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chore(deps): bump the conversion-model-toolchain group with 7 updates - #54

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Bumps the conversion-model-toolchain group with 7 updates:

Package From To
optimum-intel 2.0.0 2.2.0
nncf 3.2.0 3.4.0
optimum 2.2.0 2.3.0
transformers 5.0.0 5.18.0
tokenizers 0.22.2 0.23.2
torch 2.12.0 2.14.1
safetensors 0.7.0 0.8.0

Updates optimum-intel from 2.0.0 to 2.2.0

Release notes

Sourced from optimum-intel's releases.

v2.2.0

New Model Support

Improvements & fixes

Other Changes

  • Added an OpenVINO export space to the documentation (#1882 by @​echarlaix)
  • Added agent skills for optimum and tiny test model creation (#1848 by @​Mohamed-Ashraf273)
  • Removed unneeded transformers version checks in tests (#1933 by @​popovaan)
  • CI: run OpenVINO tests on release-branch pull requests (#1892), restore Qwen3.5 and Qwen3.5-MoE in the preview workflow (#1891), sync built docs to the hf-doc-build bucket (#1898 by @​echarlaix)

New Contributors

Compatible transformers version

Compatible with transformers>=4.51,<5.6

... (truncated)

Commits

Updates nncf from 3.2.0 to 3.4.0

Release notes

Sourced from nncf's releases.

v3.4.0

v3.3.0

Changelog

Sourced from nncf's changelog.

New in Release 3.4.0

New in Release 3.3.0

Commits

Updates optimum from 2.2.0 to 2.3.0

Release notes

Sourced from optimum's releases.

v2.3.0

What's Changed

New Contributors

Full Changelog: huggingface/optimum@v2.2.0...v2.3.0

Commits

Updates transformers from 5.0.0 to 5.18.0

Release notes

Sourced from transformers's releases.

Release 5.18.0

New Model additions

Nemotron 3 Diarization

Nemotron 3 Diarization is an open-weight streaming speaker diarization model designed to determine "who spoke when" in real-world audio. It supports both streaming and offline inference, handles up to eight speakers, and orders speaker outputs by each speaker's first arrival in the input audio.

The model uses the Arrival-Order Speaker Cache (AOSC) 1 and FIFO queue introduced for Streaming Sortformer 1, 2. A single checkpoint supports configurable latency profiles, from an 80 ms input buffer to a 30.4 s offline-style buffer, and configurable output frame resolution in multiples of 10 ms. With chunked inference, the maximum audio duration is not limited.

Links: Documentation

NemotronH Omni

NemotronH Omni is a multimodal reasoning model from NVIDIA that pairs the NemotronH hybrid Mamba-Transformer language model with a RADIO vision encoder and an optional Parakeet-based sound encoder. Image (and video) patches are projected through a RADIO tower and a pixel-shuffle MLP into the language model's embedding space at the <image> / <video> context-token positions; audio clips are projected in the same way at <audio> positions. The result is a single autoregressive model that reasons jointly over text, images, video and sound.

Links: Documentation

HyperCLOVAX Vision V2

HyperCLOVAX Vision V2 is a multimodal vision-language model developed by NAVER. It combines the HyperClovaX language model backbone with a Qwen2.5-VL vision encoder. The model supports text, image, and video inputs and is capable of chain-of-thought reasoning via built-in thinking tokens (<think>...</think>).

Links: Documentation

GTE

GTE was proposed in mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval by Xin Zhang, Yanzhao Zhang, Dingkun Long, Wen Xie, Ziqi Dai, Jialong Tang, Huan Lin, Baosong Yang, Pengjun Xie, Fei Huang, Meishan Zhang, Wenjie Li and Min Zhang.

GTE is a BERT-style bidirectional encoder that replaces absolute position embeddings with RoPE, uses a gated MLP, and applies layer normalization after each residual connection. The same architecture backs Alibaba's gte-*-v1.5, gte-multilingual-* and gte-en-mlm-* checkpoints as well as Snowflake's snowflake-arctic-embed-m-v2.0.

Links: Documentation

Breaking changes

... (truncated)

Commits
  • a906d3c v5.18.0
  • 57296d1 model: Add GTE to Transformers (#48416)
  • 88536f2 [Nemotron3Diarization] fix streaming last stft frame dropped (#49167)
  • 5cd2877 Fix missing router_logits in Qwen3.5-MoE and other MoE models (#49179)
  • 4fcb1ff Fix MiniMax M3 partial 3D vision rotary embeddings (#49164)
  • 8520b15 Add Strix Halo (gfx1151) Atlas Inference Hub-kernel path for Qwen3.5/3.6/3.8 ...
  • a877116 Fix MPS GQA version gating (#49210)
  • e0299e2 Fix additional_special_tokens data loss with extra_special_tokens (#47848)
  • 5aab642 Fix stale _added_tokens_encoder entries in cpmant and wav2vec2 (#47440)
  • 0c18a63 Fix deepstack features for mixed-input (#49177)
  • Additional commits viewable in compare view

Updates tokenizers from 0.22.2 to 0.23.2

Release notes

Sourced from tokenizers's releases.

v0.23.2

This is the last v0 release, we are moving to v1!!

