Hugging Face Transformers: Release v5.7.0

# Release v5.7.0 ## New Model additions ### Laguna <img width="699" height="176" alt="image" src="https://github.com/user-attachments/assets/d3bae269-bea7-4ddf-a53f-d4718befdb17" /> Laguna is Poolside's mixture-of-experts language model family that extends standard SwiGLU MoE transformers with two key innovations. It features per-layer head counts allowing different decoder layers to have different query-head counts while sharing the same KV cache shape, and implements a sigmoid MoE router with auxiliary-loss-free load balancing that uses element-wise sigmoid of gate logits plus learned per-expert bias for router scoring. **Links:** [Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/laguna) * Laguna XS.2 implementation (#45673) by @joerowell in [#45673](https://github.com/huggingface/transformers/pull/45673) ### DEIMv2 <img width="2874" height="908" alt="image" src="https://github.com/user-attachments/assets/fc8c59fe-f964-42ce-ae8e-c7fcace9beb7" /> DEIMv2 (DETR with Improved Matching v2) is a real-time object detection model that extends DEIM with DINOv3 features and spans eight model sizes from X to Atto for diverse deployment scenarios. It uses a Spatial Tuning Adapter (STA) for larger variants to convert DINOv3's single-scale output into multi-scale features, while ultra-lightweight models employ pruned HGNetv2 backbones. The unified design achieves superior performance-cost trade-offs, with DEIMv2-X reaching 57.8 AP with only 50.3M parameters and DEIMv2-S being the first sub-10M model to exceed 50 AP on COCO. **Links:** [Documentation](https://huggingface.co/docs/transformers/main/en/model_doc/deimv2) | [Paper](https://huggingface.co/papers/2509.20787) * model: Add DEIMv2 to Transformers (#44339) by @harshaljanjani in [#44339](https://github.com/huggingface/transformers/pull/44339) ## Attention Several attention-related bugs were fixed across multiple models, including a cross-attention cache type error in T5Gemma2 for long inputs, incorrect cached forward behavior in Qwen3.5's gated-delta-net linear attention, and a crash in GraniteMoeHybrid when no Mamba layers are present. Attention function dispatch was also updated to align with the latest model implementations. * Fix cross-attention cache layer type for T5Gemma2 long inputs (#45540) by @Beichen-Ma in [#45540] * [Qwen3.5] Fix GDN linear attention multi-token cached forward (#45513) by @kashif in [#45513] * Fix GraniteMoeHybrid _update_mamba_mask crash on attention-only models (#45514) by @tianhaocui in [#45514] * Align latest model attention function dispatch (#45598) by @Cyrilvallez in [#45598] ## Tokenizers There was a bug in AutoTokenizer that caused the wrong tokenizer class to be initialized. This caused regressions in models like DeepSeek R1. * change got reverted (#45680) by @itazap in [#45680] ## Generation Continuous batching generation received several fixes and improvements, including correcting KV deduplication and memory estimation for long sequences (16K+), and removing misleading warnings about `num_return_sequences` and other unsupported features that were incorrectly firing even when functionality worked correctly. Documentation for per-request sampling parameters was also added. * generate: drop stale num_return_sequences warning on continuous batching path (#45582) by @joaquinhuigomez in [#45582] * Remove unnecessary generate warnings (#45619) by @Cyrilvallez in [#45619] * [CB] Changes for long generation (#45530) by @remi-or in [#45530] * [docs] per-request sampling params (#45553) by @stevhliu in [#45553] ## Kernels Improved kernel support by fixing configuration reading and error handling for FP8 checkpoints (e.g., Qwen3.5-35B-A3B-FP8), enabling custom expert kernels registered from the HF Hub to be properly loaded, and resolving an incompatibility that prevented Gemma3n and Gemma4 from using the rotary kernel. * Fix configuration reading and error handling for kernels (#45610) by @hmellor in [#45610] * Allow for registered