Bump transformers from 4.38.0 to 4.48.0 in /examples/research_projects/pplm
Author: dependabot[bot]
Pull request: yes
Labels: name: dependencies

name: python

Bumps [transformers](https://github.com/huggingface/transformers) from 4.38.0 to 4.48.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a href="https://github.com/huggingface/transformers/releases">transformers's releases</a>.</em></p>
<blockquote>
<h2>v4.48.0: ModernBERT, Aria, TimmWrapper, ColPali, Falcon3, Bamba, VitPose, DinoV2 w/ Registers, Emu3, Cohere v2, TextNet, DiffLlama, PixtralLarge, Moonshine</h2>
<h2>New models</h2>
<h3>ModernBERT</h3>
<p>The ModernBert model was proposed in <a href="https://arxiv.org/abs/2412.13663">Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference</a> 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.</p>
<p>It is a refresh of the traditional encoder architecture, as used in previous models such as <a href="https://huggingface.co/docs/transformers/en/model_doc/bert">BERT</a> and <a href="https://huggingface.co/docs/transformers/en/model_doc/roberta">RoBERTa</a>.</p>
<p>It builds on BERT and implements many modern architectural improvements which have been developed since its original release, such as:</p>
<ul>
<li><a href="https://huggingface.co/blog/designing-positional-encoding">Rotary Positional Embeddings</a> to support sequences of up to 8192 tokens.</li>
<li><a href="https://arxiv.org/abs/2208.08124">Unpadding</a> to ensure no compute is wasted on padding tokens, speeding up processing time for batches with mixed-length sequences.</li>
<li><a href="https://arxiv.org/abs/2002.05202">GeGLU</a> Replacing the original MLP layers with GeGLU layers, shown to improve performance.</li>
<li><a href="https://arxiv.org/abs/2004.05150v2">Alternating Attention</a> where most attention layers employ a sliding window of 128 tokens, with Global Attention only used every 3 layers.</li>
<li><a href="https://github.com/Dao-AILab/flash-attention">Flash Attention</a> to speed up processing.</li>
<li>A model designed following recent <a href="https://arxiv.org/abs/2401.14489">The Case for Co-Designing Model Architectures with Hardware</a>, ensuring maximum efficiency across inference GPUs.</li>
<li>Modern training data scales (2 trillion tokens) and mixtures (including code ande math data)</li>
</ul>
<p><img src="https://github.com/user-attachments/assets/4256c0b1-9b40-4d71-ac42-fc94827d5e9d" alt="image" /></p>
<ul>
<li>Add ModernBERT to Transformers  by <a href="https://github.com/warner-benjamin"><code>@​warner-benjamin</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/35158">#35158</a></li>
</ul>
<h3>Aria</h3>
<p>The Aria model was proposed in <a href="https://huggingface.co/papers/2410.05993">Aria: An Open Multimodal Native Mixture-of-Experts Model</a> by Li et al. from the Rhymes.AI team.</p>
<p>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.</p>
<ul>
<li>Add Aria  by <a href="https://github.com/aymeric-roucher"><code>@​aymeric-roucher</code></a> in <a href="https://redirect.github.com/huggingface/transformers/issues/34157">#34157</a>
<img src="https://github.com/user-attachments/assets/ef41fcc9-2c5f-4a75-ab1a-438f73d3d7e2" alt="image" /></li>
</ul>
<h3>TimmWrapper</h3>
<p>We add a <code>TimmWrapper</code> set of classes such that timm models can be loaded in as transformer models into the library.</p>
<p>Here's a general usage example:</p>
<pre lang="py"><code>import torch
from urllib.request import urlopen
from PIL import Image
from transformers import AutoConfig, AutoModelForImageClassification, AutoImageProcessor
<p>checkpoint = &quot;timm/resnet50.a1_in1k&quot;
img = Image.open(urlopen(
'<a href="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png">https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png</a>'
))</p>
<p>image_processor = AutoImageProcessor.from_pretrained(checkpoint)
&lt;/tr&gt;&lt;/table&gt;
</code></pre></p>
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a href="https://github.com/huggingface/transformers/commit/6bc0fbcfa7acb6ac4937e7456a76c2f7975fefec"><code>6bc0fbc</code></a> [WIP] Emu3: add model (<a href="https://redirect.github.com/huggingface/transformers/issues/33770">#33770</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/59e28c30fa3a91213f569bccef73f082afa8c656"><code>59e28c3</code></a> Fix flex_attention in training mode (<a href="https://redirect.github.com/huggingface/transformers/issues/35605">#35605</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/7cf6230e25078742b21907ae49d1542747606457"><code>7cf6230</code></a> push a fix for now</li>
<li><a href="https://github.com/huggingface/transformers/commit/d6f446ffa79811d35484d445bc5c7932e8a536d6"><code>d6f446f</code></a> when filtering we can't use the convert script as we removed them</li>
<li><a href="https://github.com/huggingface/transformers/commit/8ce1e9578af6151e4192d59c345e2ad86ee789d4"><code>8ce1e95</code></a> [test-all]</li>
<li><a href="https://github.com/huggingface/transformers/commit/af2d7caff393cf8881396b73d92d0595b6a3b2ae"><code>af2d7ca</code></a> Add Moonshine  (<a href="https://redirect.github.com/huggingface/transformers/issues/34784">#34784</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/42b8e7916b6b6dff5cb77252286db1aa07b7b41e"><code>42b8e79</code></a> ModernBert: reuse GemmaRotaryEmbedding via modular + Integration tests (<a href="https://redirect.github.com/huggingface/transformers/issues/35459">#35459</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/e39c9f7a78fa2960a7045e8fc5a2d96b5d7eebf1"><code>e39c9f7</code></a> v4.48-release</li>
<li><a href="https://github.com/huggingface/transformers/commit/8de7b1ba8d126a6fc9f9bcc3173a71b46f0c3601"><code>8de7b1b</code></a> Add flex_attn to diffllama (<a href="https://redirect.github.com/huggingface/transformers/issues/35601">#35601</a>)</li>
<li><a href="https://github.com/huggingface/transformers/commit/1e3ddcb2d0380d0d909a44edc217dff68956ec5e"><code>1e3ddcb</code></a> ModernBERT bug fixes (<a href="https://redirect.github.com/huggingface/transformers/issues/35404">#35404</a>)</li>
<li>Additional commits viewable in <a href="https://github.com/huggingface/transformers/compare/v4.38.0...v4.48.0">compare view</a></li>
</ul>
</details>
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Hi 👋, thank you for opening this pull request! The pull request is converted to draft by default. When it is ready for review, please click the `Ready for review` button (at the bottom of the PR page).

Although this goes against our philosophy of leaving research projects "frozen in time", I'm merging this just to resolve lots of CVE alerts on the repo!

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