Metadata-Version: 2.1
Name: gpt4o
Version: 0.0.1
Summary: gpt4o - Pytorch
Home-page: https://github.com/kyegomez/gpt4o
License: MIT
Keywords: artificial intelligence,deep learning,optimizers,Prompt Engineering
Author: Kye Gomez
Author-email: kye@apac.ai
Requires-Python: >=3.10,<4.0
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.9
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Dist: einops
Requires-Dist: torch
Requires-Dist: zetascale
Project-URL: Documentation, https://github.com/kyegomez/gpt4o
Project-URL: Repository, https://github.com/kyegomez/gpt4o
Description-Content-Type: text/markdown

[![Multi-Modality](agorabanner.png)](https://discord.gg/qUtxnK2NMf)

# GPT4o
Community Open Source Implementation of GPT4o in PyTorch


## Install


# Architecture
- TikToken Tokenzier: We know fursure the tokenizer. [Which is here](https://github.com/openai/tiktoken)
- Model understands Images and Audio Natively. There are 2 approaches, process them natively or use encoders for each. I think here they're using encoders like whisper and vit for simplicity and brevity.
- Using DALLE3 as the output head to generate images
- Tokens to denote when to generate an image or audio
- Whisper output head for the audio outputs
- 

# License
MIT

