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transformers/docs/source/en/model_doc/git.md

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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