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chore: bump transformers from 5.3.0 to 5.5.0 in /libs/partners/huggingface (#38828)

Browse Source 
Bumps [transformers](https://github.com/huggingface/transformers) from
5.3.0 to 5.5.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/huggingface/transformers/releases">transformers's
releases</a>.</em></p>
<blockquote>
<h1>Release v5.5.0</h1>
<!-- raw HTML omitted -->
<h2>New Model additions</h2>
<h3>Gemma4</h3>
<p><a
href="https://github.com/huggingface/transformers/blob/HEAD/INSET_PAPER_LINK">Gemma
4</a> is a multimodal model with pretrained and instruction-tuned
variants, available in 1B, 13B, and 27B parameters. The architecture is
mostly the same as the previous Gemma versions. The key differences are
a vision processor that can output images of fixed token budget and a
spatial 2D RoPE to encode vision-specific information across height and
width axis.</p>
<!-- raw HTML omitted -->
<p>You can find all the original Gemma 4 checkpoints under the <a
href="https://huggingface.co/collections/google/gemma-4-release-67c6c6f89c4f76621268bb6d">Gemma
4</a> release.</p>
<p>The key difference from previous Gemma releases is the new design to
process <strong>images of different sizes</strong> using a
<strong>fixed-budget number of tokens</strong>. Unlike many models that
squash every image into a fixed square (like 224×224), Gemma 4 keeps the
image's natural aspect ratio while making it the right size. There a a
couple constraints to follow:</p>
<ul>
<li>The total number of pixels must fit within a patch budget</li>
<li>Both height and width must be divisible by <strong>48</strong> (=
patch size 16 × pooling kernel 3)</li>
</ul>
<blockquote>
<p>[!IMPORTANT]
Gemma 4 does <strong>not</strong> apply the standard ImageNet mean/std
normalization that many other vision models use. The model's own patch
embedding layer handles the final scaling internally (shifting values to
the [-1, 1] range).</p>
</blockquote>
<p>The number of &quot;soft tokens&quot; (aka vision tokens) an image
processor can produce is configurable. The supported options are
outlined below and the default is <strong>280 soft tokens</strong> per
image.</p>
<table>
<thead>
<tr>
<th align="center">Soft Tokens</th>
<th align="center">Patches (before pooling)</th>
<th align="center">Approx. Image Area</th>
</tr>
</thead>
<tbody>
<tr>
<td align="center">70</td>
<td align="center">630</td>
<td align="center">~161K pixels</td>
</tr>
<tr>
<td align="center">140</td>
<td align="center">1,260</td>
<td align="center">~323K pixels</td>
</tr>
<tr>
<td align="center"><strong>280</strong></td>
<td align="center"><strong>2,520</strong></td>
<td align="center"><strong>~645K pixels</strong></td>
</tr>
<tr>
<td align="center">560</td>
<td align="center">5,040</td>
<td align="center">~1.3M pixels</td>
</tr>
<tr>
<td align="center">1,120</td>
<td align="center">10,080</td>
<td align="center">~2.6M pixels</td>
</tr>
</tbody>
</table>
<p>To encode positional information for each patch in the image, Gemma 4
uses a learned 2D position embedding table. The position table stores up
to 10,240 positions per axis, which allows the model to handle very
large images. Each position is a learned vector of the same dimensions
as the patch embedding. The 2D RoPE which Gemma 4 uses independently
rotate half the attention head dimensions for the x-axis and the other
half for the y-axis. This allows the model to understand spatial
relationships like &quot;above,&quot; &quot;below,&quot; &quot;left
of,&quot; and &quot;right of.&quot;</p>
<h3>NomicBERT</h3>
<p>NomicBERT is a BERT-inspired encoder model that applies Rotary
Position Embeddings (RoPE) to create reproducible long context text
embeddings. It is the first fully reproducible, open-source text
embedding model with 8192 context length that outperforms both OpenAI
Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long
context LoCo benchmarks. The model generates dense vector embeddings for
various tasks including search, clustering, and classification using
specific instruction prefixes.</p>
<p><strong>Links:</strong> <a
href="https://huggingface.co/docs/transformers/main/en/model_doc/nomic_bert">Documentation</a>
| <a href="https://arxiv.org/abs/2402.01613">Paper</a></p>
<ul>
<li>Internalise the NomicBERT model (<a
href="https://redirect.github.com/huggingface/transformers/issues/43067">#43067</a>)
by <a href="https://github.com/ed22699"><code>@​ed22699</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/pull/43067">#43067</a></li>
</ul>
<h3>MusicFlamingo</h3>
<p>Music Flamingo is a fully open large audio–language model designed
for robust understanding and reasoning over music. It builds upon the
Audio Flamingo 3 architecture by including Rotary Time Embeddings
(RoTE), which injects temporal position information to enable the model
to handle audio sequences up to 20 minutes. The model features a unified
audio encoder across speech, sound, and music with special sound
boundary tokens for improved audio sequence modeling.</p>
<p><strong>Links:</strong> <a
href="https://huggingface.co/docs/transformers/main/en/model_doc/musicflamingo">Documentation</a>
| <a href="https://huggingface.co/papers/2511.10289">Paper</a></p>
<ul>
<li>Add Music Flamingo (<a
href="https://redirect.github.com/huggingface/transformers/issues/43538">#43538</a>)
by <a href="https://github.com/lashahub"><code>@​lashahub</code></a> in
<a
href="https://redirect.github.com/huggingface/transformers/pull/43538">#43538</a></li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="c1c34249fa"><code>c1c3424</code></a>
update</li>
<li><a
href="20bff6865a"><code>20bff68</code></a>
update release workflow</li>
<li><a
href="89564412a5"><code>8956441</code></a>
v5.5.0</li>
<li><a
href="5135e5efa7"><code>5135e5e</code></a>
casually dropping the most capable open weights on the planet (<a
href="https://redirect.github.com/huggingface/transformers/issues/45192">#45192</a>)</li>
<li><a
href="a594e09e39"><code>a594e09</code></a>
Internalise the NomicBERT model (<a
href="https://redirect.github.com/huggingface/transformers/issues/43067">#43067</a>)</li>
<li><a
href="4932e9721e"><code>4932e97</code></a>
Fix resized LM head weights being overwritten by post_init (<a
href="https://redirect.github.com/huggingface/transformers/issues/45079">#45079</a>)</li>
<li><a
href="57e8413954"><code>57e8413</code></a>
[Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (<a
href="https://redirect.github.com/huggingface/transformers/issues/45124">#45124</a>)</li>
<li><a
href="b10552e99d"><code>b10552e</code></a>
Fix TypeError: 'NoneType' object is not iterable in
GenerationMixin.generate ...</li>
<li><a
href="423f2a31d2"><code>423f2a3</code></a>
fix(models): Fix dtype mismatch in SwitchTransformers and
TimmWrapperModel (#...</li>
<li><a
href="ade7a05a42"><code>ade7a05</code></a>
Generalize gemma vision mask to videos (<a
href="https://redirect.github.com/huggingface/transformers/issues/45185">#45185</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/huggingface/transformers/compare/v5.3.0...v5.5.0">compare
view</a></li>
</ul>
</details>
<br />


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dependabot[bot]
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committed by GitHub
parent 3d4426d427
commit b27f55498f
1 changed files with 157 additions and 142 deletions
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