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Bumps the pip group with 1 update in the /examples/models/core/mixtral directory: transformers.
Bumps the pip group with 1 update in the /examples/models/core/multimodal directory: transformers.
Bumps the pip group with 1 update in the /examples/quantization directory: nemo-toolkit.
Bumps the pip group with 1 update in the /security_scanning/cpp/kernels/fmha_v2 directory: pytest.
Bumps the pip group with 1 update in the /security_scanning/examples/models/core/mixtral directory: transformers.

Updates transformers from 4.56.0 to 5.5.0

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

  • The total number of pixels must fit within a patch budget
  • Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens Patches (before pooling) Approx. Image Area
70 630 ~161K pixels
140 1,260 ~323K pixels
280 2,520 ~645K pixels
560 5,040 ~1.3M pixels
1,120 10,080 ~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

... (truncated)

Commits
  • c1c3424 update
  • 20bff68 update release workflow
  • 8956441 v5.5.0
  • 5135e5e casually dropping the most capable open weights on the planet (#45192)
  • a594e09 Internalise the NomicBERT model (#43067)
  • 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
  • 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
  • b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
  • 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
  • ade7a05 Generalize gemma vision mask to videos (#45185)
  • Additional commits viewable in compare view

Updates transformers from 4.56.0 to 5.5.0

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

  • The total number of pixels must fit within a patch budget
  • Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens Patches (before pooling) Approx. Image Area
70 630 ~161K pixels
140 1,260 ~323K pixels
280 2,520 ~645K pixels
560 5,040 ~1.3M pixels
1,120 10,080 ~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

... (truncated)

Commits
  • c1c3424 update
  • 20bff68 update release workflow
  • 8956441 v5.5.0
  • 5135e5e casually dropping the most capable open weights on the planet (#45192)
  • a594e09 Internalise the NomicBERT model (#43067)
  • 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
  • 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
  • b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
  • 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
  • ade7a05 Generalize gemma vision mask to videos (#45185)
  • Additional commits viewable in compare view

Updates nemo-toolkit from 2.0.0rc1 to 2.6.2

Release notes

Sourced from nemo-toolkit's releases.

NVIDIA Neural Modules 2.6.2

Highlights

  • This release addresses known security issues. For the latest NVIDIA Vulnerability Disclosure Information visit https://www.nvidia.com/en-us/security/, for acknowledgement please reach out to the NVIDIA PSIRT team at PSIRT@nvidia.com
  • Update tutorial on SDE and imports in Audio Notebook

Detailed Changelogs:

Uncategorized:

NVIDIA Neural Modules 2.6.1

Highlights

Detailed Changelogs:

ASR

TTS

NLP / NMT

... (truncated)

Changelog

Sourced from nemo-toolkit's changelog.

NVIDIA Neural Modules 2.6.2

Highlights

  • This release addresses known security issues. For the latest NVIDIA Vulnerability Disclosure Information visit https://www.nvidia.com/en-us/security/, for acknowledgement please reach out to the NVIDIA PSIRT team at PSIRT@nvidia.com
  • Update tutorial on SDE and imports in Audio Notebook

Detailed Changelogs:

Uncategorized:

NVIDIA Neural Modules 2.6.1

Highlights

Detailed Changelogs:

ASR

TTS

... (truncated)

Commits

Updates pytest from 7.2.2 to 9.0.3

Release notes

Sourced from pytest's releases.

9.0.3

pytest 9.0.3 (2026-04-07)

Bug fixes

  • #12444: Fixed pytest.approx which now correctly takes into account ~collections.abc.Mapping keys order to compare them.

  • #13634: Blocking a conftest.py file using the -p no: option is now explicitly disallowed.

    Previously this resulted in an internal assertion failure during plugin loading.

    Pytest now raises a clear UsageError explaining that conftest files are not plugins and cannot be disabled via -p.

  • #13734: Fixed crash when a test raises an exceptiongroup with __tracebackhide__ = True.

  • #14195: Fixed an issue where non-string messages passed to unittest.TestCase.subTest() were not printed.

  • #14343: Fixed use of insecure temporary directory (CVE-2025-71176).

Improved documentation

  • #13388: Clarified documentation for -p vs PYTEST_PLUGINS plugin loading and fixed an incorrect -p example.
  • #13731: Clarified that capture fixtures (e.g. capsys and capfd) take precedence over the -s / --capture=no command-line options in Accessing captured output from a test function <accessing-captured-output>.
  • #14088: Clarified that the default pytest_collection hook sets session.items before it calls pytest_collection_finish, not after.
  • #14255: TOML integer log levels must be quoted: Updating reference documentation.

Contributor-facing changes

  • #12689: The test reports are now published to Codecov from GitHub Actions. The test statistics is visible on the web interface.

    -- by aleguy02

9.0.2

pytest 9.0.2 (2025-12-06)

Bug fixes

  • #13896: The terminal progress feature added in pytest 9.0.0 has been disabled by default, except on Windows, due to compatibility issues with some terminal emulators.

    You may enable it again by passing -p terminalprogress. We may enable it by default again once compatibility improves in the future.

    Additionally, when the environment variable TERM is dumb, the escape codes are no longer emitted, even if the plugin is enabled.

  • #13904: Fixed the TOML type of the tmp_path_retention_count settings in the API reference from number to string.

  • #13946: The private config.inicfg attribute was changed in a breaking manner in pytest 9.0.0. Due to its usage in the ecosystem, it is now restored to working order using a compatibility shim. It will be deprecated in pytest 9.1 and removed in pytest 10.

