigid configurations.... -
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xLSTM
Neural Network architecture based on ideas of the original LSTM
xLSTM is an open-source machine learning architecture that reimagines the classic Long Short-Term Memory (LSTM) network for modern large-scale language modeling and sequence processing tasks. The project introduces a new recurrent neural network design that incorporates exponential gating mechanisms and enhanced memory structures to overcome limitations of traditional LSTM models. By introducing innovations such as matrix-based memory and improved normalization techniques, xLSTM improves the... -
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bitsandbytes
Accessible large language models via k-bit quantization for PyTorch
bitsandbytes is an open-source library designed to make training and inference of large neural networks more efficient by dramatically reducing memory usage. Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources,... -
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USO
Open-sourced unified customization model
...By decoupling style and subject, USO enables reuse of learned style/style-embeddings across different subjects, or vice versa, which makes generation more modular and controllable. The project provides tooling (in Python) including inference and workflow scripts, example configurations, and support for generation pipelines; and as of 2025, USO is also natively supported in some mainstream generative-art UI pipelines (e.g. ComfyUI) to ease adoption. -
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Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
This repository provides a from-scratch, minimalist implementation of the Vision Transformer (ViT) in PyTorch, focusing on the core architectural pieces needed for image classification. It breaks down the model into patch embedding, positional encoding, multi-head self-attention, feed-forward blocks, and a classification head so you can understand each component in isolation. The code is intentionally compact and modular, which makes it easy to tinker with hyperparameters, depth, width, and... -
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Animated Drawings
Code to accompany "A Method for Animating Children's Drawings"
AnimatedDrawings is a framework that converts user sketches or line drawings into fully animated 2D motion sequences using learned motion priors. The idea is that you draw a simple static figure (stick figure, silhouette, or contour lines), and the system produces plausible skeletal motion (walking, jumping, dancing) that adheres to the drawn shape constraints. The architecture separates shape embedding (to understand user-drawn geometry) from motion embedding / generation (to produce... -
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Prompt Declaration Language
Prompt Declaration Language is a declarative prompt programming lang
LLMs will continue to change the way we build software systems. They are not only useful as coding assistants, providing snipets of code, explanations, and code transformations, but they can also help replace components that could only previously be achieved with rule-based systems. Whether LLMs are used as coding assistants or software components, reliability remains an important concern. LLMs have a textual interface and the structure of useful prompts is not captured formally. Programming... -
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openctp
Provides CTP stock options and Zhongtai Securities XTP
openctp is a technical service platform built around the CTP trading ecosystem that provides CTP compatible interfaces for a wide range of brokerage backends and markets. Its core idea is to wrap heterogeneous stock and derivatives trading gateways such as Zhongtai XTP, Huaxin Qidian TORA, and others with CTPAPI compatible interfaces, so existing CTP programs can connect simply by swapping dynamic libraries rather than rewriting code. The project offers a comprehensive simulation environment... -
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101 FastAPI Tips
FastAPI Tips by The FastAPI Expert!
...Many entries explain not just what to do, but why a certain pattern is better in FastAPI or Starlette-based applications. The repository is best used as a checklist, reference, and learning guide for building cleaner Python APIs. -
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Step1X-Edit
A SOTA open-source image editing model
Step1X-Edit is a state-of-the-art open-source image editing model/framework that uses a multimodal large language model (LLM) together with a diffusion-based image decoder to let users edit images simply via natural-language instructions plus a reference image. You supply an existing image and a textual command — e.g. “add a ruby pendant on the girl’s neck” or “make the background a sunset over mountains” — and the model interprets the instruction, computes a latent embedding combining the... -
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SAHI
A lightweight vision library for performing large object detection
A lightweight vision library for performing large-scale object detection & instance segmentation. Object detection and instance segmentation are by far the most important fields of applications in Computer Vision. However, detection of small objects and inference on large images are still major issues in practical usage. Here comes the SAHI to help developers overcome these real-world problems with many vision utilities. Detection of small objects and objects far away in the scene is a major... -
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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... -
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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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MusicGPT
Generate music based on natural language prompts using LLMs
MusicGPT is an open-source application designed to generate music from natural language prompts using locally executed artificial intelligence models. The software allows users to run advanced music generation systems directly on their own devices without requiring heavy dependencies such as Python or full machine learning frameworks. Instead, it provides a lightweight environment capable of executing music generation models locally on CPUs or GPUs while maintaining strong performance across... -
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Agents 2.0
An Open-source Framework for Data-centric Language Agents
Agents is an open-source framework designed to build and train autonomous language agents through a data-centric and learning-oriented architecture. The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During... -
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XGBoost
Scalable and Flexible Gradient Boosting
...It supports regression, classification, ranking and user defined objectives, and runs on all major operating systems and cloud platforms. XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems. XGBoost can be used for Python, Java, Scala, R, C++ and more. It can run on a single machine, Hadoop, Spark, Dask, Flink and most other distributed environments, and is capable of solving problems beyond billions of examples. -
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Neuro SAN Studio
A playground for neuro-san
Neuro SAN Studio is a development environment and playground for building, testing, and deploying multi-agent AI systems using the Neuro SAN framework. It provides a hands-on interface where users can design agent networks, run experiments, and observe how multiple agents collaborate to solve complex tasks. The platform is built around a data-driven approach, where entire agent systems can be defined using configuration files rather than extensive code, making it accessible to both...
