ailia-ai/ailia-models

The collection of pre-trained, state-of-the-art AI models for ailia SDK

What it solves

It provides a centralized, easy-to-use collection of over 400 pre-trained, state-of-the-art AI models across diverse modalities, eliminating the need for complex setup or manual weight management for each individual model.

How it works

Users install the ailia package and clone the repository. Models are organized by category (e.g., object detection, speech recognition, LLMs). Each model is designed to be runnable via a simple CLI; weights are downloaded automatically upon execution, requiring no manual arguments for basic operation.

Who it’s for

Developers and AI researchers who want to quickly deploy and test a wide variety of pre-trained models across different domains without spending time on environment configuration and weight acquisition.

Highlights

  • Massive Model Library: Includes 419 models covering object detection, speech recognition, image generation, LLMs, and more.
  • Unified Interface: All models are runnable from the same simple CLI.
  • Automated Workflow: Weights are downloaded automatically, simplifying the setup process.
  • Broad Platform Support: Compatible with Windows, macOS, Linux, iOS, Android, Jetson, and Raspberry Pi.

Related

  • Project
    open-gigaai/giga-models

    GigaModels is an open‑source Python toolbox that wraps dozens of pre‑trained vision, diffusion and multimodal models (e.g., Grounding DINO, Depth Anything, GigaBrain‑0, Pi0, Cosmos‑Predict2.5) behind a unified `load_pipeline` API. It supports both inference and training, offers ready‑to‑run scripts, and is installable via conda + pip.

  • Project
    soaring-xiongkulu/easyaiot

    EasyAIoT is an open‑source cloud‑edge‑device platform that unifies AI video analytics, IoT device management, and enterprise integration. It runs on tiny edge boxes (≈1 GB RAM), supports both professional IP cameras and consumer P2P cameras (Tapo, Tuya, Ring, etc.) via a built‑in RTC module, provides native YOLO/SAM inference, federated edge scheduling, and a one‑click installer (PANEL) for on‑site acceptance. Installable on Linux, Windows, macOS, and ARM, it offers web, mobile, and SCADA‑style dashboards for real‑time monitoring and alerting.

  • Project
    elyra-ai/elyra

    Elyra is a collection of JupyterLab extensions that add AI‑focused features such as a visual pipeline editor, batch‑job execution for notebooks/Python/R scripts, reusable code snippets, LLM‑powered code assistance, hybrid runtime support via Jupyter Enterprise Gateway, and Git integration. Installable via pip or conda (or via Docker), it lets data‑science teams build, run, and version‑control AI pipelines without leaving the notebook environment.

  • Project
    pzqpzq/LSF_MDia

    MDia is a Python library that formalises LLM intermediate reasoning as reusable “dialect cards” (LSFs) and provides a deterministic eight‑stage pipeline (collect → create → evolve → profile → select → run → validate‑rules → report) for generating, evolving, profiling, routing, and auditing these protocols across heterogeneous models. It supports black‑box LLM APIs, offers several routing plans, includes a 100‑rule bank, and guarantees reproducibility by freezing decisions before test evaluation. The repo includes a toy offline demo that runs without any API keys, extensive documentation, MIT licensing, and a citation to the ICML 2026 poster and arXiv paper.

  • Project
    Vasco0x4/AIDA

    AIDA is an open‑source autonomous pentesting agent that lets any LLM (Claude, Gemini, OpenAI Codex, etc.) drive a Docker‑based security testing environment. The model plans the assessment, calls built‑in tools (nmap, sqlmap, custom Python, raw HTTP), logs every step in a persistent notebook, auto‑scores findings with CVSS 4.0, and can export PDF reports. It ships with a lightweight `aida-pentest` container (or can attach Exegol for more tools) and includes a web dashboard, authentication, and notification integrations. The README cites real CVEs discovered by the system. Licensed under AGPL‑v3.