Bump transformers from 5.5.0 to 5.10.1 in /python

[dependabot]

Bumps transformers from 5.5.0 to 5.10.1.

Release notes

Sourced from transformers's releases.

Release v5.10.1

v5.10.0 was yanked as we publish on a corrupted branch. Sorry everyone, this happens when we rush a release!!!

New Model additions

Gemma4 unified+ Gemma4 MTP

Gemma 4 12B Unified is an encoder-free multimodal model with pretrained and instruction-tuned variants. Unlike standard Gemma 4, which uses dedicated encoder towers, Gemma 4 12B Unified projects raw inputs directly into the language model's embedding space through lightweight linear pipelines. This results in a simpler architecture while maintaining strong multimodal performance.

Key differences from standard Gemma 4:

• No Vision Tower: Raw pixel patches are projected directly into LM space via a Dense + LayerNorm pipeline with factorized 2D positional embeddings, replacing the vision encoder.
• No Audio Tower: Raw 16 kHz waveform samples are chunked into fixed-length frames and projected through a simple RMSNorm → Linear pipeline, replacing the mel spectrogram + Conformer encoder.
• Shared Multimodal Pipeline: Both vision and audio use the same Gemma4UnifiedMultimodalEmbedder (RMSNorm → Linear) for the final projection to text hidden space.

You can find the original Gemma 4 12B Unified checkpoints under the Gemma 4 release.

• who needs encoders? (#46385) by @​douglas-reid @​sgerrard @​vasqu @​molbap

Sapiens2

Sapiens2 is a family of high-resolution vision transformers pretrained on ~1 billion curated human images, designed for human-centric computer vision tasks including pose estimation, body-part segmentation, surface normal estimation, and pointmap estimation. The models scale from 0.4B to 5B parameters and train at native 1K resolution, with hierarchical 4K variants for extended spatial reasoning. Sapiens2 achieves substantial improvements over its predecessor with +4 mAP in pose estimation, +24.3 mIoU in body-part segmentation, and 45.6% error reduction in normal estimation.

Links: Documentation | Paper

• Add Sapiens2 Model (#45919) by @​guarin in #45919

DeepSeek-OCR-2

DeepSeek-OCR-2 is an OCR-specialized vision-language model built on a distinctive architecture that combines a SAM ViT-B vision encoder with a Qwen2 hybrid attention encoder, connected through an MLP projector to a DeepSeek-V2 Mixture-of-Experts (MoE) language model. The model features a hybrid attention mechanism that applies bidirectional attention over image tokens and causal attention over query tokens, enabling efficient and accurate document understanding. It supports both plain OCR tasks and grounding capabilities with coordinate-aware output for document conversion to markdown format.

Links: Documentation

• Add Deepseek-OCR-2 model (#45075) by @​thisisiron in #45075

Mellum

Mellum is a code-focused Mixture-of-Experts language model developed by JetBrains. It is derived from the Qwen3-MoE architecture with per-layer-type RoPE and interleaved sliding window attention. The model has 12B total parameters with 2.5B active parameters per token, using 64 routed experts with 8 activated per token across 28 layers.

Links: Documentation

• feat: Add support for JetBrains' Mellum v2 code generation model (#46112) by @​shadeMe in #46112

Breaking changes

The Gemma4 vision pooler now casts inputs to float32 before scaling to prevent float16 overflow (inf saturation) with large checkpoints, which may cause minor numerical differences in outputs for users running Gemma-4 vision models in float16.

• 🚨 Fix float16 overflow in Gemma4 vision pooler (#46277) by @​Bluear7878

Audio Language Models (ALMs) now have a dedicated base model class without a language modeling head, aligning them with the design of Vision Language Models (VLMs); users relying on the previous model class structure should update their code to use the new base model class where appropriate.

• 🚨 [ALM] Add base model without head (#45534) by @​eustlb

... (truncated)

Commits

• 90c3ae5 Patch because we had to yank 5.10 because the release branch was not up to date
• 0bd94b3 v5.10.0
• 1423d22 who needs encoders? (#46385)
• 50eb20a Fix dsv4 dequant + tp/ep (#46378)
• 74464e8 Fix wrong changes produced by style/repo. check bot (#46371)
• 1b8ec34 Fix path traversal when saving Bark voice preset embeddings (#46237)
• e820678 Add Sapiens2 Model (#45919)
• 595721c Pass library_name/version to Hub calls via a shared HfApi (#46318)
• 0f0036c docs: update ACL Anthology URL in CITATION.cff (#46352)
• fa6c830 DeepGEMM BF16 + mixed FP8/FP4 + MegaMoE + refactor (#45634)
• Additional commits viewable in compare view

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[github-actions]

MAF Automated Review — Iteration 1

Result: Findings reported
Scope: full PR (1 commit(s)): ca8ef4673379
Model: claude-opus-4.8

Overview

This Dependabot change touches only python/uv.lock: it advances transformers across the 4→5 major boundary to 5.10.1 (piggy-backing a huggingface-hub 0.x→1.x bump) and simplifies redundant sys_platform environment markers. The marker churn was verified to be a semantic no-op because the resolution universe is pinned to exactly {darwin, linux, win32}, so dropping the three-way disjunction changes no install set. The one residual risk is real and confirmed by reproduction: transformers 5.x deleted the text2text-generation (the connector's default) and summarization pipeline tasks, so HuggingFaceTextCompletion now fails at construction for two of its three advertised tasks, and the mocked unit tests hide it.

Reviewed the supplied pull-request change set across correctness, security/reliability, architecture, and failure behavior.
1 verified finding remained after source verification (1 high) across 1 file. Details are attached to the affected lines below.

Affected areas: python/uv.lock

[github-actions]

Bumping transformers to 5.10.1 crosses the 4→5 major boundary, which removed the text2text-generation and summarization pipeline tasks — in 5.10.1, transformers.pipelines.SUPPORTED_TASKS contains only text-generation of the three (verified against the pinned version). HuggingFaceTextCompletion defaults to task="text2text-generation" and calls pipeline(task=task, ...) at python/semantic_kernel/connectors/ai/hugging_face/services/hf_text_completion.py:72, so constructing the service with the default task — or with task="summarization" — now raises KeyError: "Unknown task ..." on the first call. Two of the connector's three advertised tasks are broken at construction. The existing unit tests patch pipeline, so CI stays green while the real connector fails. Please either cap the dependency at transformers[torch] >=4.28,<5.0 in python/pyproject.toml (keeping the lock on 4.57.6), or update the connector to map the removed seq2seq tasks onto a 5.x-supported task and add a non-mocked construction test before adopting 5.x.