Update dependency transformers to >=5.14.1,<5.15.0#43
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This PR contains the following updates:
>=5.13.0,<5.14.0→>=5.14.1,<5.15.0Release Notes
huggingface/transformers (transformers)
v5.14.1: Patch release: v5.14.1Compare Source
Patch release v5.14.1
This patch solves a few issues which appeared when integrating Inkling model, most notably an issue affecting models using EncoderDecoderCache during assisted generation. It also fixes an issue that could appear during prefill with StaticCache and sdpa without padding for Inkling which uses a position_bias.
It contains the following commits:
v5.14.0Compare Source
Release v5.14.0
New Model additions
Inkling (fresh from Thinking Machines): 975B total, 41B active
Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and
generates text outputs. It is intended for use in English and other languages, and across
multiple coding languages. The model is designed to be used by developers building AI-
powered applications, including agentic and tool-use systems, coding assistants, chatbots, and
retrieval-augmented generation systems, and is suitable for general-purpose conversational
use, instruction-following, and other natural language and multimodal tasks. It is released with
open weights to support research, fine-tuning and integration into third-party products by
downstream developers.
TIPSv2
Links: Documentation
TIPSv2 DPT
Links: Documentation
🚨 Breaking changes
GPTNeoX now remaps
embed_outtolm_headand GPTBigCode has_supports_attention_backend = Trueenabled for vLLM compatibility; users relying on the previous weight naming or attention backend behavior for these models should update their code accordingly.Kernels
Several kernel-related fixes and improvements were made, including pinning the
kernelsdependency to a compatible version in the benchmark workflow, removing a deprecatedpackage_nameargument fromLocalLayerRepository, and making the DeepGEMM Triton fallback more robust whenCUDA_HOMEis unset or misconfigured. Additionally, SDPA prefill was updated to leverage the FlashAttention kernel withStaticCache, yielding significant performance gains (up to 260% faster for large input sizes).kernelscall (#47100) by @remi-or in [#47100]Generation
Generation improvements include adding Multi-Token Prediction (MTP) decoding support, static ensemble verification for speculative decoding to improve draft token acceptance rates, and a fix for crashes in greedy assisted generation with different tokenizers. A misleading double-negative warning message for
synced_gpusin continuous batching mode was also corrected.Performance
Fixed a Flash Attention performance regression affecting models like Qwen3-VL and resolved a MoE decode optimization bug where the grouped-to-batched matrix multiplication switch was not applied to experts residing in submodels (e.g., VLMs with a nested text config).
Cache
Cache dispatch logic was simplified by introducing explicit layer-type mappings for sliding and static layers, reducing complexity in cache routing. Additionally, fixes were made for read-only cache failures in CPU CI environments and for MPS graph cache growth during variable-length batch training on Apple Silicon.
Bugfixes and improvements
MODEL_IDS_TO_TOKENIZERS_BACKENDcapture all DeepSeek R1 distills (#47296) by @hmellor in [#47296]remap_legacy_layer_typesfor custom models (#47245) by @hmellor in [#47245]transformers_weightsconfig field (#46890) by @LinZiyuu in [#46890]_LazyAutoMapping.registeris passed astrkey (#47148) by @hmellor in [#47148]dacfailing withoutput_mismatch(tensor values differ (6), other (6)) (#47121) by @sergereview[bot] in [#47121]use_cache=Falsefor DeepSeek V4 (#46965) by @kylesayrs in [#46965]Gemma4] Update 1 integration test (#47042) by @vasqu in [#47042]Significant community contributions
The following contributors have made significant changes to the library over the last release:
kernelscall (#47100)Configuration
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