Showing 1550 open source projects for "compiler python linux"

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  • 1
    Oumi

    Oumi

    Everything you need to build state-of-the-art foundation models

    Oumi is an open-source framework that provides everything needed to build state-of-the-art foundation models, end-to-end. It aims to simplify the development of large-scale machine-learning models.
    Downloads: 4 This Week
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  • 2
    uAgents

    uAgents

    A fast and lightweight framework for creating decentralized agents

    uAgents is a library developed by Fetch.ai that allows for creating autonomous AI agents in Python. With simple and expressive decorators, you can have an agent that performs various tasks on a schedule or takes action on various events.
    Downloads: 4 This Week
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  • 3
    Browser Use MCP Server

    Browser Use MCP Server

    Browse the web, directly from Cursor etc.

    A browser automation server implementing the Model Context Protocol, designed to allow AI assistants to browse the web directly from applications like Cursor. It supports natural language commands for web navigation and interaction. ​
    Downloads: 4 This Week
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  • 4
    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: 4 This Week
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    TextWorld

    TextWorld

    ​TextWorld is a sandbox learning environment for the training

    TextWorld is a learning environment designed to train reinforcement learning agents to play text-based games, where actions and observations are entirely in natural language. Developed by Microsoft Research, TextWorld focuses on language understanding, planning, and interaction in complex, narrative-driven environments. It generates games procedurally, enabling scalable testing of agents’ natural language processing and decision-making abilities.
    Downloads: 4 This Week
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  • 6
    Browser Use

    Browser Use

    Make websites accessible for AI agents

    Browser-Use is a framework that makes websites accessible for AI agents, enabling automated interactions and data extraction from web pages.
    Downloads: 4 This Week
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  • 7
    Parlant

    Parlant

    The behavior guidance framework for customer-facing LLM agents

    Parlant is a lightweight speech-to-text and text-to-speech framework designed for real-time AI-driven voice applications.
    Downloads: 4 This Week
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  • 8
    ChatGLM.cpp

    ChatGLM.cpp

    C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)

    ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware.
    Downloads: 4 This Week
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  • 9
    embedchain

    embedchain

    Framework to easily create LLM powered bots over any dataset

    Embedchain is a framework to easily create LLM-powered bots over any dataset. If you want a javascript version, check out embedchain-js. Embedchain empowers you to create chatbot models similar to ChatGPT, using your own evolving dataset. Start building LLM powered bots under 30 seconds.
    Downloads: 4 This Week
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  • 10
    x-transformers

    x-transformers

    A simple but complete full-attention transformer

    A simple but complete full-attention transformer with a set of promising experimental features from various papers. Proposes adding learned memory key/values prior to attending. They were able to remove feedforwards altogether and attain a similar performance to the original transformers. I have found that keeping the feedforwards and adding the memory key/values leads to even better performance. Proposes adding learned tokens, akin to CLS tokens, named memory tokens, that is passed through...
    Downloads: 4 This Week
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  • 11
    DeepLabCut

    DeepLabCut

    Implementation of DeepLabCut

    ... or for multi-animals. Please see the original paper and the latest work below! This package is collaboratively developed by the Mathis Group & Mathis Lab at EPFL (releases prior to 2.1.9 were developed at Harvard University). The code is freely available and easy to install in a few clicks with Anaconda (and pypi). DeepLabCut is an open-source Python package for animal pose estimation.
    Downloads: 4 This Week
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  • 12
    AutoDev

    AutoDev

    The AI-powered coding wizard

    The AI-powered coding wizard with multilingual support, auto code generation, and a helpful bug-slaying assistant. Customizable prompts and a magic Auto Dev/Testing/Document/Agent feature are included.
    Downloads: 4 This Week
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  • 13
    tslearn

    tslearn

    The machine learning toolkit for time series analysis in Python

    The machine learning toolkit for time series analysis in Python. tslearn expects a time series dataset to be formatted as a 3D numpy array. The three dimensions correspond to the number of time series, the number of measurements per time series and the number of dimensions respectively (n_ts, max_sz, d). In order to get the data in the right format.
    Downloads: 3 This Week
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  • 14
    CAMEL AI

