Bump transformers from 4.34.0 to 5.3.0

[dependabot]

Bumps transformers from 4.34.0 to 5.3.0.

Release notes

Sourced from transformers's releases.

v5.1.0: EXAONE-MoE, PP-DocLayoutV3, Youtu-LLM, GLM-OCR

New Model additions

EXAONE-MoE

K-EXAONE is a large-scale multilingual language model developed by LG AI Research. Built using a Mixture-of-Experts architecture, K-EXAONE features 236 billion total parameters, with 23 billion active during inference. Performance evaluations across various benchmarks demonstrate that K-EXAONE excels in reasoning, agentic capabilities, general knowledge, multilingual understanding, and long-context processing.

• Add EXAONE-MoE implementations (#43080) by @​nuxlear

PP-DocLayoutV3

PP-DocLayoutV3 is a unified and high-efficiency model designed for comprehensive layout analysis. It addresses the challenges of complex physical distortions—such as skewing, curving, and adverse lighting—by integrating instance segmentation and reading order prediction into a single, end-to-end framework.

• [Model] Add PP-DocLayoutV3 Model Support (#43098) by @​zhang-prog

Youtu-LLM

Youtu-LLM is a new, small, yet powerful LLM, contains only 1.96B parameters, supports 128k long context, and has native agentic talents. On general evaluations, Youtu-LLM significantly outperforms SOTA LLMs of similar size in terms of Commonsense, STEM, Coding and Long Context capabilities; in agent-related testing, Youtu-LLM surpasses larger-sized leaders and is truly capable of completing multiple end2end agent tasks.

• Add Youtu-LLM model (#43166) by @​LuJunru

GlmOcr

GLM-OCR is a multimodal OCR model for complex document understanding, built on the GLM-V encoder–decoder architecture. It introduces Multi-Token Prediction (MTP) loss and stable full-task reinforcement learning to improve training efficiency, recognition accuracy, and generalization. The model integrates the CogViT visual encoder pre-trained on large-scale image–text data, a lightweight cross-modal connector with efficient token downsampling, and a GLM-0.5B language decoder. Combined with a two-stage pipeline of layout analysis and parallel recognition based on PP-DocLayout-V3, GLM-OCR delivers robust and high-quality OCR performance across diverse document layouts.

• [GLM-OCR] GLM-OCR Support (#43391)by @​zRzRzRzRzRzRzR

Breaking changes

• 🚨 T5Gemma2 model structure (#43633) - Makes sure that the attn implementation is set to all sub-configs. The config.encoder.text_config was not getting its attn set because we aren't passing it to PreTrainedModel.init. We can't change the model structure without breaking so I manually re-added a call to self.adjust_attn_implemetation in modeling code

• 🚨 Generation cache preparation (#43679) - Refactors cache initialization in generation to ensure sliding window configurations are now properly respected. Previously, some models (like Afmoe) created caches without passing the model config, causing sliding window limits to be ignored. This is breaking because models with sliding window attention will now enforce their window size limits during generation, which may change generation behavior or require adjusting sequence lengths in existing code.

• 🚨 Delete duplicate code in backbone utils (#43323) - This PR cleans up backbone utilities. Specifically, we have currently 5 different config attr to decide which backbone to load, most of which can be merged into one and seem redundant After this PR, we'll have only one config.backbone_config as a single source of truth. The models will load the backbone from_config and load pretrained weights only if the checkpoint has any weights saved. The overall idea is same as in other composite models. A few config arguments are removed as a result.

• 🚨 Refactor DETR to updated standards (#41549) - standardizes the DETR model to be closer to other vision models in the library.

• 🚨Fix floating-point precision in JanusImageProcessor resize (#43187) - replaces an int() with round(), expect light numerical differences

• 🚨 Remove deprecated AnnotionFormat (#42983) - removes a missnamed class in favour of AnnotationFormat.

... (truncated)

Commits

• aad13b8 v5.3.0
• f6c63a6 protect imports (#44437)
• fd6bc38 [vllm + v5 fix] handle TokenizersBackend fallback properly for v5 (#44255)
• 30c4801 Fix CLI NameError: name 'TypeAdapter' is not defined (#44256)
• ee4c220 Enforce min length in some generate tests (#44401)
• a4f3df0 [tiny] Add olmo_hybrid to tokenizer auto-mapping (#44416)
• 1313588 Update PR template (#44415)
• 7235d44 Add eurobert (#39455)
• f60c4e9 Add Qwen3.5 support for sequence classification (#44406)
• fa7f4b6 update the expected output for qwen2_5_vl w/ pytorch 2.10 XPU (#44426)
• Additional commits viewable in compare view

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Note

Medium Risk
Major-version transformers bump with no code updates; compatibility with torch 1.13.1 and Wav2Vec2 loading paths should be verified before merge.

Overview
Dependency-only change: pins transformers from 4.34.0 to 5.3.0 in requirements.txt (major 4→5).

The repo loads Wav2Vec2ForPreTraining (facebook/mms-300m / wav2vec2-xls-r-300m) for frozen intermediate-layer features in synthesis and preprocessing—no application code changes in this PR. Upgrading still means validating installs against the existing torch 1.13.1+cu117 stack and re-running W2V feature extraction / inference smoke tests, since Transformers 5.x includes breaking API and behavior changes that can affect model loading even when usage looks narrow.

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Incompatible PyTorch with transformers

High Severity

Bumping transformers to 5.3.0 while torch stays at 1.13.1 conflicts with Hugging Face’s v5 support matrix (PyTorch 2.4+). A normal pip install -r requirements.txt can fail or leave Wav2Vec2-based inference and preprocessing broken at import or runtime.

Additional Locations (1)

• requirements.txt#L10-L12

 

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