More details coming soon 👀

What's Changed

New Contributors

Full Changelog: huggingface/tokenizers@v0.23.1...v0.23.2

Release v0.23.1

TL;DR

tokenizers 0.23.1 is the first proper stable release in the 0.23 line — 0.23.0 only ever shipped as rc0 because the release pipeline itself was broken (Node side hadn't shipped multi-platform binaries since 2023, Python side was on pyo3 0.27 without free-threaded support). 0.23.1 is the version where everything actually goes out the door together: full Node multi-platform wheels for the first time in years, Python 3.14 (regular and free-threaded 3.14t), full type hints for every Python class, and a stack of measurable perf wins on the BPE / added-vocab hot paths.

There is no functional 0.23.0 published — we tag 0.23.1 directly so users don't accidentally pull a never-shipped version.

🚨 Breaking changes

  • Drop Python 3.9 (#1952) — requires-python = ">=3.10"; 3.9 users stay on 0.22.x.
  • add_tokens normalizes content at insertion (#1995) — re-saved tokenizer.json may differ in the added_tokens block. Existing files load unchanged.
  • Type stubs are precise (#1928, #1997) — methods that returned Any now return real types; mypy --strict may surface previously-hidden errors. Stub layout also moved from tokenizers/<sub>/__init__.pyi to tokenizers/<sub>.pyi. This breaks the surface of some of the processors like RobertaProcessign's __init__ .
  • 3.14t-only: setters/getters return PyResult<T> because of Arc<RwLock<Tokenizer>>; a poisoned lock surfaces as PyException instead of a panic.

... (truncated)

Commits
  • 88a4498 add lock
  • e4ea65f real release
  • 7c5964b add lock
  • bc405ad push rc0
  • d582781 Add a ParityBpeTrainer example and list it in the trainers API docs (#2217)
  • 447890f fix(python): pin the ruff rule set so a ruff release can't redden main (#2292)
  • 68b3ab7 chore(node): take node security alerts from 33 to 0 (#2287)
  • 74ef81f ci: pin every action to one SHA, drop stale audit suppressions (#2291)
  • b26727f chore(node): drop 8 unused devDependencies (#2286)
  • 7c7cfd8 chore: remove stale examples (kills 50% of dependabot traffic) (#2285)
  • Additional commits viewable in compare view

Updates torch from 2.12.0 to 2.14.1

Release notes

Sourced from torch's releases.

PyTorch 2.14.1 Release

This release is meant to fix the following regressions and silent correctness issues:

Silent correctness fixes

  • Fix incorrect torch.linalg.lstsq solutions on MPS for complex batched underdetermined systems (#196113), fixed by #196128
  • Fix non-orthogonal U and inaccurate small singular values from torch.linalg.svd on MPS for rank-deficient and ill-conditioned inputs (#196112), fixed by #196139 and #199063
  • Update the CUDA 13.2 Linux binaries to CUDA 13.2.2 (#196351). This NVIDIA update resolves two critical issues that could produce incorrect results (CUDA 13.2.2 release notes):
    • cuBLAS: cublasLtMatmul() could ignore tensor-wide scaling for NVFP4 matrix multiplications (introduced in CUDA 13.2 Update 1)
    • Compiler: failed thread reconvergence could leave stale or corrupted register values in kernels with nested thread divergence (present since CUDA 12.8)

Regression fixes

  • Fix torch.linalg.svd, torch.linalg.svdvals and torch.linalg.lstsq failing on MPS with a Metal pipeline-state error for inputs above 8192 elements (#195937), fixed by #195949 and #195950
  • Fix internal assert in torch.svd(out=) on MPS for complex inputs (#195822), fixed by #195872

PyTorch 2.14.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

torch.nn

  • torch.nn.LinearCrossEntropyOptions no longer accepts acc_policy="balanced"; use "compact" instead (#188283)

    The "balanced" policy was removed because "compact" provides the same weight-gradient accumulation precision with lower memory use on CUDA, already uses the equivalent scratch layout for mixed-precision inputs on other devices, and was never selected by "auto". Constructing the options with acc_policy="balanced" now raises ValueError: invalid acc_policy: 'balanced'; expected one of 'auto', 'accurate', 'compact'.

