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smolagents
Agents write python code to call tools and orchestrate other agents
This library is the simplest framework out there to build powerful agents. We provide our definition in this page, where you’ll also find tips for when to use them or not (spoilers: you’ll often be better off without agents). smolagents is a lightweight framework for building AI agents using large language models (LLMs). It simplifies the development of AI-driven applications by providing tools to create, train, and deploy language model-based agents. -
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OAuthLib
A generic, spec-compliant, thorough implementation of the OAuth
A generic, spec-compliant, thorough implementation of the OAuth request-signing logic for Python 3.8+. OAuthLib is a framework which implements the logic of OAuth1 or OAuth2 without assuming a specific HTTP request object or web framework. Use it to graft OAuth client support onto your favorite HTTP library, or provide support onto your favourite web framework. If you're a maintainer of such a library, write a thin veneer on top of OAuthLib and get OAuth support for very little effort. -
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CyberScraper 2077
A Powerful web scraper powered by LLM | OpenAI, Gemini & Ollama
CyberScraper 2077 is not just another web scraping tool – it's a glimpse into the future of data extraction. Born from the neon-lit streets of a cyberpunk world, this AI-powered scraper uses OpenAI, Gemini and LocalLLM Models to slice through the web's defenses, extracting the data you need with unparalleled precision and style. -
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Agently
AI Agent Application Development Framework
Build AI agent native application in very little code. Easy to interact with AI agents in code using structure data and chained-calls syntax. Enhance AI Agent using plugins instead of rebuilding a whole new agent. Agently is a development framework that helps developers build AI agent native applications really fast. You can use and build AI agents in your code in an extremely simple way. -
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Intel Extension for PyTorch
A Python package for extending the official PyTorch
Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel® AVX-512) Vector Neural Network Instructions (VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel Xe Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel... -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale. -
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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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Llama Recipes
Scripts for fine-tuning Meta Llama3 with composable FSDP & PEFT method
The 'llama-recipes' repository is a companion to the Meta Llama models. We support the latest version, Llama 3.1, in this repository. The goal is to provide a scalable library for fine-tuning Meta Llama models, along with some example scripts and notebooks to quickly get started with using the models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama and other tools in the LLM ecosystem. The examples here showcase how to run... -
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adstex
Automated generation of NASA ADS bibtex entries directly from citation
adstex automatically identifies all citation keys (e.g., identifiers, author+year) in your TeX source files and uses NASA's Astrophysics Data System (ADS) to generate corresponding bibtex entries. Write your papers without worrying about the bibtex entries. Simply put down arXiv IDs, ADS bibcodes, DOIs, or first author & year citation keys in your \cite commands, and then use adstex to automatically generate the bibtex file for you. -
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Markdown package LaTeX
Package for converting and rendering markdown documents in TeX
The Markdown package converts CommonMark markup to TeX commands. The functionality is provided both as a Lua module, and as plain TeX, LaTeX, and ConTeXt macro packages that can be used to directly typeset TeX documents containing markdown markup. Unlike other convertors, the Markdown package does not require any external programs and makes it easy to redefine how each and every markdown element is rendered. Creative abuse of the markdown syntax is encouraged. -
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mistletoe
A fast, extensible and spec-compliant Markdown parser in pure Python
mistletoe is a Markdown parser in pure Python, designed to be fast, spec-compliant and fully customizable. Apart from being the fastest CommonMark-compliant Markdown parser implementation in pure Python, mistletoe also supports easy definitions of custom tokens. Parsing Markdown into an abstract syntax tree also allows us to swap out renderers for different output formats, without touching any of the core components. -
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Remarshal
Convert between CBOR, JSON, MessagePack, TOML, and YAML
Convert between CBOR, JSON, MessagePack, TOML, and YAML. When installed, provides the command-line command remarshal as well as the short commands {cbor,json,msgpack,toml,yaml}2{cbor,json,msgpack,toml,yaml}. You can perform format conversion, reformatting, and error detection using these commands. CBOR, MessagePack, and YAML with binary fields cannot be converted to JSON or TOML. Binary fields are converted between CBOR, MessagePack, and YAML. -
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spacy-llm
Integrating LLMs into structured NLP pipelines
Large Language Models (LLMs) feature powerful natural language understanding capabilities. With only a few (and sometimes no) examples, an LLM can be prompted to perform custom NLP tasks such as text categorization, named entity recognition, coreference resolution, information extraction and more. This package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for fast prototyping and prompting, and turning unstructured responses into robust outputs for various... -
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django-prometheus
Export Django monitoring metrics for Prometheus.io
Export Django monitoring metrics for Prometheus.io. This library provides Prometheus metrics for Django-related operations. Prometheus uses Histogram based grouping for monitoring latencies. You can define custom buckets for latency, adding more buckets decreases performance but increases accuracy. SQLite, MySQL, and PostgreSQL databases can be monitored. Just replace the ENGINE property of your database, replacing django.db.backends with django_prometheus.db.backends. You may want to... -
