Popular GitHub Repositories for Common Spreadsheet Format

Python SDK, Proxy Server (LLM Gateway) to call 100+ LLM APIs in OpenAI format - [Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, Groq]

Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.

MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville

A collection of pre-trained, state-of-the-art models in the ONNX format

File Parser optimised for LLM Ingestion with no loss ? Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

common data analysis and machine learning tasks using python

Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, and PyTorch with more integrations coming..

Spreadsheet with AI, Code, Connections

Intelligent (edge and LLM) proxy for agents. Move faster by letting Arch handle the pesky heavy lifting in building agentic apps. Fast ?? task clarification, agent routing, seamless integration of prompts with tools for common tasks, and unified access and observability of LLMs.

ManimML is a project focused on providing animations and visualizations of common machine learning concepts with the Manim Community Library.

A high-performance Python-based I/O system for large (and small) deep learning problems, with strong support for PyTorch.

An Open-Source Python3 tool with SMALL models for recognizing layouts, tables, math formulas (LaTeX), and text in images, converting them into Markdown format. A free alternative to Mathpix, empowering seamless conversion of visual content into text-based representations. 80+ languages are supported.

Jupyter extensions that help you write code faster: Context aware AI Chat, Autocomplete, and Spreadsheet

An AI-powered data science team of agents to help you perform common data science tasks 10X faster.

A collection of production-ready Generative AI Agent templates built for Google Cloud. It accelerates development by providing a holistic, production-ready solution, addressing common challenges (Deployment & Operations, Evaluation, Customization, Observability) in building and deploying GenAI agents.

A Unity library for common movement AI

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

This project converts the API of Anthropic's Claude model to the OpenAI Chat API format.

Common interface for interacting with AI agents. The protocol is tech stack agnostic - you can use it with any framework for building agents.

A Solution Accelerator for the RAG pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. This includes most common requirements and best practices.