chore(deps): bump transformers from 4.48.3 to 4.53.2 in /backend/python/coqui

[dependabot]

Bumps transformers from 4.48.3 to 4.53.2.

Release notes

Sourced from transformers's releases.

Patch Release v4.53.2

This patch contains the following bug fixes:

• Fix some bug for finetune and batch infer For GLM-4.1V (#39090)
• [bugfix] fix flash attention 2 unavailable error on Ascend NPU (#39166)
• Fix errors when use verl to train GLM4.1v model (#39199)
• [pagged-attention] fix off-by-1 error in pagged attention generation (#39258)
• [smollm3] add tokenizer mapping for smollm3 (#39271)
• [sliding window] revert and deprecate (#39301)
• fix Glm4v batch videos forward (#39172)
• Add a default value for position_ids in masking_utils (#39310)

Patch Release v4.53.1

This patch contains several bug fixes. The following commits are included:

• Fix: unprotected import of tp plugin (#39083)
• Fix key mapping for VLMs (#39029)
• Several fixes for Gemma3n(#39135)
• [qwen2-vl] fix FA2 inference (#39121)
• [smolvlm] fix video inference (#39147)
• Fix multimodal processor get duplicate arguments when receive kwargs for initialization (#39125)
• when delaying optimizer creation only prepare the model (#39152)
• Add packed tensor format support for flex/sdpa/eager through the mask! (#39194)

Release v4.53.0

Gemma3n

Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages.

Gemma 3n models use selective parameter activation technology to reduce resource requirements. This technique allows the models to operate at an effective size of 2B and 4B parameters, which is lower than the total number of parameters they contain. For more information on Gemma 3n's efficient parameter management technology, see the Gemma 3n page.

from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
torch_dtype=torch.bfloat16,
model="google/gemma-3n-e4b",
device="cuda",
)
output = pipe(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
text="<image_soft_token> in this image, there is"
)
print(output)
</tr></table>

... (truncated)

Commits

• 37f8b0b style
• d15e157 finally fix glm4v accordingly
• 9873b2f fix attr post conflict
• d6f3155 Fix conflict
• cbac426 Add a default value for position_ids in masking_utils (#39310)
• c122145 fix Glm4v batch videos forward (#39172)
• 9da3f7d [sliding window] revert and deprecate (#39301)
• b2fbc02 [smollm3] add tokenizer mapping for smollm3 (#39271)
• c30a6b7 [pagged-attention] fix off-by-1 error in pagged attention generation (#39258)
• be7d1a9 Fix errors when use verl to train GLM4.1v model (#39199)
• Additional commits viewable in compare view

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