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*This model was released on 2022-05-27 and added to Hugging Face Transformers on 2023-01-03.*

# GIT

<div class="flex flex-wrap space-x-1">
<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The GIT model was proposed in [GIT: A Generative Image-to-text Transformer for Vision and Language](https://huggingface.co/papers/2205.14100) by
Jianfeng Wang, Zhengyuan Yang, Xiaowei Hu, Linjie Li, Kevin Lin, Zhe Gan, Zicheng Liu, Ce Liu, Lijuan Wang. GIT is a decoder-only Transformer
that leverages [CLIP](clip)'s vision encoder to condition the model on vision inputs besides text. The model obtains state-of-the-art results on
image captioning and visual question answering benchmarks.

The abstract from the paper is the following:

*In this paper, we design and train a Generative Image-to-text Transformer, GIT, to unify vision-language tasks such as image/video captioning and question answering. While generative models provide a consistent network architecture between pre-training and fine-tuning, existing work typically contains complex structures (uni/multi-modal encoder/decoder) and depends on external modules such as object detectors/taggers and optical character recognition (OCR). In GIT, we simplify the architecture as one image encoder and one text decoder under a single language modeling task. We also scale up the pre-training data and the model size to boost the model performance. Without bells and whistles, our GIT establishes new state of the arts on 12 challenging benchmarks with a large margin. For instance, our model surpasses the human performance for the first time on TextCaps (138.2 vs. 125.5 in CIDEr). Furthermore, we present a new scheme of generation-based image classification and scene text recognition, achieving decent performance on standard benchmarks.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/git_architecture.jpg"
alt="drawing" width="600"/>

<small> GIT architecture. Taken from the <a href="https://huggingface.co/papers/2205.14100" target="_blank">original paper</a>. </small>

This model was contributed by [nielsr](https://huggingface.co/nielsr).
The original code can be found [here](https://github.com/microsoft/GenerativeImage2Text).

## Usage tips

- GIT is implemented in a very similar way to GPT-2, the only difference being that the model is also conditioned on `pixel_values`.

## Resources

A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with GIT.

- Demo notebooks regarding inference + fine-tuning GIT on custom data can be found [here](https://github.com/NielsRogge/Transformers-Tutorials/tree/master/GIT).
- See also: [Causal language modeling task guide](../tasks/language_modeling)

If you're interested in submitting a resource to be included here, please feel free to open a Pull Request and we will review it.
The resource should ideally demonstrate something new instead of duplicating an existing resource.

## GitVisionConfig

[[autodoc]] GitVisionConfig

## GitVisionModel

[[autodoc]] GitVisionModel
    - forward

## GitConfig

[[autodoc]] GitConfig
    - all

## GitProcessor

[[autodoc]] GitProcessor
    - __call__

## GitModel

[[autodoc]] GitModel
    - forward

## GitForCausalLM

[[autodoc]] GitForCausalLM
    - forward