experts from kernels hub (#45577) by @winglian in [#45577] * Gemma3n and Gemma4 cannot use rotary kernel (#45564) by @Cyrilvallez in [#45564] ## Bugfixes and improvements * fixing more typos (#45689) by @vasqu in [#45689] * [docs] cb memory management (#45587) by @stevhliu in [#45587] * [docs] cpu offloading (#45660) by @stevhliu in [#45660] * docs(README_zh-hans): clarify conditions for not using Transformers (#45688) by @GuaiZai233 in [#45688] * fix padding side issue for fast_vlm tests (#45592) by @kaixuanliu in [#45592] * Fix `x_clip`: 8 failed test cases (#45394) by @kaixuanliu in [#45394] * zero_shot_object_detection ValueError fix for python 3.13 (#45669) by @AnkitAhlawat7742 in [#45669] * Fix pageable H2D copies in Gated DeltaNet PyTorch fallback (#45665) by @ruixiang63 in [#45665] * Fix UnboundLocalError in shard_and_distribute_module for replicated parameters (#45675) by @Abdennacer-Badaoui in [#45675] * [MistralCommonBackend] Soften validation mode and apply_chat_template arguments check (#45628) by @juliendenize in [#45628] * Fix `NameError: PeftConfigLike` triggered by `PreTrainedModel.__init_subclass__` (#45658) by @qgallouedec in [#45658] * chore(typing): added modeling_utils to ty (#45425) by @tarekziade in [#45425] * [gemma4] infer from config instead of hardcoding (#45606) by @eustlb in [#45606] * Update quants tests (#45480) by @SunMarc in [#45480] * 🔴🔴🔴 fix: skip `clean_up_tokenization` for BPE tokenizers in `PreTrainedTokenizerFast` (#44915) by @maxsloef-goodfire in [#44915] * Fix colmodernvbert tests (#45652) by @Cyrilvallez in [#45652] * [CB] [Major] Add CPU request offloading (#45184) by @remi-or in [#45184] * Fix peft constructors (#45622) by @Cyrilvallez in [#45622] * chore: speedup modular converter (~30%) (#45046) by @tarekziade in [#45046] * Fix whisper return language (#42227) by @FredHaa in [#42227] * Add `supports_gradient_checkpointing` to `NemotronHPreTrainedModel` (#45625) by @sergiopaniego in [#45625] * Raise clear error for `problem_type="single_label_classification"` with `num_labels=1` (#45611) by @gaurav0107 in [#45611] * CircleCI with torch 2.11 (#45633) by @ydshieh in [#45633] * chore: bump doc-builder SHA for main doc build workflow (#45631) by @rtrompier in [#45631] * Allow more artifacts to be download in CI (#45629) by @ydshieh in [#45629] * chore(qa): split pipeline and add type checking (#45432) by @tarekziade in [#45432] * Skip failing offloading tests (#45624) by @Cyrilvallez in [#45624] * fix: compute auxiliary losses when denoising is disabled in D-FINE (#45601) by @Abineshabee in [#45601] * qa: bumped mlinter and allow local override (#45585) by @tarekziade in [#45585] * Processing Utils: continue when content is a string (#45605) by @RyanMullins in [#45605] * SonicMoe (#45433) by @IlyasMoutawwakil in [#45433] * fix transformers + torchao nvfp4 serialization (#45573) by @vkuzo in [#45573] * [AMD CI] Fix expectations for Gemma3n (#45602) by @Abdennacer-Badaoui in [#45602] * [docs] multi-turn tool calling (#45554) by @stevhliu in [#45554] * Fix `AttributeError` on `s_aux=None` in `flash_attention_forward` (#45589) by @jamesbraza in [#45589] * do not index past decoded chars with special tokens (#45435) by @itazap in [#45435] * Update dev version (#45583) by @vasqu in [#45583] * Update torchao usage for XPU and CPU (#45560) by @jiqing-feng in [#45560] ## Significant community contributions The following contributors have made significant changes to the library over the last release: * @vasqu * fixing more typos (#45689) * Update dev version (#45583) * @joerowell * Laguna XS.2 implementation (#45673) * @tarekziade * chore(typing): added modeling_utils to ty (#45425) * chore: speedup modular converter (~30%) (#45046) * chore(qa): split pipeline and add type checking (#45432) * qa: bumped mlinter and allow local override (#45585) * @harshaljanjani * model: Add DEIMv2 to Transformers (#44339) * @remi-or * [CB] [Major] Add CPU request offloading (#45184) * [CB] Changes for long generation (#45530)