[Dependabot] Update(deps): Bump transformers from 4.57.3 to 5.6.2

[dependabot]

Bumps transformers from 4.57.3 to 5.6.2.

Release notes

Sourced from transformers's releases.

Patch release v5.6.2

Qwen 3.5 and 3.6 MoE (text-only) were broken when using with FP8. It should now work again with this 🫡

• Fix configuration reading and error handling for kernels (huggingface/transformers#45610) by @​hmellor

Full Changelog: huggingface/transformers@v5.6.1...v5.6.2

Patch release v5.6.1

Flash attention path was broken! Sorry everyone for this one 🤗

• Fix AttributeError on s_aux=None in flash_attention_forward (huggingface/transformers#45589) by @​jamesbraza

Release v5.6.0

New Model additions

OpenAI Privacy Filter

OpenAI Privacy Filter is a bidirectional token-classification model for personally identifiable information (PII) detection and masking in text. It is intended for high-throughput data sanitization workflows where teams need a model that they can run on-premises that is fast, context-aware, and tunable. The model labels an input sequence in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure, predicting probability distributions over 8 privacy-related output categories for each input token.

Links: Documentation

• [Privacy Filter] Add model (#45580) by @​vasqu in #45580

QianfanOCR

Qianfan-OCR is a 4B-parameter end-to-end document intelligence model developed by Baidu that performs direct image-to-text conversion without traditional multi-stage OCR pipelines. It supports a broad range of prompt-driven tasks including structured document parsing, table extraction, chart understanding, document question answering, and key information extraction all within one unified model. The model features a unique "Layout-as-Thought" capability that generates structured layout representations before producing final outputs, making it particularly effective for complex documents with mixed element types.

Links: Documentation | Paper

• add Qianfan-OCR model definition (#45280) by @​marvinzh in #45280

SAM3-LiteText

SAM3-LiteText is a lightweight variant of SAM3 that replaces the heavy SAM3 text encoder (353M parameters) with a compact MobileCLIP-based text encoder optimized through knowledge distillation, while keeping the SAM3 ViT-H image encoder intact. This reduces text encoder parameters by up to 88% while maintaining segmentation performance comparable to the original model. The model enables efficient vision-language segmentation by addressing the redundancy found in text prompting for segmentation tasks.

Links: Documentation | Paper

• Add SAM3-LiteText (#44320) by @​NielsRogge in #44320

SLANet

SLANet and SLANet_plus are lightweight models designed for table structure recognition, focusing on accurately recognizing table structures in documents and natural scenes. The model improves accuracy and inference speed by adopting a CPU-friendly lightweight backbone network PP-LCNet, a high-low-level feature fusion module CSP-PAN, and a feature decoding module SLA Head that aligns structural and positional information. SLANet was developed by Baidu PaddlePaddle Vision Team as part of their table structure recognition solutions.

Links: Documentation

• [Model] Add SLANet Model Support (#45532) by @​zhang-prog in #45532

Breaking changes

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Commits

• aa935fb v5.6.2
• c1f8458 Fix configuration reading and error handling for kernels (#45610)
• 45add8a v5.6.1
• 6c317af Fix AttributeError on s_aux=None in flash_attention_forward (#45589)
• 3e80155 v5.6.0
• 7187177 [Privacy Filter] Add model (#45580)
• 864db66 Add ForSequenceClassification heads for the OLMo family (#45551)
• bca7eee Add IndexCache support for GLM5 DSA (#45424)
• 5617561 Fix redundant logic in video processing SmolVLM (#45272)
• 0ea540e Add /v1/completions endpoint (OpenAI legacy completions API) to `transformers...
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