    CAMEL AI

    Finding the Scaling Law of Agents. A multi-agent framework

    The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their "cognitive" processes. To address the challenges of achieving autonomous...
    Downloads: 4 This Week
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  • 15
    Langroid

    Langroid

    Harness LLMs with Multi-Agent Programming

    Given the remarkable abilities of recent Large Language Models (LLMs), there is an unprecedented opportunity to build intelligent applications powered by this transformative technology. The top question for any enterprise is: how best to harness the power of LLMs for complex applications? For technical and practical reasons, building LLM-powered applications is not as simple as throwing a task at an LLM system and expecting it to do it. Effectively leveraging LLMs at scale requires a...
    Downloads: 4 This Week
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  • 16
    CrewAI

    CrewAI

    Framework for orchestrating role-playing, autonomous AI agents

    Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks. The power of AI collaboration has too much to offer. CrewAI is designed to enable AI agents to assume roles, share goals, and operate in a cohesive unit - much like a well-oiled crew. Whether you're building a smart assistant platform, an automated customer service ensemble, or a multi-agent research team, CrewAI...
    Downloads: 4 This Week
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  • 17
    gpt-engineer

    gpt-engineer

    Full stack AI software engineer

    gpt-engineer is an open-source platform designed to help developers automate the software development process using natural language. The platform allows users to specify software requirements in plain language, and the AI generates and executes the corresponding code. It can also handle improvements and iterative development, giving users more control over the software they’re building. Built with a terminal-based interface, gpt-engineer is customizable, enabling developers to experiment...
    Downloads: 4 This Week
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  • 18
    MLflow

    MLflow

    Open source platform for the machine learning lifecycle

    MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud).
    Downloads: 4 This Week
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  • 19
    PaddleX

    PaddleX

    PaddlePaddle End-to-End Development Toolkit

    PaddleX is a deep learning full-process development tool based on the core framework, development kit, and tool components of Paddle. It has three characteristics opening up the whole process, integrating industrial practice, and being easy to use and integrate. Image classification and labeling is the most basic and simplest labeling task. Users only need to put pictures belonging to the same category in the same folder. When the model is trained, we need to divide the training set, the...
    Downloads: 4 This Week
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  • 20
    AIMET

    AIMET

    AIMET is a library that provides advanced quantization and compression

    Qualcomm Innovation Center (QuIC) is at the forefront of enabling low-power inference at the edge through its pioneering model-efficiency research. QuIC has a mission to help migrate the ecosystem toward fixed-point inference. With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware...
    Downloads: 4 This Week
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  • 21
    Deepchecks

    Deepchecks

    Test Suites for validating ML models & data

    Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate...
    Downloads: 4 This Week
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  • 22
    MONAI

    MONAI

    AI Toolkit for Healthcare Imaging

    The MONAI framework is the open-source foundation being created by Project MONAI. MONAI is a freely available, community-supported, PyTorch-based framework for deep learning in healthcare imaging. It provides domain-optimized foundational capabilities for developing healthcare imaging training workflows in a native PyTorch paradigm. Project MONAI also includes MONAI Label, an intelligent open source image labeling and learning tool that helps researchers and clinicians collaborate, create...
    Downloads: 4 This Week
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  • 23
    lightning AI

    lightning AI

    The most intuitive, flexible, way for researchers to build models

    Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any...
    Downloads: 4 This Week
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  • 24
    YYeTsBot

    YYeTsBot

    Renren Film and Television bot, fully connected to Renren resources

    Renren Film and Television bot, fully connected to all Renren Film and television resources without deletion of resources. The database of this site is permanently open source and free. You can directly send the name of the episode you want to watch, and you can choose to share the webpage or link (ed2k and magnet links). When searching for resources, it will be searched according to my predetermined priority (everyone video offline, subtitle man), of course, you can also use commands to...
    Downloads: 4 This Week
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  • 25
    Interpretable machine learning

    Interpretable machine learning

    Book about interpretable machine learning

    This book is about interpretable machine learning. Machine learning is being built into many products and processes of our daily lives, yet decisions made by machines don't automatically come with an explanation. An explanation increases the trust in the decision and in the machine learning model. As the programmer of an algorithm you want to know whether you can trust the learned model. Did it learn generalizable features? Or are there some odd artifacts in the training data which the...
    Downloads: 4 This Week
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