Building and better understanding vision-language models: insights and
  future directions
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.
66cc04c301fbc62ecda3f5c2
Hugo Laurençon
claimed_verified
2024-08-28T15:57:23.421Z
6177322d37f32ecb1e2d4cdf
https://cdn-avatars.huggingface.co/v1/production/uploads/1635201569275-noauth.jpeg
Hugo Laurençon
user
HugoLaurencon
66cc04c301fbc62ecda3f5c3
Andrés Marafioti
claimed_verified
2024-08-27T09:22:59.164Z
65d66b494bbd0d92b641cdbb
https://cdn-avatars.huggingface.co/v1/production/uploads/65d66b494bbd0d92b641cdbb/6-7dm7B-JxcoS1QlCPdMN.jpeg
Andres Marafioti
user
andito
66cc04c301fbc62ecda3f5c4
Victor Sanh
66cc04c301fbc62ecda3f5c5
Léo Tronchon
admin_assigned
2024-08-30T13:09:27.214Z
6244866a456803e9500d0f6a
https://cdn-avatars.huggingface.co/v1/production/uploads/1652185658647-6244866a456803e9500d0f6a.jpeg
Leo Tronchon
user
Leyo
Building and better understanding vision-language models: insights and
  future directions
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.
A comprehensive tutorial on building vision-language models, detailing the development of Idefics3-8B with an improved dataset enhancing document understanding.
vision-language models VLMs state-of-the-art Docmatix document understanding