Hugging Face Transformers Release 5.15.0

Release v5.15.0

New Model additions

Meta Muse Glimmer

Muse Glimmer, released today, is Meta's new multimodal model, especially designed for agentic use cases. Distilled from Muse to 30B parameters, and released under the Apache 2.0 license, it can be deployed to local setups for privacy-aware applications such as coding, document analysis, personal assistants, Claw- or Hermes-like setups.

Muse Glimmer is a dense 30B parameter model consisting of:

• 2B ViT-style encoder for vision (Perception Encoder)
• 28B parameter text decoder

We're covering it in the following blogpost: http://hf.co/blog/muse-glimmer

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GraniteMoeSWA & GraniteSWA

Links: Documentation

• Add Granite-swa and Granitemoe-swa model support (#47179) by @daviswer in #47179

Links: Documentation

• Add Granite-swa and Granitemoe-swa model support (#47179) by @daviswer in #47179

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A.X-K1 & A.X-K2

Links: Documentation

• Add AXK2 from SKT (#47528) by @vasqu in #47528

Links: Documentation

• add_axk1 (#46867) by @kmswin1 in #46867

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Cosmos3 Edge

Links: Documentation

• Add Cosmos3 Edge model support (#47181) by @atharvajoshi10 in #47181

Breaking changes

Kernels are now opt-in rather than mandatory for linear attention models (Mamba, GDN, Conv-only, etc.), so users who relied on automatic kernel selection must explicitly enable kernels to maintain previous behavior.

• [Kernels] Refactor all linear attn models & native kernels fallback (#47630) by @vasqu

The cache cropping API now only accepts negative values (relative offsets) instead of absolute sizes, so users calling crop methods directly must update their code to pass negative values accordingly.

• [cache] Cropping can only be done with negative values (#47720) by @Cyrilvallez

T5 and its model family (MT5, LongT5, etc.) now support SDPA and other attention backends via ALL_ATTENTION_FUNCTIONS, meaning the default attention implementation may change and users relying on the previous eager-only path should explicitly set attn_implementation="eager" if needed.

• Enable SDPA (and other attention backends) for T5 and propagate to the T5 family (#47014) by @jiqing-feng

Several small private helper functions (e.g., _is_url, _build_image_tokens) have been removed from multimodal processor files, so users or downstream libraries that imported these private functions directly must remove or replace those references.

• :rotating_light: Processors update the rest (#46556) by @zucchini-nlp

Attention

This release includes several attention fixes and improvements, including correcting Multi-Head Latent Attention (MLA) cache compression, optimizing Flash Attention max sequence length computation in vision models, and fixing bugs in CTRL flex-attention and SDPA prefill with position bias. Additional changes refactor linear attention models for better maintainability, make Gemma 4's heterogeneous attention config explicit, and improve MPS support via metal-flash-sdpa integration.

• [Fix] Fix multi-head latent attention (MLA) (#47761) by @remi-or in [#47761]
• Refactor all linear attention models to latest best standards for convolution (#47452) by @Cyrilvallez in [#47452]
• Allow metal-flash-sdpa for OpenAIPrivacyFilter on MPS (#46740) by @ArthurZucker in [#46740]
• Use new per_layer_config for Gemma 4 so that heterogeneous attention config is explicit (#47384) by @hmellor in [#47384]
• add paged attention tests support for XPU (#47163) by @kaixuanliu in [#47163]
• Move value padding into the attention interfaces that need it (#47451) by @hmellor in [#47451]
• Simplify function dispatch for linear attention (#47450) by @Cyrilvallez in [#47450]
• Optimize flash attention max seqlen computation in vision attention (#47170) by @ShareLer in [#47170]
• Fix BlockMask crash in CTRL flex-attention generation (#46854) by @jiqing-feng in [#46854]
• [CB] Automatically switch attention implementation to flash (#47330) by @remi-or in [#47330]
• Fix sdpa prefill with position_bias (#47359) by @Cyrilvallez in [#47359]

Vision

Vision improvements in this release include performance optimizations such as faster image preprocessing for vision-language models (GLM4V, MiniMaxM3-VL, and others) by eliminating redundant tensor copies, and more efficient Flash Attention variable-length paths by precomputing maximum sequence lengths once per forward pass. Several bug fixes were also applied, including correcting dtype alignment in Kosmos2/Kosmos2_5 embedding merges, fixing a position-embedding initialization fallback in Phi4Multimodal, resolving PIL resize parity in Hunyuan-VL, and patching stop-sequence handling in the image-text-to-text pipeline.