... (truncated)

Commits
  • 5c48869 [MPS] Fix Jacobi SVD convergence for small columns (#199086)
  • 35223a2 [release/2.14] Run the release runner-group reconcile in pytorch/pytorch (#19...
  • 41ffbc4 [release-only] Update version to 2.14.1 (#198661)
  • e8f0c01 [release/2.14] [CD] Update to CUDA 13.2.2 for Linux binaries (#198644)
  • 911665d [MPS] Cherry-pick native SVD fixes into release/2.14 (#198394)
  • 2b3ec34 [release/2.14] Import SDPAParams in test_transformers to fix lint (#194970)
  • 08187d9 [cuDNN] Add guards for cuDNN SDPA decode (#194963)
  • 8ceea97 Pin cython < 3.3.0 for the Windows Triton wheel build (#194931)
  • 99ecebc [Cherry-pick][release/2.14] [inductor] Fix loop-local load CSE lifetime (#194...
  • ec283a7 Bump the Python 3.15 numpy pin to 2.5.2 (#194821)
  • Additional commits viewable in compare view

Updates safetensors from 0.7.0 to 0.8.0

Release notes

Sourced from safetensors's releases.

v0.8.0

News

safetensors joins the PyTorch foundation!

Read more on that: https://huggingface.co/blog/safetensors-joins-pytorch-foundation

What's changed

Safetensors 0.8.0 brings direct to Metal loading on Apple Silicon, GIL-free serialization, broader hardware and dtype coverage, and a stronger Python API.

Breaking

The serialize and serialize_file functions now release the GIL during writes, enabling true multithreaded saves from Python. Their input contract has also changed: tensor metadata is now passed via a TensorSpec class (exported from safetensors) instead of plain dicts, making API more explicit and robust to misinputs. This is a breaking change for anyone calling the low-level serialize / serialize_file API directly; the high-level wrappers (safetensors.torch, safetensors.numpy, safetensors.paddle) are updated internally and their public API is unchanged.

The minimum supported Python version is now 3.10 (was 3.9). Python 3.9 reached end-of-life in October 2025.

TensorIndexer::Narrow now carries a step: NonZeroUsize parameter, so a slice is now start:stop:step. This is a fix as this silent error was hidden behind the Storage::Torch variant which offloaded slicing logic to torch directly.

CI

On the platform side, this release adds Windows ARM64 wheel builds, riscv64 Linux wheels, and CI has been hardened with pinned GitHub Actions SHAs.

Also dropped the anaconda CI we had as there's already an automatic tracker via conda-forge.

New features

  • Direct MPS load on Apple Silicon: tensors are directly loaded in an MTLBuffer and handed to the frameworks that support it (only torch atm) via DLPack, skipping needless copies.
  • New backend parameter introduced, for the addition of the pread backend. We now support loading files via pread(2) syscall instead of just mmap. Useful for specific archs/platforms.
  • get_slice now handles ellipsis [...] and strided slices [:, ::8] wherever safetensors does the slicing itself (pread for any framework, MPS, and mmap outside torch/paddle), which silently dropped the step or rejected ... before.
  • MUSA device support for MooreThreads GPUs.
  • New dtype support includes float8_e4m3fnuz and float8_e5m2fnuz (AMD FNUZ FP8 formats).
  • The reader is now explicitly lenient about leading whitespace in the JSON header, which keeps the door open for future page-aligned writes.

Improvements/perf

  • File writes on macOS now use F_NOCACHE for direct I/O, yielding roughly 30% faster save_file on Apple Silicon.
  • The packaging dependency has been dropped from the [torch] extra, replaced by a simple hasattr probe for efficiency.

What's Changed

... (truncated)

Changelog

Sourced from safetensors's changelog.

0.8.0-dev.0 → 0.8.0


Update lockfiles again:

cargo check
cd bindings/python &amp;&amp; uv lock
</code></pre>
<p>Commit:</p>
<pre><code>Set version to 0.8.0
</code></pre>
<p>The Python version is not set in <code>pyproject.toml</code> directly — maturin reads it from <code>bindings/python/Cargo.toml</code> at build time.</p>
<h3>4. Create the release on GitHub</h3>
<p>Go to the <a href="https://github.com/huggingface/safetensors/releases">GitHub Releases page</a> and draft a new release:</p>
<ul>
<li><strong>Choose the release branch</strong> (e.g. <code>git_v0.8.0</code>)</li>
<li><strong>Create a new tag</strong> on publish: <code>v0.8.0</code></li>
<li><strong>Generate release notes</strong> from the previous tag (e.g. <code>v0.7.0</code>)</li>
<li>Add any notable highlights at the top of the generated notes</li>
</ul>
<p>Structure for manual notes:</p>
<pre lang="markdown"><code>## Breaking changes
- ...

