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

• Add NeoMME and NeoMME-Retriever (#47992) by @tonywu71 in #47992

Fun-ASR-Nano

Fun-ASR-Nano is an 800M-parameter end-to-end speech recognition model developed by Alibaba DAMO Academy's FunAudioLLM team. It achieves state-of-the-art performance on Chinese, English, and Japanese ASR benchmarks while being significantly smaller than comparable models.

Key features are

• Chinese, English, and Japanese, including 7 Chinese dialects and 26 regional accents
• Hotword customization for domain-specific vocabulary
• Native punctuation output (no separate punctuation model needed)

Links: [Documentation](https://huggin…

[余略，详见 https://github.com/huggingface/transformers/releases/tag/v5.17.0]