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Showing 17982 open source projects for "compiler python linux"
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BEIR
A Heterogeneous Benchmark for Information Retrieval
BEIR is a benchmark framework for evaluating information retrieval models across various datasets and tasks, including document ranking and question answering. -
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Data-Juicer
Data processing for and with foundation models
Data-Juicer is an open-source data processing and augmentation framework designed to enhance the quality and diversity of datasets for machine learning tasks. It includes a modular pipeline for scalable data transformation. -
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STORM
An LLM-powered knowledge curation system that researches topics
STORM is an open-source virtual assistant framework developed by Stanford's OVAL lab. It is designed for creating natural language interfaces and assistants that can interact with APIs, databases, and services in a modular way. -
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DeepBI
LLM based data scientist, AI native data application
DeepBI is an AI-native data analysis platform. DeepBI leverages the power of large language models to explore, query, visualize, and share data from any data source. Users can use DeepBI to gain data insight and make data-driven decisions. -
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Pyreft
ReFT: Representation Finetuning for Language Models
PyreFT is a tool by Stanford NLP for fine-tuning transformer models with an emphasis on efficient, resource-conserving training and customizability for NLP tasks. -
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wikmd
A file based wiki that uses markdown
It’s a file-based wiki that aims to simplicity. Instead of storing the data in a database I chose to have a file-based system. The advantage of this system is that every file is directly readable inside a terminal etc. Also when you have direct access to the system you can export the files to anything you like. -
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Dataproc Templates
Dataproc templates and pipelines for solving simple in-cloud data task
Dataproc templates are designed to address various in-cloud data tasks, including data import/export/backup/restore and bulk API operations. These templates leverage the power of Google Cloud's Dataproc, supporting both Dataproc Serverless and Dataproc clusters. Google provides this collection of pre-implemented Dataproc templates as a reference and for easy customization. -
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bert4torch
An elegent pytorch implement of transformers
An elegant PyTorch implement of transformers. -
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MusicLM - Pytorch
Implementation of MusicLM music generation model in Pytorch
Implementation of MusicLM, Google's new SOTA model for music generation using attention networks, in Pytorch. They are basically using text-conditioned AudioLM, but surprisingly with the embeddings from a text-audio contrastive learned model named MuLan. MuLan is what will be built out in this repository, with AudioLM modified from the other repository to support the music generation needs here. -
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Payloads All The Things
A list of useful payloads and bypass for Web Application Security
A list of useful payloads and bypasses for Web Application Security. Feel free to improve with your payloads and techniques. The API key is a unique identifier that is used to authenticate requests associated with your project. Some developers might hardcode them or leave it on public shares. -
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MyUW
MyUW Django development server
MyUW is a project under the University of Wisconsin-Madison Information Technology Department. The MyUW team develops several free and open source products which they operate in the MyUW portal, a portal that provides applicants, advisors, students, faculty and staff with an easy, secure and efficient way to access the university's services, applications and resources. This particular project will allow you to get started with the Django development server, running MyUW as javerage. From... -
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nbdev
Create delightful software with Jupyter Notebooks
nbdev is a notebook-driven development platform (by fast.ai/AnswerDotAI) enabling you to write code, tests, documentation, and deploy software, all from Jupyter Notebooks. It provides a unified literate programming workflow where you can tag notebook cells for export to Python modules, auto-generate documentation via Quarto (and host it on GitHub Pages), run tests embedded in notebooks, manage clean notebooks with Git-friendly metadata hooks, and seamlessly publish packages to PyPI/conda, all... -
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Peewee-async
Asynchronous interface for peewee ORM powered by asyncio
peewee-async is an async extension for the Peewee ORM, enabling non-blocking database access in asyncio-powered Python applications. It allows you to use familiar Peewee models while benefiting from asynchronous I/O, making it ideal for web applications or services that require concurrency. -
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Piccolo
A fast, user friendly ORM and query builder which supports asyncio