• Modularize qwen-format vision processors (#47573) by @zucchini-nlp in [#47573]
• Update daily CI Docker image to torch 2.13.0 / CUDA 13.0 (#47738) by @ydshieh in [#47738]
• Align image feature dtype in kosmos2 and kosmos2_5 embedding merge (#47691) by @<NOT FOUND> in [#47691]
• Speed up image preprocessing for vision-language models (#47453) by @labAxiaoming in [#47453]
• Fix vision position-embedding init width fallback in Phi4Multimodal (#47509) by @<NOT FOUND> in [#47509]
• Fix Hunyuan-VL PIL image resize parity with reference preprocessing (#47233) by @IMvision12 in [#47233]
• Fix image-text-to-text stop_sequence handling (#47032) by @Sunt-ing in [#47032]
• Refactor image loading in tests to use load_test_image helper (#47218) by @LevelVoid in [#47218]

Generation

Several generation improvements and bug fixes were made, including enabling batched audio generation for Qwen2.5/3-Omni, allowing sliding window cache layers to work with speculative decoding, and fixing memory overhead from static cache persistence across generate() calls. Multiple model-specific bugs were also resolved, including crashes in KyutaiSpeechToText, MusicgenForCausalLM, CTRL flex-attention, and assisted decoding for EncoderDecoder cache and OlmoHybrid models.

• Align OlmoHybrid to use a native cache in generate (#47604) by @Cyrilvallez in [#47604]
• [generate] Stop setting the static cache as an attribute to save memory (#47731) by @Cyrilvallez in [#47731]
• Add support for batched Qwen2.5/3-Omni audio generation (#47186) by @IMvision12 in [#47186]
• [cache] Allow sliding window layers to be roll-backed for speculative decoding (#47447) by @Cyrilvallez in [#47447]
• Fix shape mismatch in KyutaiSpeechToText generate() last window (#46952) by @jiqing-feng in [#46952]
• Fix typo in MusicgenForCausalLM.generate() (#46974) by @jiqing-feng in [#46974]
• Fix assisted decoding for models with EncoderDecoder cache & OlmoHybrid (#47361) by @Cyrilvallez in [#47361]

Cache

Several cache-related bugs were fixed, including correcting NemotronH's missing "mlp" layer-type mapping, resolving recurrent-layer padding masks being skipped during chunked prefill and cache continuation for hybrid models, and fixing assisted decoding for models with EncoderDecoderCache and OlmoHybrid. Additional improvements include aligning OlmoHybrid to use a native cache, enabling sliding window layers to support speculative decoding rollback, and stopping the static cache from being stored as a model attribute to reduce unexpected memory overhead.

• [docs] MPS graph cache (#47304) by @stevhliu in [#47304]
• Fix NemotronH: Register "mlp" in the cache layer-type mappings (#47535) by @qgallouedec in [#47535]
• Fix recurrent-layer padding mask being skipped on continued forwards (chunked prefill, cache continuation) (#47087) by @abcgco in [#47087]

Kernels

The kernels python package will very likely be a required dependency for transformers[torch] in the near future. This will help us deliver maximum performance to all users; kernels will only be downloaded from trusted publishers manually approved by the HF team. Please let us know of any issues you're facing beforehands so that we may solidify our integration.

Improved robustness of the kernels integration by refactoring function handling to use layer repos, fixing CI EROFS fallback patches for kernel downloads via HfApi, resolving a positional argument collision in causal_conv1d_fn, and bumping the FP8 kernels version to prevent NaNs.

• [conftest] Fix EROFS fallback for kernel downloads (correct interception point) (#47794) by @ydshieh in [#47794]
• [conftest] Fix EROFS fallback for kernel downloads via HfApi (#47791) by @ydshieh in [#47791]
• [Kernels] Refactor function handling (#46883) by @vasqu in [#46883]
• Kernels and loaders robustification (#47334) by @IlyasMoutawwakil in [#47334]
• Fix causal_conv1d_fn positional activation colliding with hub kernel's seq_idx (#47527) by @qgallouedec in [#47527]
• [FP8] Bump kernels version (#47344) by @vasqu in [#47344]
• [docs] FlashAttention kernel fallback (#47345) by @stevhliu in [#47345]

Quantization

Quantization support was expanded with FP8 kernels for compressed-tensors models, fixes for FP8 module normalization and format-based compression detection, and a multi-device MXFP4 dequantization race condition fix. GPTQ and MXFP4 tests were also extended to cover Intel XPU devices.

