build(deps): bump transformers from 5.16.1 to 5.17.0 in /skills/remove-ai-marks/scripts

[dependabot]

Bumps transformers from 5.16.1 to 5.17.0.

Release notes

Sourced from transformers's releases.

Release 5.17.0

Release v5.17.0

New Model additions

HYV4

Hy4-Preview is a 780B-parameter mixture-of-experts language model that activates 49B parameters per token. Each MoE layer holds 256 routed experts plus one always-active shared expert and routes every token to 8 of them. The context window is 1M tokens.

The architecture combines four features:

• Multi-head Latent Attention (MLA) compresses keys and values into a low-rank latent (kv_lora_rank) that kv_b_proj expands back to one key/value per query head.
• DeepSeek Sparse Attention (DSA) selects index_topk keys per query with a lightweight indexer. Following IndexShare, only the layers marked "full" in indexer_types run an indexer; "shared" layers reuse the previous full layer's selection.
• Gated MLA with learnable attention sinks, where each head owns a sink logit that participates in the softmax and contributes no value, as in GPT-OSS.
• Independent Hyper-Connections (iHC) replace the plain residual path with hc_mult parallel residual streams that are collapsed before, and redistributed after, every sublayer.

The implementation does not execute the multi-token prediction (MTP) layers. Released checkpoints keep those weights so that other runtimes can use them for speculative decoding; they are ignored at load time.

Links: Documentation

• Add h4 (#48473) by @​ArthurZucker in #48473

VibeVoice

VibeVoice is a novel framework for synthesizing high-fidelity, long-form speech with multiple speakers by employing a next-token diffusion approach within a Large Language Model (LLM) structure. It's designed to capture the authentic conversational "vibe" and is particularly suited for generating audio content like podcasts and multi-participant audiobooks.

Links: Documentation

• Implement VibeVoice (#40546) by @​pengzhiliang in #40546

NeoMME

NeoMME is a family of efficient 260M and 800M parameter multimodal-native multilingual foundation encoders from H Company. It processes multilingual text tokens and raw image patches in a single bidirectional Transformer encoder, without a separately pretrained vision tower or causal language model.

NeoMME-Retriever is a model fine-tuned from the NeoMME backbone for visual document retrieval with joint late-interaction and dense objectives. It takes text queries and documents (text or page screenshots) and produces multi-vector embeddings for MeanMaxSim scoring (late-interaction) and mean-pooled embeddings for cosine similarity (dense).

Links: Documentation

... (truncated)

Commits

• 856157a v5.17.0
• 5b7dcb0 MRoPE continued (#48594)
• 50bbcc6 [fix] Update stale expected strings in HunYuanVL integration tests (#48646)
• e8bcd79 [Quantizaiton]support 5/6/7 bits in AutoRound (#48481)
• 3283d5f [fix] Update stale golden values and fix expected_logits shape in FlavaForPre...
• 5f47b5a [tests] Fix integration test golden values broken by fast image processor def...
• fc50134 Add Fun-ASR-Nano model (#46180)
• d9fe823 Fix YOLOS device mismatch with device_map="auto" (#46886)
• cbc1651 [Generate] Avoid unconditionally downloading remote hub file (#48620)
• bd05a4b Honor shift_labels in decoder-only LLM/VLM losses (#48493)
• Additional commits viewable in compare view

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