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idefics2-re-verse
de-Rodrigo/idefics2-re-verse
idefics2-re-verse is a image-text-to-text model from de-Rodrigo. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
<a href="https://x.com/nearcyan/status/1706914605262684394" <div style="text-align: center;" <picture <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/ass…
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Public
Updated Mar 28, 2026
Repo size
374 MB
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0
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Repo makeup
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.safetensors93.4 MB · 96%
At a glance
- Task
- Image-Text-to-Text
- License
- mit
- Access
- Public
- Created
- Mar 26, 2026
- Updated
- Mar 28, 2026
- SHA
- 715f3a96
Try a prompt
Base models
Datasets
Model card
From the Hugging Face model README
IDEFICS2 Merit
<a href="https://x.com/nearcyan/status/1706914605262684394"> <div style="text-align: center;"> <picture> <source media="(prefers-color-scheme: dark)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png"> <source media="(prefers-color-scheme: light)" srcset="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png"> <img alt="DragonHuggingFace" src="https://huggingface.co/de-Rodrigo/donut-merit/resolve/main/assets/dragon_huggingface.png" style="width: 200px;"> </picture> </div> </a>Model Architecture
This model is based on the Donut architecture and fine-tuned on the Merit dataset for form understanding tasks.
Example Usage
from transformers import AutoModel
model = AutoModel.from_pretrained("de-Rodrigo/idefics2-merit")
WIP 🛠️
- Task
- Image-Text-to-Text
- License
- mit
- Languages
- en, es
- Created
- Mar 26, 2026
- Updated
- Mar 28, 2026