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django-braces
Reusable, generic mixins for Django
django-braces provides useful Mixins for Django's class-based views. Most of these mixins replicate the behavior of Django's function-based view decorators. Others solve common headaches with working with class-based views. django-braces is stable and time-tested. It does not receive a lot of updates and is not in active development. django-braces also only officially supports Python versions that are still receiving fixes and Django LTS versions. django-braces will work with a most modern... -
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TorchMetrics
Machine learning metrics for distributed, scalable PyTorch application
TorchMetrics is a collection of 80+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. Your data will always be placed on the same device as your metrics. You can log Metric objects directly in Lightning to reduce even more boilerplate. The module-based metrics contain internal metric states (similar to the parameters of the PyTorch module) that automate accumulation and synchronization across devices! Automatic accumulation over multiple batches. Automatic... -
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Qlib
Qlib is an AI-oriented quantitative investment platform
Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib. With Qlib, users can easily try their ideas to create better Quant investment strategies. At the module level, Qlib is a platform that consists of... -
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Oppia
A free, online learning platform to make quality education accessible
Oppia is an online learning tool that enables anyone to easily create and share interactive activities (called 'explorations'). These activities simulate a one-on-one conversation with a tutor, making it possible for students to learn by doing while getting feedback. Oppia identifies common wrong answers and provides tailored feedback, so that students get a personalized experience. Our lessons keep students engaged through playful characters and use different strategies to solidify their... -
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Python Fire
Automatically generate CLIs from absolutely any Python object
Python Fire is a library that automatically generates command line interfaces (CLIs) from absolutely any Python object. It’s a really simple and easy way to create CLI in Python, and can also enable you to explore existing code or turn other people’s code into a CLI. Python Fire lets you call Fire on any Python object: be it functions, classes, objects, modules, lists-- you name it! It will help you develop as well as debug Python code, and make transitioning between Bash and Python a... -
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LongBench
LongBench v2 and LongBench (ACL 25'&24')
LongBench is a comprehensive benchmark designed to evaluate the ability of large language models to understand and reason over very long textual contexts. Traditional language model benchmarks typically evaluate tasks involving relatively short inputs, which does not reflect many real-world applications such as analyzing large documents or entire code repositories. LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across... -
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Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
Xtuner is a large-scale training engine designed for efficient training and fine-tuning of modern large language models, particularly mixture-of-experts architectures. The framework focuses on enabling scalable training for extremely large models while maintaining efficiency across distributed computing environments. Unlike traditional 3D parallel training strategies, XTuner introduces optimized parallelism techniques that simplify scaling and reduce system complexity when training massive... -
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EKS Best Practices
A best practices guide for day 2 operations
The Amazon EKS Best Practices Guide is a public repository containing comprehensive documentation and guidance for operating production-grade Kubernetes clusters on AWS’s managed service, Amazon EKS. Rather than a code library, it serves as a reference catalogue of patterns, anti-patterns, checklists and architectures across domains such as security, reliability, scalability, networking, cost optimization and hybrid cloud deployments. The repository is maintained by AWS but open to... -
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Granite TSFM
Foundation Models for Time Series
granite-tsfm collects public notebooks, utilities, and serving components for IBM’s Time Series Foundation Models (TSFM), giving practitioners a practical path from data prep to inference for forecasting and anomaly-detection use cases. The repository focuses on end-to-end workflows: loading data, building datasets, fine-tuning forecasters, running evaluations, and serving models. It documents the currently supported Python versions and points users to where the core TSFM models are hosted... -
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Python Slack SDK
Slack Developer Kit for Python
Python Slack SDK is the official Python developer kit for building applications that interact with the Slack platform. It provides modular packages for Slack APIs, allowing developers to use only the capabilities they need while still keeping the pieces compatible with each other. The SDK covers the Web API, incoming webhooks, Socket Mode, OAuth, Audit Logs, SCIM, RTM, Block Kit models, and request signature verification. It is useful for bots, workflow tools, internal automations, admin... -
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OPAL
Policy and data administration, distribution, and real-time updates
OPAL is an open-source administration layer for keeping authorization policies and authorization data synchronized in real time. It works with policy engines such as OPA and Cedar so applications can make decisions using current policy and data without constantly rebuilding custom sync logic. The project aggregates policy and data from external sources, watches for changes, and distributes updates to connected policy agents. It is built for cloud-native and microservice environments where...
