build(deps): bump transformers from 4.51.3 to 5.16.1 in /Shared

[dependabot]

Bumps transformers from 4.51.3 to 5.16.1.

Release notes

Sourced from transformers's releases.

Release v5.16.1

This is a special release as we include GLM! (and a few small fixes)

GLM-5.3-Flash

GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.

GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.

Links: Documentation

• [Glm 5.3 Flash] GLM 5.3 Flash Support (#48342) by @​Dovis01 in #48342

Small patch fixes

Mainly BC behavior for TP and pinning a hf kernel for security reasons 🤗

• Restore BC for the tensor-parallel API (#48300) by @​ArthurZucker
• Fix kernel commit and repo paths for ESMFold2 (#48186) by @​Rocketknight1

Full Changelog: huggingface/transformers@v5.16.0...v5.16.1

Release: v5.16.0

Release v5.16.0

New Model additions

Qwen4-Exp

Qwen4-Exp builds on Qwen3.5's hybrid text and multimodal architecture with three key components: GatedResidual (GR), Qwen Sparse Attention (QSA), and Per-Layer Embedding (PLE).

GR is a Qwen-developed residual architecture that combines Hyper-Connection with GatedNorm. It mixes multiple residual streams with fine-grained elementwise gating before each attention and Mixture-of-Experts (MoE) block, then controls how much of the block output is injected back into each stream.

QSA uses multiple query heads to score compressed key blocks, selects the most relevant contiguous token blocks, and keeps the incomplete trailing block uncompressed. This block-level selection reduces indexing overhead and improves memory locality for long sequences. Combined with Gated DeltaNet, QSA makes Qwen4-Exp the first hybrid architecture to integrate linear and sparse attention, substantially improving inference efficiency for long-context workloads.

PLE enriches selected decoder layers with layer-specific lexical features derived from hashed token n-grams and a dilated depthwise convolution.

Links: Documentation

• Add Qwen4Exp model (#48337) by @​Cyrilvallez in #48337

GraniteSpeech5

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Commits

• 93c8b7b v5.16.1
• cdaca83 [Glm 5.3 Flash] GLM 5.3 Flash Support (#48342)
• b366ad3 Fix kernel commit and repo paths for ESMFold2 (#48186)
• f2f4327 Restore BC for the tensor-parallel API (#48300)
• 93d1bcf release: v5.16.0
• 276c5c4 Add Qwen4Exp model (#48337)
• d3cdaec Fix video-llama modular conversion (#48336)
• 949e301 CI: gate the hunyuan-moe slow test (#48330)
• 5fcf605 Add a regression test for force_accelerate_hooks signature preservation (#48...
• 27e7f6c Add an opt-in per-frame pixel cap (cap_pixels_per_frame) to the Qwen3-VL vide...
• Additional commits viewable in compare view

[dependabot]

Superseded by #78.