... (truncated)

Commits

Updates transformers from 4.56.0 to 5.5.0

Release notes

Sourced from transformers's releases.

Release v5.5.0

New Model additions

Gemma4

Gemma 4 is a multimodal model with pretrained and instruction-tuned variants, available in 1B, 13B, and 27B parameters. The architecture is mostly the same as the previous Gemma versions. The key differences are a vision processor that can output images of fixed token budget and a spatial 2D RoPE to encode vision-specific information across height and width axis.

You can find all the original Gemma 4 checkpoints under the Gemma 4 release.

The key difference from previous Gemma releases is the new design to process images of different sizes using a fixed-budget number of tokens. Unlike many models that squash every image into a fixed square (like 224×224), Gemma 4 keeps the image's natural aspect ratio while making it the right size. There a a couple constraints to follow:

  • The total number of pixels must fit within a patch budget
  • Both height and width must be divisible by 48 (= patch size 16 × pooling kernel 3)

[!IMPORTANT] Gemma 4 does not apply the standard ImageNet mean/std normalization that many other vision models use. The model's own patch embedding layer handles the final scaling internally (shifting values to the [-1, 1] range).

The number of "soft tokens" (aka vision tokens) an image processor can produce is configurable. The supported options are outlined below and the default is 280 soft tokens per image.

Soft Tokens Patches (before pooling) Approx. Image Area
70 630 ~161K pixels
140 1,260 ~323K pixels
280 2,520 ~645K pixels
560 5,040 ~1.3M pixels
1,120 10,080 ~2.6M pixels

To encode positional information for each patch in the image, Gemma 4 uses a learned 2D position embedding table. The position table stores up to 10,240 positions per axis, which allows the model to handle very large images. Each position is a learned vector of the same dimensions as the patch embedding. The 2D RoPE which Gemma 4 uses independently rotate half the attention head dimensions for the x-axis and the other half for the y-axis. This allows the model to understand spatial relationships like "above," "below," "left of," and "right of."

NomicBERT

NomicBERT is a BERT-inspired encoder model that applies Rotary Position Embeddings (RoPE) to create reproducible long context text embeddings. It is the first fully reproducible, open-source text embedding model with 8192 context length that outperforms both OpenAI Ada-002 and OpenAI text-embedding-3-small on short-context MTEB and long context LoCo benchmarks. The model generates dense vector embeddings for various tasks including search, clustering, and classification using specific instruction prefixes.

Links: Documentation | Paper

MusicFlamingo

Music Flamingo is a fully open large audio–language model designed for robust understanding and reasoning over music. It builds upon the Audio Flamingo 3 architecture by including Rotary Time Embeddings (RoTE), which injects temporal position information to enable the model to handle audio sequences up to 20 minutes. The model features a unified audio encoder across speech, sound, and music with special sound boundary tokens for improved audio sequence modeling.

Links: Documentation | Paper

... (truncated)

Commits
  • c1c3424 update
  • 20bff68 update release workflow
  • 8956441 v5.5.0
  • 5135e5e casually dropping the most capable open weights on the planet (#45192)
  • a594e09 Internalise the NomicBERT model (#43067)
  • 4932e97 Fix resized LM head weights being overwritten by post_init (#45079)
  • 57e8413 [Qwen3.5 MoE] Add _tp_plan to ForConditionalGeneration (#45124)
  • b10552e Fix TypeError: 'NoneType' object is not iterable in GenerationMixin.generate ...
  • 423f2a3 fix(models): Fix dtype mismatch in SwitchTransformers and TimmWrapperModel (#...
  • ade7a05 Generalize gemma vision mask to videos (#45185)
  • Additional commits viewable in compare view

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@dependabot
Bump the pip group across 5 directories with 3 updates
d4a6528 
Bumps the pip group with 1 update in the /examples/models/core/mixtral directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /examples/models/core/multimodal directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /examples/quantization directory: [nemo-toolkit](https://github.com/nvidia/nemo).
Bumps the pip group with 1 update in the /security_scanning/cpp/kernels/fmha_v2 directory: [pytest](https://github.com/pytest-dev/pytest).
Bumps the pip group with 1 update in the /security_scanning/examples/models/core/mixtral directory: [transformers](https://github.com/huggingface/transformers).


Updates `transformers` from 4.56.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.0...v5.5.0)

Updates `transformers` from 4.56.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.0...v5.5.0)

Updates `nemo-toolkit` from 2.0.0rc1 to 2.6.2
- [Release notes](https://github.com/nvidia/nemo/releases)
- [Changelog](https://github.com/NVIDIA-NeMo/Speech/blob/v2.6.2/CHANGELOG.md)
- [Commits](NVIDIA-NeMo/Speech@r2.0.0rc1...v2.6.2)

Updates `pytest` from 7.2.2 to 9.0.3
- [Release notes](https://github.com/pytest-dev/pytest/releases)
- [Changelog](https://github.com/pytest-dev/pytest/blob/main/CHANGELOG.rst)
- [Commits](pytest-dev/pytest@7.2.2...9.0.3)

Updates `transformers` from 4.56.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.0...v5.5.0)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: nemo-toolkit
  dependency-version: 2.6.2
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: pytest
  dependency-version: 9.0.3
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.5.0
  dependency-type: direct:production
  dependency-group: pip
...

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@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Aug 5, 2026
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