Piccolo is a modern, fast, and type-safe ORM for Python, designed with developer ergonomics in mind. It provides a clean syntax for defining schemas and building queries while supporting both sync and async execution. With built-in admin tools and rich introspection, Piccolo is suitable for web apps, APIs, and small-to-medium scale backends that prioritize clarity and speed. -
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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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AgentRun
The easiest, and fastest way to run AI-generated Python code safely
AgentRun is a framework for building autonomous AI agents capable of executing complex tasks with minimal human intervention. It provides a structured environment for defining agent behaviors, managing workflows, and integrating AI models to achieve specific goals. -
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mypy-baseline
Integrate mypy in seconds with existing codebase
A CLI tool for painless integration of mypy with an existing Python project. When you run it for the first time, it will remember all types of errors that you already have in the project (generate “baseline”). All consecutive runs will ignore these errors and report only the ones that you introduced after that. -
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Quark Agent
Quark Agent - Your AI-powered Android APK Analyst
With Quark Agent, you can perform analyses using only natural language. It creates Quark Script code following your ideas and adjusts the code promptly as you provide feedback. -
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Shapash
Explainability and Interpretability to Develop Reliable ML models
Shapash is a Python library dedicated to the interpretability of Data Science models. It provides several types of visualization that display explicit labels that everyone can understand. Data Scientists can more easily understand their models, share their results and easily document their projects in an HTML report. End users can understand the suggestion proposed by a model using a summary of the most influential criteria. -
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pomegranate
Fast, flexible and easy to use probabilistic modelling in Python
pomegranate is a library for probabilistic modeling defined by its modular implementation and treatment of all models as the probability distributions they are. The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution,... -
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sktime
A unified framework for machine learning with time series
sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes time series classification, regression, clustering, annotation, and forecasting. It comes with time series algorithms and scikit-learn compatible tools to build, tune and validate time series models. Our objective is to enhance the interoperability and usability of the time series analysis ecosystem in its entirety. sktime provides a unified... -
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Pretty Jupyter
Creates dynamic html report from jupyter notebook.
Pretty Jupyter is an easy-to-use package that allows to create beautiful & dynamic HTML reports. Most of the features require little to no work to get working and greatly improve the quality of the output report, or even the developer’s comfort when creating the report. For example, tabs make some visualizations much more comfortable. The features are integrated directly into the output page, therefore there is no need to have an interpreter running in the backend. This makes the HTML easily... -
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ipyvizzu
Build animated charts in Jupyter Notebook and similar environments
ipyvizzu - Build animated charts in Jupyter Notebook and similar environments with a simple Python syntax ipyvizzu is an animated charting tool for Jupyter, Google Colab, Databricks, Kaggle and Deepnote notebooks among other platforms. ipyvizzu enables data scientists and analysts to utilize animation for storytelling with data using Python. It's built on the open-source JavaScript/C++ charting library Vizzu. There is a new extension of ipyvizzu, ipyvizzu-story with which the animated charts... -
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The PyPlot module for Julia
Plotting for Julia based on matplotlib.pyplot
This module provides a Julia interface to the Matplotlib plotting library from Python, and specifically to the matplotlib.pyplot module. PyPlot uses the Julia PyCall package to call Matplotlib directly from Julia with little or no overhead (arrays are passed without making a copy). (See also PythonPlot.jl for a version of PyPlot.jl using the alternative PythonCall.jl package.) This package takes advantage of Julia's multimedia I/O API to display plots in any Julia graphical backend, including... -
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Guardrails
Adding guardrails to large language models
Guardrails is a Python package that lets a user add structure, type and quality guarantees to the outputs of large language models (LLMs). At the heart of Guardrails is the rail spec. rail is intended to be a language-agnostic, human-readable format for specifying structure and type information, validators and corrective actions over LLM outputs. We create a RAIL spec to describe the expected structure and types of the LLM output, the quality criteria for the output to be considered valid...
