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Bump the pip group across 6 directories with 23 updates - #534

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Bump the pip group across 6 directories with 23 updates#534
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Bumps the pip group with 2 updates in the /Flux.2-Klein-4B/RyzenAI directory: onnx and transformers.
Bumps the pip group with 23 updates in the /microsoft-Phi-4-mini-instruct/QAIRT directory:

Package From To
onnx 1.17.0 1.22.0
sentencepiece 0.2.0 0.2.1
transformers 4.46.0 5.5.0
aiohttp 3.13.3 3.14.1
bleach 6.3.0 6.4.0
bokeh 3.2.2 3.8.2
cryptography 45.0.7 48.0.1
flask 2.2.5 3.1.3
idna 3.11 3.15
jupyter-server 2.18.2 2.20.0
jupyterlab 4.5.7 4.5.9
lxml 5.2.1 6.1.0
paramiko 3.5.1 5.0.0
pillow 8.4.0 12.2.0
protobuf 3.20.2 5.29.6
pyarrow 23.0.0 23.0.1
pytest 8.1.1 9.0.3
requests 2.32.5 2.33.0
starlette 1.0.0 1.3.1
tornado 6.5.4 6.5.7
urllib3 2.6.3 2.7.0
webob 1.8.9 1.8.10
werkzeug 2.2.3 3.1.6

Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/baseline directory: transformers.
Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/cpu directory: transformers.
Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/cuda directory: transformers.
Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/webgpu directory: transformers.

Updates onnx from 1.20.1 to 1.22.0

Release notes

Sourced from onnx's releases.

v1.22.0

ONNX v1.22.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

What's Changed

Breaking Changes and Deprecations

Spec and Operator

Two new operators LinearAttention-27 and CausalConvWithState-27 were introduced.

Reference Implementation

Utilities and Tools

Build, CI and Tests

... (truncated)

Commits

Updates transformers from 4.56.2 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 onnx from 1.20.1 to 1.22.0

Release notes

Sourced from onnx's releases.

v1.22.0

ONNX v1.22.0 is now available with exciting new features! We would like to thank everyone who contributed to this release! Please visit onnx.ai to learn more about ONNX and associated projects.

What's Changed

Breaking Changes and Deprecations

Spec and Operator

Two new operators LinearAttention-27 and CausalConvWithState-27 were introduced.

Reference Implementation

Utilities and Tools

Build, CI and Tests

... (truncated)

Commits

Updates transformers from 4.56.2 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 4.56.2 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 4.56.2 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 4.56.2 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 4.56.2 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 tokensDescription has been truncated

@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Jul 8, 2026
Copilot AI review requested due to automatic review settings July 8, 2026 02:05
@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Jul 8, 2026
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@dependabot dependabot Bot mentioned this pull request Jul 8, 2026
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Bump the pip group across 6 directories with 23 updates
5e3ec1a 
Bumps the pip group with 2 updates in the /Flux.2-Klein-4B/RyzenAI directory: [onnx](https://github.com/onnx/onnx) and [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 23 updates in the /microsoft-Phi-4-mini-instruct/QAIRT directory:

| Package | From | To |
| --- | --- | --- |
| [onnx](https://github.com/onnx/onnx) | `1.17.0` | `1.22.0` |
| [sentencepiece](https://github.com/google/sentencepiece) | `0.2.0` | `0.2.1` |
| [transformers](https://github.com/huggingface/transformers) | `4.46.0` | `5.5.0` |
| [aiohttp](https://github.com/aio-libs/aiohttp) | `3.13.3` | `3.14.1` |
| [bleach](https://github.com/mozilla/bleach) | `6.3.0` | `6.4.0` |
| [bokeh](https://github.com/bokeh/bokeh) | `3.2.2` | `3.8.2` |
| [cryptography](https://github.com/pyca/cryptography) | `45.0.7` | `48.0.1` |
| [flask](https://github.com/pallets/flask) | `2.2.5` | `3.1.3` |
| [idna](https://github.com/kjd/idna) | `3.11` | `3.15` |
| [jupyter-server](https://github.com/jupyter-server/jupyter_server) | `2.18.2` | `2.20.0` |
| [jupyterlab](https://github.com/jupyterlab/jupyterlab) | `4.5.7` | `4.5.9` |
| [lxml](https://github.com/lxml/lxml) | `5.2.1` | `6.1.0` |
| [paramiko](https://github.com/paramiko/paramiko) | `3.5.1` | `5.0.0` |
| [pillow](https://github.com/python-pillow/Pillow) | `8.4.0` | `12.2.0` |
| [protobuf](https://github.com/protocolbuffers/protobuf) | `3.20.2` | `5.29.6` |
| [pyarrow](https://github.com/apache/arrow) | `23.0.0` | `23.0.1` |
| [pytest](https://github.com/pytest-dev/pytest) | `8.1.1` | `9.0.3` |
| [requests](https://github.com/psf/requests) | `2.32.5` | `2.33.0` |
| [starlette](https://github.com/Kludex/starlette) | `1.0.0` | `1.3.1` |
| [tornado](https://github.com/tornadoweb/tornado) | `6.5.4` | `6.5.7` |
| [urllib3](https://github.com/urllib3/urllib3) | `2.6.3` | `2.7.0` |
| [webob](https://github.com/Pylons/webob) | `1.8.9` | `1.8.10` |
| [werkzeug](https://github.com/pallets/werkzeug) | `2.2.3` | `3.1.6` |

Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/baseline directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/cpu directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/cuda directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /tencent-HY-MT1.5-1.8B/webgpu directory: [transformers](https://github.com/huggingface/transformers).


