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chore(deps): update loader dependencies non-major - #300
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| datasource | package | from | to | | ---------- | ------------ | ------ | ------ | | pypi | pydantic | 2.13.5 | 2.14.0 | | pypi | transformers | 5.18.0 | 5.19.0 |
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This PR contains the following updates:
==2.13.5→==2.14.0==5.18.0→==5.19.0Release Notes
pydantic/pydantic (pydantic)
v2.14.0Compare Source
GitHub release
What's Changed
The highlights of the v2.14 release are available in the blog post.
Several minor changes (considered non-breaking changes according to our versioning policy)
are also included in this release. Make sure to look into them before upgrading.
This release drops support for Python 3.9 and adds support for Python 3.15.
New Features
multiple_ofconstraint infractionandtimedeltacore schemas by @Viicos in #13873__namespace__parameter tocreate_model()by @Viicos in #13895Changes
ordered-dictcore schema by @Viicos in #13796countercore schema by @Viicos in #13824model_configattribute by @Viicos in #13825NameEmailvalidation by @Viicos in #13897Fixes
string_sub_typeunused error code by @Viicos in #13821OverflowErrorduringByteSizevalidation by @Viicos in #13850multiple_ofconstraints to be positive by @Viicos in #13862TypeErrorgracefully during docstring extraction by @Viicos in #13874TypedDicts in JSON Schema by @Viicos in #13891ser_json_timedeltato all datetime types in serialization inference by @Viicos in #13892initandkw_only=FalsefromField()to stdlib dataclass fields by @Viicos in #13898ZoneInfovalidation by @Viicos in #13912Decimalvalidation by @Viicos in #13940Fractionvalidation by @Viicos in #13944New Contributors
huggingface/transformers (transformers)
v5.19.0Compare Source
Release v5.19.0
New Model additions
EmbeddingGemma2
EmbeddingGemma 2 is a multimodal embedding model from Google built on the Gemma 4 architecture. It encodes text, images, audio, and video, individually or combined in one input, into a shared 768-dimensional vector space for cross-modal retrieval, semantic similarity, clustering, and classification. It uses Matryoshka Representation Learning, so embeddings can be truncated to 512, 256, or 128 dimensions. It also offers configurable visual and video token budgets, and unused vision or audio towers can be disabled at load time to save memory.
Links: Documentation
Breaking changes
All MoE models whose routers compute logits now return them when
output_router_logits=True, following the Qwen3-MoE pattern (arouter_logitsrecorder on the base model,MoeModelOutputWithPastfrom the backbone, and a MoE causal LM output from the head), so code that relied on the previous outputs or their absence should read the router logits from these output classes.Owlv2ForObjectDetection.embed_image_querynow selects the query box with the highest objectness score, as in the original OWLv2 notebook, instead of the OWL-ViT heuristic, so image-guided query embeddings and detections may differ from earlier releases.The
"paged|"prefix for SDPA and flash attention implementations is deprecated, so users should set the regular attention implementation (e.g.sdpaorflash_attention_2) for continuous batching instead ofpaged|sdpaorpaged|flash_attention_2.The regular flash and SDPA attention functions (
flash_attention.py,sdpa_attention.py) now support continuous batching directly, and"paged|..."implementations for these are redirected to them, while eager still requires the"paged|eager"prefix.In continuous batching, the cache update for the index-based and block-table paths is now fused into a single call, which slightly changes the cache update function's behavior and affects any custom code that calls the separate update paths.
Continuous batching internals changed in preparation for removing
"paged": `maxParallelization
Expert parallelism gains a token-dispatch implementation, selected via the new
ep_dispatch_expertsplan rule and now the default for Qwen3 MoE and Mellum, which removes the requirement that EP size equal TP size. TheTrainerwas also adapted to work with expert parallelism, and the docs now note that PEFT adapters support tensor parallelism. A CI-related fix for pipeline-parallel chart2table inference was also included.distributed]: adapt Trainer to work Expert Parallel (#48873) by @3outeille in [#48873]distributed] Add expert-parallel token dispatch, default for Qwen3 MoE (#48865) by @3outeille in [#48865]Cache
This release fixes quantized cache handling:
generateno longer mutates the user'scache_config, andQuantizedLayer.reorder_cacheis repaired. It also adds per-layer cache configuration, soDynamicCacheandStaticCacheinitialize each layer from its own config (sliding window, attention chunk size, conv states, and attention head counts) to better support heterogeneous models. Separately, the deprecation cycle on mask and cacheBugfixes and improvements
distributed] Default MoEep_plansto token dispatch (#49160) by @3outeille in [#49160].python-version(#49311) by @tarekziade in [#49311]distributed] decoupled tp_plan and ep_plan (#48859) by @3outeille in [#48859]distributed] Add dense and expert device mesh (#48857) by @3outeille in [#48857]labelsdocstring of CLIPSegForImageSegmentation (#49001) by @qgallouedec in [#49001]Mixin(#49105) by @zucchini-nlp in [#49105]torch_tpubackend (#49207) by @tengomucho in [#49207]GTE] Add memory mixin (#49234) by @vasqu in [#49234]Significant community contributions
The following contributors have made significant changes to the library over the last release:
GTE] Add memory mixin (#49234)Configuration
📅 Schedule: (UTC)
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