Bump transformers from 4.52.4 to 5.5.3

[dependabot]

Bumps transformers from 4.52.4 to 5.5.3.

Release notes

Sourced from transformers's releases.

Patch release: v5.5.3

Small patch release to fix device_map support for Gemma4! It contains the following commit:

• [gemma4] Fix device map auto (#45347) by @​Cyrilvallez

Patch release: v5.5.2

Small patch dedicated to optimizing gemma4, fixing inference with use_cache=False due to k/v states sharing between layers, as well as conversion mappings for some models that would inconsistently serialize their weight names. It contains the following PRs:

• Add MoE to Gemma4 TP plan (#45219) by @​sywangyi and @​Cyrilvallez
• [gemma4] Dissociate kv states sharing from the Cache (#45312) by @​Cyrilvallez
• [gemma4] Remove all shared weights, and silently skip them during loading (#45336) by @​Cyrilvallez
• Fix conversion mappings for vlms (#45340) by @​Cyrilvallez

Patch release v5.5.1

This patch is very small and focuses on vLLM and Gemma4!

** Fix export for gemma4 and add Integration tests (#45285) by @​Cyrilvallez ** Fix vllm cis (#45139) by @​ArthurZucker

Release v5.5.0

New Model additions

Gemma4

Gemma 4 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.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. 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:

• The total number of pixels must fit within a patch budget
• Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not 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).

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 280 soft tokens per image.

Soft Tokens	Patches (before pooling)	Approx. Image Area
70	630	~161K pixels
140	1,260	~323K pixels
280	2,520	~645K pixels

... (truncated)

Commits

• c6c8503 Release: v5.5.3
• dccd162 [gemma4] Fix device map auto (#45347)
• 52d24b0 Release: 5.5.2
• 5df3452 [gemma4] Remove all shared weights, and silently skip them during loading (#4...
• 2cf8ff0 Fix conversion mappings for vlms (#45340)
• 286e789 [gemma4] Dissociate kv states sharing from the Cache (#45312)
• 23c562c Add MoE to Gemma4 TP plan (#45219)
• a1b90c4 v5.5.1
• 4f8e2bb Fix export for gemma4 and add Integration tests (#45285)
• d30a963 Fix vllm cis (#45139)
• Additional commits viewable in compare view

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