Showing 2623 open source projects for "minecraft(linux)"

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  • 1
    RSS to Telegram Bot

    RSS to Telegram Bot

    A Telegram RSS bot that cares about your reading experience

    A Telegram RSS bot that cares about your reading experience.
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  • 2
    BudouX

    BudouX

    Standalone, small, language-neutral

    Standalone. Small. Language-neutral. BudouX is the successor to Budou, the machine learning-powered line break organizer tool. It is standalone. It works with no dependency on third-party word segmenters such as Google cloud natural language API. It is small. It takes only around 15 KB including its machine learning model. It's reasonable to use it even on the client-side. It is language-neutral. You can train a model for any language by feeding a dataset to BudouX’s training...
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  • 3
    TFX

    TFX

    TFX is an end-to-end platform for deploying production ML pipelines

    TensorFlow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines. Both the components themselves and the integrations with orchestration systems can be extended. TFX components interact with an ML Metadata backend that keeps a record of component runs, input and output artifacts, and...
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  • 4
    Nixtla TimeGPT

    Nixtla TimeGPT

    TimeGPT-1: production ready pre-trained Time Series Foundation Model

    TimeGPT is a production ready, generative pretrained transformer for time series. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code. Whether you're a bank forecasting market trends or a startup predicting product demand, TimeGPT democratizes access to cutting-edge predictive insights, eliminating the need for a dedicated team of machine learning engineers. A generative model for time series. TimeGPT is capable of...
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  • 5
    Chronos Forecasting

    Chronos Forecasting

    Pretrained (Language) Models for Probabilistic Time Series Forecasting

    Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as...
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  • 6
    HDBSCAN

    HDBSCAN

    A high performance implementation of HDBSCAN clustering

    HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster...
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  • 7
    TextAttack

    TextAttack

    Python framework for adversarial attacks, and data augmentation

    Generating adversarial examples for NLP models. TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
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  • 8
    supervision

    supervision

    We write your reusable computer vision tools

    We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us.
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  • 9
    omegaml

    omegaml

    MLOps simplified. From ML Pipeline ⇨ Data Product without the hassle

    omega|ml is the innovative Python-native MLOps platform that provides a scalable development and runtime environment for your Data Products. Works from laptop to cloud.
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    Build Securely on AWS with Proven Frameworks

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  • 10
    imodelsX

    imodelsX

    Interpretable prompting and models for NLP

    Interpretable prompting and models for NLP (using large language models). Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better...
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  • 11
    Denoising Diffusion Probabilistic Model

    Denoising Diffusion Probabilistic Model

    Implementation of Denoising Diffusion Probabilistic Model in Pytorch

    Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model.
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  • 12
    OpenChronicle

    OpenChronicle

    Open-source, local-first memory for any tool-capable LLM agent

    OpenChronicle is a knowledge management and storytelling platform designed to organize information into structured timelines and interconnected narratives. It allows users to create chronological records that link events, ideas, and entities in a cohesive format. The system emphasizes visualization and organization of complex information over time. It can be used for research, writing, or personal knowledge tracking. OpenChronicle supports extensibility, enabling customization of how data is...
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  • 13
    Screenshot to Code

    Screenshot to Code

    A neural network that transforms a design mock-up into static websites

    Screenshot-to-code is a tool or prototype that attempts to convert UI screenshots (e.g., of mobile or web UIs) into code representations, likely generating layouts, HTML, CSS, or markup from image inputs. It is part of a research/proof-of-concept domain in UI automation and image-to-UI code generation. Mapping visual design to code constructs. Code/UI layout (HTML, CSS, or markup). Examples/demo scripts showing “image UI code”.
    Downloads: 0 This Week
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  • 14
    TreeQuest

    TreeQuest

    A Tree Search Library with Flexible API for LLM Inference-Time Scaling

    TreeQuest, developed by SakanaAI, is a versatile Python library implementing adaptive tree search algorithms—such as AB‑MCTS—for enhancing inference-time performance of large language models (LLMs). It allows developers to define custom state-generation and scoring functions (e.g., via LLMs), and then efficiently explores possible answer trees during runtime. With support for multi-LLM collaboration, checkpointing, and mixed policies, TreeQuest enables smarter, trial‑and‑error question...
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  • 15
    Airweave

    Airweave

    Airweave lets agents search any app

    Airweave is an open-source platform that enables agents to semantically search across various applications, databases, and APIs. By transforming disparate data sources into a unified, searchable knowledge base, Airweave facilitates intelligent information retrieval through REST APIs or the MCP protocol. It's particularly useful for building AI agents that require access to structured and unstructured data across multiple platforms.
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  • 16
    Magentic UI

    Magentic UI

    A research prototype of a human-centered web agent

    Magentic-UI is a research prototype developed by Microsoft that serves as a human-centered interface powered by a multi-agent system. It enables users to automate complex web tasks, such as browsing, form filling, and data analysis, while maintaining control over the process. The system emphasizes transparency and user involvement, making it suitable for tasks requiring both automation and human oversight.
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  • 17
    LitGPT

    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.
    Downloads: 0 This Week
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  • 18
    ManiSkill

    ManiSkill

    SAPIEN Manipulation Skill Framework

    ManiSkill is a benchmark platform for training and evaluating reinforcement learning agents on dexterous manipulation tasks using physics-based simulations. Developed by Hao Su Lab, it focuses on robotic manipulation with diverse, high-quality 3D tasks designed to challenge perception, control, and planning in robotics. ManiSkill provides both low-level control and visual observation spaces for realistic learning scenarios.
    Downloads: 0 This Week
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  • 19
    PilottAI

    PilottAI

    Python framework for building scalable multi-agent systems

    pilottai is an AI-based autonomous drone navigation system utilizing reinforcement learning for real-time decision-making. It is designed for simulating and training drones to fly safely through dynamic environments using AI-based controllers.
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  • 20
    FastAgency

    FastAgency

    The fastest way to bring multi-agent workflows to production

    FastAgency is a framework that simplifies the creation and deployment of AI-driven automation agents. It provides a structured environment for developing AI assistants capable of handling various business and technical tasks.
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  • 21
    CRAB

    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.
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  • 22
    DataProfiler

    DataProfiler

    Extract schema, statistics and entities from datasets

    DataProfiler is an AI-powered tool for automatic data analysis and profiling, designed to detect patterns, anomalies, and schema inconsistencies in structured and unstructured datasets. The DataProfiler is a Python library designed to make data analysis, monitoring, and sensitive data detection easy. Loading Data with a single command, the library automatically formats & loads files into a DataFrame. Profiling the Data, the library identifies the schema, statistics, entities (PII / NPI), and...
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  • 23
    Automatic text summarizer

    Automatic text summarizer

    Module for automatic summarization of text documents and HTML pages

    Sumy is an automatic text summarization library that provides multiple algorithms for extracting key content from documents and articles. Simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains a simple evaluation framework for text summaries. Implemented summarization methods are described in the documentation. I also maintain a list of alternative implementations of the summarizers in various programming languages.
    Downloads: 0 This Week
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  • 24
    LlamaParse

    LlamaParse

    Parse files for optimal RAG

    LlamaParse is a GenAI-native document parser that can parse complex document data for any downstream LLM use case (RAG, agents). Load in 160+ data sources and data formats, from unstructured, and semi-structured, to structured data (API's, PDFs, documents, SQL, etc.) Store and index your data for different use cases. Integrate with 40+ vector stores, document stores, graph stores, and SQL db providers.
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  • 25
    Prompt flow

    Prompt flow

    Build high-quality LLM apps

    Prompt flow is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, and evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.
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