Add Aimv2 model (#36625)
* Model skelton * changes * temp push * changes * Added support for aimv2-native * More changes * More changes * Stupid mistake correction * Added config and refactor * Added vison model * update * Refactor for lit variant * Added Text Model * Minor fixes * nits * update * Preliminary tests * More fixes * Updated tests 🤗 * Refactor * Updated testcase * Updated config * make fixup * more fixes * Bug fix and updates * deadcode * Fixes * nit * up * Happy CI ✅ * Reduce LOC * nit * nit * make style * return_dict refactor * bug fix * fix * doc update * nit * make fixup * Minor update * _init_weigths modifcation * update tests * Minor fixes post review * Update w.r.t GradientCheckpointingLayer * docs update * update * nit * Use more Modular 😉 * Change name from AIMv2 to Aimv2 * Nit * make style * Add model doc pointer * make style * Update model doc section * updates * Modify attn mask and interface * update test * Final change * Utilize flash and flex attn * keep attn mask * camelcase model name in test file * Fix docstring * Fix config warning finally and create_causal_mask * disable torchscript * remove unused arg * remove from tests * balance model size for tests * fix device * tests * tests * flaky test * fix import --------- Co-authored-by: Cyril Vallez <cyril.vallez@huggingface.co> Co-authored-by: Cyril Vallez <cyril.vallez@gmail.com>
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Yaswanth Gali
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<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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# AIMv2
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## Overview
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The AIMv2 model was proposed in [Multimodal Autoregressive Pre-training of Large Vision Encoders](https://arxiv.org/abs/2411.14402) by Enrico Fini, Mustafa Shukor, Xiujun Li, Philipp Dufter, Michal Klein, David Haldimann, Sai Aitharaju, Victor Guilherme Turrisi da Costa, Louis Béthune, Zhe Gan, Alexander T Toshev, Marcin Eichner, Moin Nabi, Yinfei Yang, Joshua M. Susskind, Alaaeldin El-Nouby.
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The abstract from the paper is the following:
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*We introduce a novel method for pre-training of large-scale vision encoders. Building on recent advancements in autoregressive pre-training of vision models, we extend this framework to a multimodal setting, i.e., images and text. In this paper, we present AIMV2, a family of generalist vision encoders characterized by a straightforward pre-training process, scalability, and remarkable performance across a range of downstream tasks. This is achieved by pairing the vision encoder with a multimodal decoder that autoregressively generates raw image patches and text tokens. Our encoders excel not only in multimodal evaluations but also in vision benchmarks such as localization, grounding, and classification. Notably, our AIMV2-3B encoder achieves 89.5% accuracy on ImageNet-1k with a frozen trunk. Furthermore, AIMV2 consistently outperforms state-of-the-art contrastive models (e.g., CLIP, SigLIP) in multimodal image understanding across diverse settings.*
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This model was contributed by [Yaswanth Gali](https://huggingface.co/yaswanthgali).
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The original code can be found [here](https://github.com/apple/ml-aim).
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## Usage Example
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Here is an example of Image Feature Extraction using specific checkpoints on resized images and native resolution images:
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```python
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import requests
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from PIL import Image
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from transformers import AutoImageProcessor, AutoModel
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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processor = AutoImageProcessor.from_pretrained("apple/aimv2-large-patch14-native")
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model = AutoModel.from_pretrained("apple/aimv2-large-patch14-native")
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inputs = processor(images=image, return_tensors="pt")
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outputs = model(**inputs)
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```
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Here is an example of a checkpoint performing zero-shot classification:
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```python
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import requests
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from PIL import Image
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from transformers import AutoProcessor, AutoModel
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url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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text = ["Picture of a dog.", "Picture of a cat.", "Picture of a horse."]
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processor = AutoProcessor.from_pretrained("apple/aimv2-large-patch14-224-lit")
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model = AutoModel.from_pretrained("apple/aimv2-large-patch14-224-lit")
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inputs = processor(
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images=image,
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text=text,
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add_special_tokens=True,
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truncation=True,
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padding=True,
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return_tensors="pt",
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)
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outputs = model(**inputs)
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probs = outputs.logits_per_image.softmax(dim=-1)
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```
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## Aimv2Config
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[[autodoc]] Aimv2Config
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## Aimv2TextConfig
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[[autodoc]] Aimv2TextConfig
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## Aimv2VisionConfig
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[[autodoc]] Aimv2VisionConfig
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## Aimv2Model
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[[autodoc]] Aimv2Model
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- forward
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## Aimv2VisionModel
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[[autodoc]] Aimv2VisionModel
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- forward
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## Aimv2TextModel
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[[autodoc]] Aimv2TextModel
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- forward
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</pt>
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<tf>
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