Bump transformers from 4.45.2 to 4.48.0

[dependabot[bot]]

Bumps transformers from 4.45.2 to 4.48.0.

Release notes

Sourced from transformers's releases.

v4.48.0: ModernBERT, Aria, TimmWrapper, ColPali, Falcon3, Bamba, VitPose, DinoV2 w/ Registers, Emu3, Cohere v2, TextNet, DiffLlama, PixtralLarge, Moonshine

New models

ModernBERT

The ModernBert model was proposed in Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference by Benjamin Warner, Antoine Chaffin, Benjamin Clavié, Orion Weller, Oskar Hallström, Said Taghadouini, Alexis Galalgher, Raja Bisas, Faisal Ladhak, Tom Aarsen, Nathan Cooper, Grifin Adams, Jeremy Howard and Iacopo Poli.

It is a refresh of the traditional encoder architecture, as used in previous models such as BERT and RoBERTa.

It builds on BERT and implements many modern architectural improvements which have been developed since its original release, such as:

• Rotary Positional Embeddings to support sequences of up to 8192 tokens.
• Unpadding to ensure no compute is wasted on padding tokens, speeding up processing time for batches with mixed-length sequences.
• GeGLU Replacing the original MLP layers with GeGLU layers, shown to improve performance.
• Alternating Attention where most attention layers employ a sliding window of 128 tokens, with Global Attention only used every 3 layers.
• Flash Attention to speed up processing.
• A model designed following recent The Case for Co-Designing Model Architectures with Hardware, ensuring maximum efficiency across inference GPUs.
• Modern training data scales (2 trillion tokens) and mixtures (including code ande math data)

• Add ModernBERT to Transformers by @​warner-benjamin in #35158

Aria

The Aria model was proposed in Aria: An Open Multimodal Native Mixture-of-Experts Model by Li et al. from the Rhymes.AI team.

Aria is an open multimodal-native model with best-in-class performance across a wide range of multimodal, language, and coding tasks. It has a Mixture-of-Experts architecture, with respectively 3.9B and 3.5B activated parameters per visual token and text token.

• Add Aria by @​aymeric-roucher in #34157

TimmWrapper

We add a TimmWrapper set of classes such that timm models can be loaded in as transformer models into the library.

Here's a general usage example:

import torch
from urllib.request import urlopen
from PIL import Image
from transformers import AutoConfig, AutoModelForImageClassification, AutoImageProcessor
checkpoint = "timm/resnet50.a1_in1k"
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image_processor = AutoImageProcessor.from_pretrained(checkpoint)
</tr></table>

... (truncated)

Commits

• 6bc0fbc [WIP] Emu3: add model (#33770)
• 59e28c3 Fix flex_attention in training mode (#35605)
• 7cf6230 push a fix for now
• d6f446f when filtering we can't use the convert script as we removed them
• 8ce1e95 [test-all]
• af2d7ca Add Moonshine (#34784)
• 42b8e79 ModernBert: reuse GemmaRotaryEmbedding via modular + Integration tests (#35459)
• e39c9f7 v4.48-release
• 8de7b1b Add flex_attn to diffllama (#35601)
• 1e3ddcb ModernBERT bug fixes (#35404)
• Additional commits viewable in compare view

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