Project description

Next GPT

Implementation of "NExT-GPT: Any-to-Any Multimodal LLM",

Paper Link

Appreciation

Lucidrains
Agorians

Install

pip install next-gpt

Usage

Architecture

Todo

License

MIT

Citations

@articles{wu2023nextgpt,
  title={NExT-GPT: Any-to-Any Multimodal LLM},
  author={Shengqiong Wu, Hao Fei, Leigang Qu, Wei Ji, Tat-Seng Chua},
  journal = {CoRR},
  volume = {abs/2309.05519},
  year={2023}
}

Project details

These details have not been verified by PyPI

Project links

GitHub Statistics

View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery

Release history Release notifications | RSS feed

This version

0.0.1

Sep 12, 2023

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

omnimodal-0.0.1.tar.gz (21.0 kB view hashes)

Uploaded Sep 12, 2023 Source

Built Distribution

omnimodal-0.0.1-py3-none-any.whl (21.9 kB view hashes)

Uploaded Sep 12, 2023 Python 3

Hashes for omnimodal-0.0.1.tar.gz

Hashes for omnimodal-0.0.1.tar.gz
Algorithm	Hash digest
SHA256	`4ca4f355d1270bf6e58b219f78740169d31d57f571791584e29b3bbef2191942`
MD5	`62478970b7f94d1eb8cfced0414516e2`
BLAKE2b-256	`ae63ee396448f50ee5ae1243beec8f0695770fb206bb9e05191a3de6ca3753bc`

Hashes for omnimodal-0.0.1-py3-none-any.whl

Hashes for omnimodal-0.0.1-py3-none-any.whl
Algorithm	Hash digest
SHA256	`5250c937a8a5b5785611151767b10fdda8f8f6168b5e055f6145b6a6e15a0191`
MD5	`51e45a278fdc9794f23b26091855fccd`
BLAKE2b-256	`cf70c147e39f2aa0860357be17638a3af912ba0b24be89bd85ec87c650ff6f98`