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*This model was published in HF papers on 2024-08-22 and contributed to Hugging Face Transformers on 2024-09-25.*

# Idefics3

<div class="flex flex-wrap space-x-1">
<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
</div>

## Overview

The Idefics3 model was proposed in [Building and better understanding vision-language models: insights and future directions](https://huggingface.co/papers/2408.12637) by Hugo Laurençon, Andrés Marafioti, Victor Sanh, and Léo Tronchon.

Idefics3 is an adaptation of the Idefics2 model with three main differences:

- It uses Llama3 for the text model.
- It uses an updated processing logic for the images.
- It removes the perceiver.

The abstract from the paper is the following:

*The field of vision-language models (VLMs), which take images and texts as inputs and output texts, is rapidly evolving and has yet to reach consensus on several key aspects of the development pipeline, including data, architecture, and training methods. This paper can be seen as a tutorial for building a VLM. We begin by providing a comprehensive overview of the current state-of-the-art approaches, highlighting the strengths and weaknesses of each, addressing the major challenges in the field, and suggesting promising research directions for underexplored areas. We then walk through the practical steps to build Idefics3-8B, a powerful VLM that significantly outperforms its predecessor Idefics2-8B, while being trained efficiently, exclusively on open datasets, and using a straightforward pipeline. These steps include the creation of Docmatix, a dataset for improving document understanding capabilities, which is 240 times larger than previously available datasets. We release the model along with the datasets created for its training.*

## Usage tips

Input images are processed either by upsampling (if resizing is enabled) or at their original resolution. The resizing behavior depends on two parameters: do_resize and size.

If `do_resize` is set to `True`, the model resizes images so that the longest edge is 4*364 pixels by default.
The default resizing behavior can be customized by passing a dictionary to the `size` parameter. For example, `{"longest_edge": 4 * 364}` is the default, but you can change it to a different value if needed.

Here’s how to control resizing and set a custom size:

```python
image_processor = Idefics3ImageProcessor(do_resize=True, size={"longest_edge": 2 * 364}, max_image_size=364)
```

Additionally, the `max_image_size` parameter, which controls the size of each square patch the image is decomposed into, is set to 364 by default but can be adjusted as needed. After resizing (if applicable), the image processor decomposes the images into square patches based on the `max_image_size` parameter.

This model was contributed by [amyeroberts](https://huggingface.co/amyeroberts) and [andimarafioti](https://huggingface.co/andito).

## Idefics3Config

[[autodoc]] Idefics3Config

## Idefics3VisionConfig

[[autodoc]] Idefics3VisionConfig

## Idefics3VisionTransformer

[[autodoc]] Idefics3VisionTransformer

## Idefics3Model

[[autodoc]] Idefics3Model
    - forward
    - get_image_features

## Idefics3ForConditionalGeneration

[[autodoc]] Idefics3ForConditionalGeneration
    - forward
    - get_image_features

## Idefics3ImageProcessor

[[autodoc]] Idefics3ImageProcessor
    - preprocess

## Idefics3ImageProcessorPil

[[autodoc]] Idefics3ImageProcessorPil
    - preprocess

## Idefics3Processor

[[autodoc]] Idefics3Processor
    - __call__