Updates `onnx` from 1.20.1 to 1.22.0
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](onnx/onnx@v1.20.1...v1.22.0)

Updates `transformers` from 4.56.2 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `onnx` from 1.20.1 to 1.22.0
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](onnx/onnx@v1.20.1...v1.22.0)

Updates `transformers` from 4.56.2 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.2 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.2 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.2 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.2 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `onnx` from 1.17.0 to 1.22.0
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](onnx/onnx@v1.20.1...v1.22.0)

Updates `sentencepiece` from 0.2.0 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.2.0...v0.2.1)

Updates `transformers` from 4.46.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `aiohttp` from 3.13.3 to 3.14.1
- [Changelog](https://github.com/aio-libs/aiohttp/blob/master/CHANGES.rst)
- [Commits](aio-libs/aiohttp@v3.13.3...v3.14.1)

Updates `bleach` from 6.3.0 to 6.4.0
- [Changelog](https://github.com/mozilla/bleach/blob/main/CHANGES)
- [Commits](mozilla/bleach@v6.3.0...v6.4.0)

Updates `bokeh` from 3.2.2 to 3.8.2
- [Changelog](https://github.com/bokeh/bokeh/blob/3.8.2/docs/CHANGELOG)
- [Commits](bokeh/bokeh@3.2.2...3.8.2)

Updates `cryptography` from 45.0.7 to 48.0.1
- [Changelog](https://github.com/pyca/cryptography/blob/main/CHANGELOG.rst)
- [Commits](pyca/cryptography@45.0.7...48.0.1)

Updates `flask` from 2.2.5 to 3.1.3
- [Release notes](https://github.com/pallets/flask/releases)
- [Changelog](https://github.com/pallets/flask/blob/main/CHANGES.rst)
- [Commits](pallets/flask@2.2.5...3.1.3)

Updates `idna` from 3.11 to 3.15
- [Release notes](https://github.com/kjd/idna/releases)
- [Changelog](https://github.com/kjd/idna/blob/master/HISTORY.md)
- [Commits](kjd/idna@v3.11...v3.15)

Updates `jupyter-server` from 2.18.2 to 2.20.0
- [Release notes](https://github.com/jupyter-server/jupyter_server/releases)
- [Changelog](https://github.com/jupyter-server/jupyter_server/blob/main/CHANGELOG.md)
- [Commits](jupyter-server/jupyter_server@v2.18.2...v2.20.0)

Updates `jupyterlab` from 4.5.7 to 4.5.9
- [Release notes](https://github.com/jupyterlab/jupyterlab/releases)
- [Changelog](https://github.com/jupyterlab/jupyterlab/blob/main/RELEASE.md)
- [Commits](https://github.com/jupyterlab/jupyterlab/compare/@jupyterlab/lsp@4.5.7...@jupyterlab/lsp@4.5.9)

Updates `lxml` from 5.2.1 to 6.1.0
- [Release notes](https://github.com/lxml/lxml/releases)
- [Changelog](https://github.com/lxml/lxml/blob/master/CHANGES.txt)
- [Commits](lxml/lxml@lxml-5.2.1...lxml-6.1.0)

Updates `onnx` from 1.17.0 to 1.22.0
- [Release notes](https://github.com/onnx/onnx/releases)
- [Changelog](https://github.com/onnx/onnx/blob/main/docs/Changelog-ml.md)
- [Commits](onnx/onnx@v1.20.1...v1.22.0)

Updates `paramiko` from 3.5.1 to 5.0.0
- [Commits](paramiko/paramiko@3.5.1...5.0.0)

Updates `pillow` from 8.4.0 to 12.2.0
- [Release notes](https://github.com/python-pillow/Pillow/releases)
- [Changelog](https://github.com/python-pillow/Pillow/blob/main/CHANGES.rst)
- [Commits](python-pillow/Pillow@8.4.0...12.2.0)

Updates `protobuf` from 3.20.2 to 5.29.6
- [Release notes](https://github.com/protocolbuffers/protobuf/releases)
- [Commits](https://github.com/protocolbuffers/protobuf/commits)

Updates `pyarrow` from 23.0.0 to 23.0.1
- [Release notes](https://github.com/apache/arrow/releases)
- [Commits](apache/arrow@apache-arrow-23.0.0...apache-arrow-23.0.1)

