This is an automated email from the ASF dual-hosted git repository.
github-merge-queue[bot] pushed a commit to branch
gh-readonly-queue/main/pr-6614-dff1f5ce02fd6c28baa41732c49cddc6db0bbe95
in repository https://gitbox.apache.org/repos/asf/texera.git
commit d5363d191ff54f5eb956c3aaeeb63ac8e4ddcfa1
Author: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
AuthorDate: Wed Jul 22 00:22:43 2026 +0000
fix(deps, pyamber): bump transformers from 5.3.0 to 5.5.0 in /amber (#6614)
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 "soft tokens" (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 "above," "below," "left
of," and "right of."</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="https://github.com/huggingface/transformers/commit/c1c34249fa27deefbd4a377dfbf883a39baf5c6d";><code>c1c3424</code></a>
update</li>
<li><a
href="https://github.com/huggingface/transformers/commit/20bff6865a756a074f5b893b57f0ae438b25ec46";><code>20bff68</code></a>
update release workflow</li>
<li><a
href="https://github.com/huggingface/transformers/commit/89564412a56ae6581f8aa48a533a835860dc9f43";><code>8956441</code></a>
v5.5.0</li>
<li><a
href="https://github.com/huggingface/transformers/commit/5135e5efa7203cd23aac0866de12dfeef038422d";><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="https://github.com/huggingface/transformers/commit/a594e09e3924120f1f5508e7d81946bf3504df2b";><code>a594e09</code></a>
Internalise the NomicBERT model (<a
href="https://redirect.github.com/huggingface/transformers/issues/43067";>#43067</a>)</li>
<li><a
href="https://github.com/huggingface/transformers/commit/4932e9721e230bea915341e7f04db32885b6c6af";><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="https://github.com/huggingface/transformers/commit/57e84139542c8c297873f35fcd25f66ffcf132ae";><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="https://github.com/huggingface/transformers/commit/b10552e99dc4974b30126995baea455df43f8476";><code>b10552e</code></a>
Fix TypeError: 'NoneType' object is not iterable in
GenerationMixin.generate ...</li>
<li><a
href="https://github.com/huggingface/transformers/commit/423f2a31d2bd05bdc1dc30dd938389edaa998fde";><code>423f2a3</code></a>
fix(models): Fix dtype mismatch in SwitchTransformers and
TimmWrapperModel (#...</li>
<li><a
href="https://github.com/huggingface/transformers/commit/ade7a05a42bf53b183bb78c181743be063c5ff14";><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 />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't
alter it yourself. You can also trigger a rebase manually by commenting
`@dependabot rebase`.
[//]: # (dependabot-automerge-start)
[//]: # (dependabot-automerge-end)
---
<details>
<summary>Dependabot commands and options</summary>
<br />
You can trigger Dependabot actions by commenting on this PR:
- `@dependabot rebase` will rebase this PR
- `@dependabot recreate` will recreate this PR, overwriting any edits
that have been made to it
- `@dependabot show <dependency name> ignore conditions` will show all
of the ignore conditions of the specified dependency
- `@dependabot ignore this major version` will close this PR and stop
Dependabot creating any more for this major version (unless you reopen
the PR or upgrade to it yourself)
- `@dependabot ignore this minor version` will close this PR and stop
Dependabot creating any more for this minor version (unless you reopen
the PR or upgrade to it yourself)
- `@dependabot ignore this dependency` will close this PR and stop
Dependabot creating any more for this dependency (unless you reopen the
PR or upgrade to it yourself)
You can disable automated security fix PRs for this repo from the
[Security Alerts page](https://github.com/apache/texera/network/alerts).
</details>
---------
Signed-off-by: dependabot[bot] <[email protected]>
Co-authored-by: dependabot[bot]
<49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Xinyuan Lin <[email protected]>
Co-authored-by: Claude Fable 5 <[email protected]>
---
amber/LICENSE-binary-python | 2 +-
amber/operator-requirements.txt | 2 +-
2 files changed, 2 insertions(+), 2 deletions(-)
diff --git a/amber/LICENSE-binary-python b/amber/LICENSE-binary-python
index b50b5cada7..b2d772c7f6 100644
--- a/amber/LICENSE-binary-python
+++ b/amber/LICENSE-binary-python
@@ -233,7 +233,7 @@ Python packages:
- safetensors==0.8.0
- tenacity==8.5.0
- tokenizers==0.22.2
- - transformers==5.3.0
+ - transformers==5.5.0
- tzdata==2026.3
- websocket-client==1.9.0
- yarl==1.24.2
diff --git a/amber/operator-requirements.txt b/amber/operator-requirements.txt
index 4765b205cc..9759f624c4 100644
--- a/amber/operator-requirements.txt
+++ b/amber/operator-requirements.txt
@@ -26,7 +26,7 @@ torch==2.12.1+cpu ; platform_system == "Linux" and
platform_machine == "x86_64"
torch==2.12.1 ; platform_system != "Linux" or platform_machine != "x86_64"
scikit-learn==1.7.2
-transformers==5.3.0
+transformers==5.5.0
# Not imported by any operator template: plotly's create_ternary_contour
# (ternary contour operator, #4193) requires scikit-image at runtime.
scikit-image==0.25.2