• extend tests/quantization/gptq/test_gptq.py::GPTQTestCUDA and tests/q... (#47166) by @sywangyi in [#47166]
• Compressed tensors fp8 (#47216) by @SunMarc in [#47216]
• Fix A.X-K2 fp8 modules_to_not_convert normalization for the gated-norm MLP (#47578) by @kmswin1 in [#47578]
• [Quantization]: Refactor is_quantization_compressed for format-based detection (#47152) by @rigen1048 in [#47152]
• Fix multi-device mxfp4 dequantization race in _convert_moe_packed_tensors (#47423) by @kaixuanliu in [#47423]

Audio

Batched audio generation is now supported for Qwen2.5/3-Omni, and several bug fixes were applied across audio models, including a dtype mismatch in Gemma4 audio feature merging, a bfloat16 positional embedding error in AudioFlamingo3, and missing backend requirement guards for Voxtral. The VibeVoice ASR processor was also updated to make audio input optional and support multiple audios per prompt.

• feat[vLLM x v5]: Make audio optional and support multiple audios in VibeVoice ASR processor (#47483) by @harshaljanjani in [#47483]
• Fix Gemma4 audio feature dtype mismatch in masked_scatter (#47482) by @danielhanchen in [#47482]
• [fix] fix requirements audio feature and proc (#47113) by @eustlb in [#47113]
• [AudioFlamingo3] Fix bfloat16 dtype mismatch in audio encoder positional embedding (#47258) by @snkii in [#47258]

Parallelization

Expanded FSDP support across 94 ForCausalLM model classes with auto-generated FSDP plans, added end-to-end FSDP tests including distributed checkpoint save/load and generation, and introduced a dedicated FSDP CI job. Additionally, fixed a device mismatch bug in create_bidirectional_sliding_window_mask under model parallelism and resolved a tensor parallel inference issue for models with tied embeddings.

• skip fsdp tests when backend is mps (#47601) by @3outeille in [#47601]
• Fix model parallel device mismatch in create_bidirectional_sliding_window_mask (#47560) by @abcgco in [#47560]
• Add FSDP plans to all models (#47165) by @3outeille in [#47165]
• Fix TP inference for tied embedding (#47503) by @3outeille in [#47503]
• Add FSDP CI and end-to-end FSDP tests + save fsdp (#47357) by @3outeille in [#47357]

Tokenization

This release adds native support for Mistral's "tekken" tokenizer format via AutoTokenizer, fixes a CodeLlama tokenizer bug where leading whitespace was incorrectly dropped during decode, and patches a potential ReDoS vulnerability caused by unescaped tokenizer filenames being used as regex patterns in from_pretrained.

• [Mistral] Add native tekken tokenizer support to AutoTokenizer (#47507) by @juliendenize in [#47507]
• Fix CodeLlama tokenizer dropping leading whitespace on decode (#47488) by @SuryanshSS1011 in [#47488]
• Fix potential ReDoS by escaping tokenizer filename used as regex pattern (#47498) by @hameedibrh in [#47498]

Serve

Improved the serve chat parsing to unify streaming and non-streaming paths under a single response parser that handles tool calls, reasoning, and content, simplifying the addition of new model support. Additionally, hardened daily CI reporting by fixing GitHub API diagnostic output being captured in Slack payloads and adding rate-limit resilience to prevent report failures when paginating large job matrices.

• CI: Log GitHub API diagnostics to stderr (#47635) by @tarekziade in [#47635]
• Update serve chat parsing (#46267) by @SunMarc in [#46267]
• ci: harden daily CI reporting against GitHub API rate limits (#47382) by @tarekziade in [#47382]

Bugfixes and improvements

• Fix cached_files silently returning stale file on read-only filesystem (EROFS) (#47852) by @ydshieh in [#47852]
• Fix PhimoeIntegrationTest (#46539) by @ydshieh in [#46539]
• make examples under doc device agnostic (#47812) by @kaixuanliu in [#47812]
• cancel deterministic for XPU in gemma4 tests (#47790) by @kaixuanliu in [#47790]
• Serialize post-mlinter-review after post-link to avoid PR description race (#47832) by @ydshieh in [#47832]
• Use content hash for mlinter review deduplication (#47830) by @ydshieh in [#47830]
• Add new args in auto-docstring (#47737) by @zucchini-nlp in [#47737]
• Add check_model_inits.py (#47656) by @guarin in [#47656]