chore(deps): bump transformers from 4.57.2 to 5.5.4 in /cmd/runtimes/deepspeed

[dependabot]

Bumps transformers from 4.57.2 to 5.5.4.

Release notes

Sourced from transformers's releases.

Patch release v5.5.4

This is mostly some fixes that are good to have asap, mostly for tokenizers; ** Fix Kimi-K2.5 tokenizer regression and _patch_mistral_regex Attribute… (#45305) by ArthurZucker

For training: ** Fix #45305 + add regression test GAS (#45349) by florian6973, SunMarc ** Fix IndexError with DeepSpeed ZeRO-3 when kernels rotary is active (#…) by ArthurZucker

And for Qwen2.5-VL : ** Fix Qwen2.5-VL temporal RoPE scaling applied to still images (#45330) by Kash6, zucchini-nlp

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)

... (truncated)

Commits

• 75d3bdc v5.5.4
• 285dcec Fix IndexError with DeepSpeed ZeRO-3 when kernels rotary is active (#45414)
• 16f5c6c Fix Qwen2.5-VL temporal RoPE scaling applied to still images (#45330)
• 1b5a1d9 Fix #45305 + add regression test GAS (#45349)
• fdffefb Fix Kimi-K2.5 tokenizer regression and _patch_mistral_regex AttributeError (#...
• 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)
• Additional commits viewable in compare view

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[dependabot]

Superseded by #119.