Updates `pytest` from 8.1.1 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@8.1.1...9.0.3)

Updates `requests` from 2.32.5 to 2.33.0
- [Release notes](https://github.com/psf/requests/releases)
- [Changelog](https://github.com/psf/requests/blob/main/HISTORY.md)
- [Commits](psf/requests@v2.32.5...v2.33.0)

Updates `sentencepiece` from 0.2.0 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.2.0...v0.2.1)

Updates `starlette` from 1.0.0 to 1.3.1
- [Release notes](https://github.com/Kludex/starlette/releases)
- [Changelog](https://github.com/Kludex/starlette/blob/main/docs/release-notes.md)
- [Commits](Kludex/starlette@1.0.0...1.3.1)

Updates `tornado` from 6.5.4 to 6.5.7
- [Changelog](https://github.com/tornadoweb/tornado/blob/master/docs/releases.rst)
- [Commits](tornadoweb/tornado@v6.5.4...v6.5.7)

Updates `transformers` from 4.46.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `urllib3` from 2.6.3 to 2.7.0
- [Release notes](https://github.com/urllib3/urllib3/releases)
- [Changelog](https://github.com/urllib3/urllib3/blob/main/CHANGES.rst)
- [Commits](urllib3/urllib3@2.6.3...2.7.0)

Updates `webob` from 1.8.9 to 1.8.10
- [Changelog](https://github.com/Pylons/webob/blob/main/CHANGES.txt)
- [Commits](Pylons/webob@1.8.9...1.8.10)

Updates `werkzeug` from 2.2.3 to 3.1.6
- [Release notes](https://github.com/pallets/werkzeug/releases)
- [Changelog](https://github.com/pallets/werkzeug/blob/main/CHANGES.rst)
- [Commits](pallets/werkzeug@2.2.3...3.1.6)

Updates `sentencepiece` from 0.2.0 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.2.0...v0.2.1)

Updates `transformers` from 4.46.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `sentencepiece` from 0.2.0 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.2.0...v0.2.1)

Updates `transformers` from 4.46.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `sentencepiece` from 0.2.0 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.2.0...v0.2.1)

Updates `transformers` from 4.46.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `sentencepiece` from 0.2.0 to 0.2.1
- [Release notes](https://github.com/google/sentencepiece/releases)
- [Commits](google/sentencepiece@v0.2.0...v0.2.1)

Updates `transformers` from 4.46.0 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

Updates `transformers` from 4.56.1 to 5.5.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.56.2...v5.5.0)

---
updated-dependencies:
- dependency-name: aiohttp
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- dependency-name: bleach
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- dependency-name: bokeh
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- dependency-name: cryptography
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- dependency-name: flask
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- dependency-name: idna
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- dependency-name: jupyter-server
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- dependency-name: jupyterlab
  dependency-version: 4.5.9
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- dependency-name: lxml
  dependency-version: 6.1.0
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- dependency-name: onnx
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- dependency-name: onnx
  dependency-version: 1.22.0
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- dependency-name: onnx
  dependency-version: 1.22.0
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- dependency-name: onnx
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  dependency-type: direct:production
- dependency-name: paramiko
  dependency-version: 5.0.0
  dependency-type: direct:production
- dependency-name: pillow
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  dependency-type: direct:production
- dependency-name: protobuf
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  dependency-type: direct:production
- dependency-name: pyarrow
  dependency-version: 23.0.1
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- dependency-name: pytest
  dependency-version: 9.0.3
  dependency-type: direct:production
- dependency-name: requests
  dependency-version: 2.33.0
  dependency-type: direct:production
- dependency-name: sentencepiece
  dependency-version: 0.2.1
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- dependency-name: sentencepiece
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- dependency-name: sentencepiece
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- dependency-name: sentencepiece
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- dependency-name: sentencepiece
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- dependency-name: sentencepiece
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- dependency-name: starlette
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- dependency-name: tornado
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  dependency-type: direct:production
- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
  dependency-version: 5.3.0
  dependency-type: direct:production
- dependency-name: transformers
  dependency-version: 5.3.0
  dependency-type: direct:production
- dependency-name: transformers
  dependency-version: 5.3.0
  dependency-type: direct:production
- dependency-name: transformers
  dependency-version: 5.3.0
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
  dependency-version: 5.3.0
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
  dependency-version: 5.3.0
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- dependency-name: transformers
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- dependency-name: transformers
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- dependency-name: transformers
  dependency-version: 5.3.0
  dependency-type: direct:production
- dependency-name: urllib3
  dependency-version: 2.7.0
  dependency-type: direct:production
- dependency-name: webob
  dependency-version: 1.8.10
  dependency-type: direct:production
- dependency-name: werkzeug
  dependency-version: 3.1.6
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot
dependabot Bot force-pushed the dependabot/pip/Flux.2-Klein-4B/RyzenAI/pip-53e690644d branch from 0ed13b7 to 5e3ec1a Compare July 20, 2026 06:24
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