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Release v5.14.0

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@ArthurZucker ArthurZucker released this 15 Jul 19:02

Release v5.14.0

New Model additions

Inkling (fresh from Thinking Machines): 975B total, 41B active

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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

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Links: Documentation

TIPSv2 DPT

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Links: Documentation

🚨 Breaking changes

GPTNeoX now remaps embed_out to lm_head and GPTBigCode has _supports_attention_backend = True enabled for vLLM compatibility; users relying on the previous weight naming or attention backend behavior for these models should update their code accordingly.

  • 🚨 Fix GPTBigCode and GPTNeoX for the Transformers modelling backend for vLLM (#47198) by @hmellor

Kernels

Several kernel-related fixes and improvements were made, including pinning the kernels dependency to a compatible version in the benchmark workflow, removing a deprecated package_name argument from LocalLayerRepository, and making the DeepGEMM Triton fallback more robust when CUDA_HOME is unset or misconfigured. Additionally, SDPA prefill was updated to leverage the FlashAttention kernel with StaticCache, yielding significant performance gains (up to 260% faster for large input sizes).

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_gpus in 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

Significant community contributions

The following contributors have made significant changes to the library over the last release:

  • @ArthurZucker
    • v5.14.0
  • @tarekziade
    • ci: cover xet as well (runtime error) (#47338)
    • Pin kernels to compatible version in benchmark workflow (#47339)
    • Switch mlinter to 0.1.2 (#47172)
    • Make executorch exporter tests always use xnnpack backend (#47201)
    • Remove executorch from all-latest-gpu image + add torch smoke test (#47196)
    • we want to run the CI in the release branches (#47125)
  • @remi-or
    • [Nit] Add kernels_fallback_ok kwarg to is_flash_attn_N_available (#47318)
    • [Nit] Add expectations for gemma4 tests on H100 (#47311)
    • [Fix] Remove deprecated argument from kernels call (#47100)
    • [Fix] Make DeepGEMM triton fallback more robust (#47126)
    • Fix experts implementation in two spots (#47097)
    • [Fix] Remove old automatic cross attn pattern from output recorders (#47117)
  • @ydshieh
    • tests: reduce processor test memory usage by using tiny Hub checkpoints (#47213)
    • Fix flash-attn Docker build broken by setuptools 83 removing pkg_resources (#47251)
    • Fix InputTokensDetails missing cache_write_tokens for openai>=2.34.0 (#47248)
    • Revert "Trigger a scheduled run" (#47249)
    • Fix CI read-only cache failures by patching cached_files in conftest (#47043)
    • Trigger a scheduled run (#47209)
    • tests: reduce processor test memory usage and use tiny test assets (#47168)
    • processor tests: use tiny Hub repos to reduce CI memory (#47115)
  • @eladsegal
    • Add heterogeneous config support (per-layer configuration) (#45333)
  • @eustlb
  • @Ternura143