New features


    

  • ...
    • 



Bug fixes


    

  • ...
    • 



Internal / CI


    

  • ...
    
</code></pre>
    
<p>Click <strong>Publish release</strong>. This creates the tag, which triggers the three release workflows.</p>
    
<h3>5. Monitor the CI</h3>
    
<p>The tag triggers two workflow runs in the <a href="https://github.com/huggingface/safetensors/actions&quot;&gt;Actions tab</a>:</p>
    
<ul>
    
<li><strong>CI</strong> (Python release) — builds wheels for every platform × Python version, uploads to PyPI. Can take up to over 30 minutes.</li>
    
</ul>
    
<!-- raw HTML omitted -->
    
</blockquote>
    
<p>... (truncated)</p>
    
</details>
    
<details>
    
<summary>Commits</summary>
    • 



<ul>

<li><a href="https://github.com/safetensors/safetensors/commit/a406ca3e7a90598be0cd05a50069cb9bf5ef6ba6&quot;&gt;&lt;code&gt;a406ca3&lt;/code&gt;&lt;/a> fix(release): move crates.io to trusted publisher OIDC</li>

<li><a href="https://github.com/safetensors/safetensor...


Description has been truncated

@dependabot
chore(deps): bump the conversion-model-toolchain group with 7 updates
b192d9b 
Bumps the conversion-model-toolchain group with 7 updates:

| Package | From | To |
| --- | --- | --- |
| [optimum-intel](https://github.com/huggingface/optimum-intel) | `2.0.0` | `2.2.0` |
| [nncf](https://github.com/openvinotoolkit/nncf) | `3.2.0` | `3.4.0` |
| [optimum](https://github.com/huggingface/optimum) | `2.2.0` | `2.3.0` |
| [transformers](https://github.com/huggingface/transformers) | `5.0.0` | `5.18.0` |
| [tokenizers](https://github.com/huggingface/tokenizers) | `0.22.2` | `0.23.2` |
| [torch](https://github.com/pytorch/pytorch) | `2.12.0` | `2.14.1` |
| [safetensors](https://github.com/huggingface/safetensors) | `0.7.0` | `0.8.0` |


Updates `optimum-intel` from 2.0.0 to 2.2.0
- [Release notes](https://github.com/huggingface/optimum-intel/releases)
- [Commits](huggingface/optimum-intel@v2.0.0...v2.2.0)

Updates `nncf` from 3.2.0 to 3.4.0
- [Release notes](https://github.com/openvinotoolkit/nncf/releases)
- [Changelog](https://github.com/openvinotoolkit/nncf/blob/develop/ReleaseNotes.md)
- [Commits](openvinotoolkit/nncf@v3.2.0...v3.4.0)

Updates `optimum` from 2.2.0 to 2.3.0
- [Release notes](https://github.com/huggingface/optimum/releases)
- [Commits](huggingface/optimum@v2.2.0...v2.3.0)

Updates `transformers` from 5.0.0 to 5.18.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v5.0.0...v5.18.0)

Updates `tokenizers` from 0.22.2 to 0.23.2
- [Release notes](https://github.com/huggingface/tokenizers/releases)
- [Commits](huggingface/tokenizers@v0.22.2...v0.23.2)

Updates `torch` from 2.12.0 to 2.14.1
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.12.0...v2.14.1)

Updates `safetensors` from 0.7.0 to 0.8.0
- [Release notes](https://github.com/huggingface/safetensors/releases)
- [Changelog](https://github.com/safetensors/safetensors/blob/main/RELEASE.md)
- [Commits](safetensors/safetensors@v0.7.0...v0.8.0)

---
updated-dependencies:
- dependency-name: optimum-intel
  dependency-version: 2.2.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: conversion-model-toolchain
- dependency-name: nncf
  dependency-version: 3.4.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: conversion-model-toolchain
- dependency-name: optimum
  dependency-version: 2.3.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: conversion-model-toolchain
- dependency-name: transformers
  dependency-version: 5.18.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: conversion-model-toolchain
- dependency-name: tokenizers
  dependency-version: 0.23.2
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: conversion-model-toolchain
- dependency-name: torch
  dependency-version: 2.14.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: conversion-model-toolchain
- dependency-name: safetensors
  dependency-version: 0.8.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: conversion-model-toolchain
...

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