OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Aurora — wenhaochai

Aurora is a Python-based, Apache-2.0-licensed research series of multimodal language models. Its AuroraCap project provides a model for detailed video captioning, along with a video-caption benchmark, training data, and training and evaluation code.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Aurora — wenhaochai — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use AuroraCap when you want to experiment with detailed video captions or study video-captioning evaluation using a model project that includes benchmark and training materials.

Who it is for

  • Developers prototyping detailed video-description features
  • Researchers evaluating video captioning and video understanding
  • Practitioners exploring multimodal model training and inference

Documented uses

  • Generate a detailed text description from video input
  • Adjust visual-token retention for captioning, visual question answering, or OCR-related tasks
  • Explore a video-detailed-caption benchmark and its associated training set
  • Install the project’s additional code for model training or benchmark evaluation
Before you choose

Requirements & limits

The repository is public and identifies the project as Apache-2.0 licensed. The model and dataset licenses should also be followed.

  • The README gives a direct inference example using a 7B model checkpoint and a local video input.
  • The project includes a benchmark, trainset, and training and evaluation code in addition to the model.
  • The README recommends Python 3.10 or later and states that PyTorch 2.5 is not supported.
  • The README describes SGLang deployment as future work, not an available documented deployment path.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Run the documented AuroraCap inference example

Generate a detailed caption from a video using the repository’s example inference command.

Before you start

  • Conda and Python 3.10 or later in a virtual environment
  • PyTorch and torchvision; the README says PyTorch 2.5 is not supported
  • A local clone of the repository and access to the AuroraCap-7B-VID-xtuner model checkpoint
  • A video file to provide as visual input

Steps

  1. Create and activate a Conda environment with Python 3.10.

  2. Install PyTorch and torchvision following the PyTorch installation instructions.

  3. Clone the repository and change into its directory.

  4. Run inference.py with the model path, video input, prompt, frame count and other parameters shown in the README example.

Expected outcome

The inference script is configured to produce a detailed text description of the supplied video.

Guide limitations

  • The example specifies eight frames and a token_kept_ratio of 0.8; the README recommends 0.2–0.4 for captioning tasks.
  • The README says PyTorch 2.5 is not supported.
  • The guide does not establish hardware requirements or guarantee successful execution.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
wenhaochai/aurora · Official homepage title · retained in its source language
How we review sourcesSuggest a correction