Popular GitHub Repositories for Requirements

The Cradle framework is a first attempt at General Computer Control (GCC). Cradle supports agents to ace any computer task by enabling strong reasoning abilities, self-improvment, and skill curation, in a standardized general environment with minimal requirements.

AutoGroq is a groundbreaking tool that revolutionizes the way users interact with Autogen? and other AI assistants. By dynamically generating tailored teams of AI agents based on your project requirements, AutoGroq eliminates the need for manual configuration and allows you to tackle any question, problem, or project with ease and efficiency.

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.

Triton Model Analyzer is a CLI tool to help with better understanding of the compute and memory requirements of the Triton Inference Server models.

Resume_Builder_AIHawk is a powerful Python tool that allows you to automatically customize your resume based on a job URL, ensuring it perfectly aligns with the job requirements and skills. With an interactive command-line interface, this tool makes it easy to navigate through options and select from various pre-defined styles

Full finetuning of large language models without large memory requirements

The AI-powered PPT generation service based on ChatPPT can create presentations based on themes, requirements, or uploaded documents, supporting online editing and downloading.基于chatppt进行的AI PPT生成服务，可以满足基于主题或者要求、以及上传文档进行生成ppt，支持在线编辑与下载。

A Cloud-Native, framework agnostic Agent Runtime Ecosystem to democratise AI/ML adoption for Enterprises with Compliance & Defence requirements

Transform your software development lifecycle with Specif AI - an intelligent platform that turns project ideas into structured requirements and actionable tasks in minutes.

? Revolutionize Your Operations with One Sentence Automation: Utilizing large language models and Multi-Agents to generate operational copy, images, and videos with one-line requirements.

An ecologically inspired multi-agent system. Agents are designed with neural network based decision making, and complex resource requirements.

This Guidance demonstrates how to deploy a machine learning inference architecture on Amazon Elastic Kubernetes Service (Amazon EKS). It addresses the basic implementation requirements as well as ways you can pack thousands of unique PyTorch deep learning (DL) models into a scalable architecture and evaluate performance

Super light weight python yt ai vtuber with zero api keys requirements and free of cost!

This project demonstrates how to utilize Codellama, a local open-source Large Language Model (LLM), and customize its behavior according to your specific requirements using a Modelfile.

HAI Build is Presidio’s human-centric approach to harnessing Generative AI to produce faster, more collaborative, better-quality outcomes across the software development lifecycle. This groundbreaking innovation unlocks the ideal working relationship between human and machine.

A powerful, unofficial OpenAI-compatible API service offering free access to GPT-4o, GPT-4-turbo, and audio preview models like gpt-4o-audio-preview & Realtime Models like gpt-4o-realtime. Features streaming responses, voice synthesis, TTS with no authentication requirements. Hosted on Hugging Face & Railway (Free tier)

as机器学习库支持各种度量系数的计算，同时具有knn，决策树，线性回归等机器学习基础算法计算组件的实现，其还具有SQL式编程的数据计算库，与强大的机器视觉库，能够轻松实现各种人工智能处理需求。| The AS machine learning library supports various machine learning algorithms, as well as SQL programming for data calculation and a powerful machine vision library, which can easily meet various data processing requirements.

A curated list of free courses from reputable universities that meet the requirements of an undergraduate curriculum in Data Science, excluding general education. With projects, supporting materials in an organized structure.

mi-optimize is a versatile tool designed for the quantization and evaluation of large language models (LLMs). The library's seamless integration of various quantization methods and evaluation techniques empowers users to customize their approaches according to specific requirements and constraints, providing a high level of flexibility.

Requirements classification approach using BERT's transfer learning capabilities