Skip to content

Bump the pip group across 6 directories with 2 updates - #2

Open
dependabot[bot] wants to merge 1 commit into
mainfrom
dependabot/pip/public/api/skills/google/cloud/agent-platform-tuning/references/pip-10cf436a26
Open

Bump the pip group across 6 directories with 2 updates#2
dependabot[bot] wants to merge 1 commit into
mainfrom
dependabot/pip/public/api/skills/google/cloud/agent-platform-tuning/references/pip-10cf436a26

Conversation

@dependabot

@dependabot dependabot Bot commented on behalf of github Sep 2, 2026

Copy link
Copy Markdown

Bumps the pip group with 1 update in the /public/api/skills/google/cloud/agent-platform-tuning/references directory: pyarrow.
Bumps the pip group with 1 update in the /skills/google/cloud/agent-platform-tuning/references directory: pyarrow.
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/convnext-tiny-cifar10 directory: transformers.
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/detr-resnet50-cppe5 directory: transformers.
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/segformer-b0-foodseg103 directory: transformers.
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/smolvlm-256m-vqav2 directory: transformers.

Updates pyarrow from 19.0.1 to 23.0.1

Release notes

Sourced from pyarrow's releases.

Apache Arrow 23.0.1

Release Notes URL: https://arrow.apache.org/release/23.0.1.html

Apache Arrow 23.0.1 RC0

Release Notes: Release Candidate: 23.0.1 RC0

Apache Arrow 23.0.0

Release Notes URL: https://arrow.apache.org/release/23.0.0.html

Apache Arrow 23.0.0 RC2

Release Notes: Release Candidate: 23.0.0 RC2

Apache Arrow 22.0.0

Release Notes URL: https://arrow.apache.org/release/22.0.0.html

Apache Arrow 22.0.0 RC1

Release Notes: Release Candidate: 22.0.0 RC1

Apache Arrow 22.0.0 RC0

Release Notes: Release Candidate: 22.0.0 RC0

Apache Arrow 21.0.0

Release Notes URL: https://arrow.apache.org/release/21.0.0.html

Apache Arrow 21.0.0 RC6

Release Notes: Release Candidate: 21.0.0 RC6

Apache Arrow 21.0.0 RC5

Release Notes: Release Candidate: 21.0.0 RC5

Apache Arrow 21.0.0 RC4

Release Notes: Release Candidate: 21.0.0 RC4

Apache Arrow 21.0.0 RC3

Release Notes: Release Candidate: 21.0.0 RC3

Apache Arrow 21.0.0 RC2

Release Notes: Release Candidate: 21.0.0 RC2

Apache Arrow 20.0.0

Release Notes URL: https://arrow.apache.org/release/20.0.0.html

Apache Arrow 20.0.0 RC2

Release Notes: Release Candidate: 20.0.0 RC2

Apache Arrow 20.0.0 RC1

Release Notes: Release Candidate: 20.0.0 RC1

Apache Arrow 20.0.0 RC0

Release Notes: Release Candidate: 20.0.0 RC0

Commits

Updates pyarrow from 19.0.1 to 23.0.1

Release notes

Sourced from pyarrow's releases.

Apache Arrow 23.0.1

Release Notes URL: https://arrow.apache.org/release/23.0.1.html

Apache Arrow 23.0.1 RC0

Release Notes: Release Candidate: 23.0.1 RC0

Apache Arrow 23.0.0

Release Notes URL: https://arrow.apache.org/release/23.0.0.html

Apache Arrow 23.0.0 RC2

Release Notes: Release Candidate: 23.0.0 RC2

Apache Arrow 22.0.0

Release Notes URL: https://arrow.apache.org/release/22.0.0.html

Apache Arrow 22.0.0 RC1

Release Notes: Release Candidate: 22.0.0 RC1

Apache Arrow 22.0.0 RC0

Release Notes: Release Candidate: 22.0.0 RC0

Apache Arrow 21.0.0

Release Notes URL: https://arrow.apache.org/release/21.0.0.html

Apache Arrow 21.0.0 RC6

Release Notes: Release Candidate: 21.0.0 RC6

Apache Arrow 21.0.0 RC5

Release Notes: Release Candidate: 21.0.0 RC5

Apache Arrow 21.0.0 RC4

Release Notes: Release Candidate: 21.0.0 RC4

Apache Arrow 21.0.0 RC3

Release Notes: Release Candidate: 21.0.0 RC3

Apache Arrow 21.0.0 RC2

Release Notes: Release Candidate: 21.0.0 RC2

Apache Arrow 20.0.0

Release Notes URL: https://arrow.apache.org/release/20.0.0.html

Apache Arrow 20.0.0 RC2

Release Notes: Release Candidate: 20.0.0 RC2

Apache Arrow 20.0.0 RC1

Release Notes: Release Candidate: 20.0.0 RC1

Apache Arrow 20.0.0 RC0

Release Notes: Release Candidate: 20.0.0 RC0

Commits

Updates transformers from 4.49.0 to 5.10.1

Release notes

Sourced from transformers's releases.

Release v5.10.1

v5.10.0 was yanked as we publish on a corrupted branch. Sorry everyone, this happens when we rush a release!!!

New Model additions

Gemma4 unified+ Gemma4 MTP

Gemma 4 12B Unified is an encoder-free multimodal model with pretrained and instruction-tuned variants. Unlike standard Gemma 4, which uses dedicated encoder towers, Gemma 4 12B Unified projects raw inputs directly into the language model's embedding space through lightweight linear pipelines. This results in a simpler architecture while maintaining strong multimodal performance.

Key differences from standard Gemma 4:

  • No Vision Tower: Raw pixel patches are projected directly into LM space via a Dense + LayerNorm pipeline with factorized 2D positional embeddings, replacing the vision encoder.
  • No Audio Tower: Raw 16 kHz waveform samples are chunked into fixed-length frames and projected through a simple RMSNorm → Linear pipeline, replacing the mel spectrogram + Conformer encoder.
  • Shared Multimodal Pipeline: Both vision and audio use the same Gemma4UnifiedMultimodalEmbedder (RMSNorm → Linear) for the final projection to text hidden space.

You can find the original Gemma 4 12B Unified checkpoints under the Gemma 4 release.

Sapiens2

Sapiens2 is a family of high-resolution vision transformers pretrained on ~1 billion curated human images, designed for human-centric computer vision tasks including pose estimation, body-part segmentation, surface normal estimation, and pointmap estimation. The models scale from 0.4B to 5B parameters and train at native 1K resolution, with hierarchical 4K variants for extended spatial reasoning. Sapiens2 achieves substantial improvements over its predecessor with +4 mAP in pose estimation, +24.3 mIoU in body-part segmentation, and 45.6% error reduction in normal estimation.

Links: Documentation | Paper

DeepSeek-OCR-2

DeepSeek-OCR-2 is an OCR-specialized vision-language model built on a distinctive architecture that combines a SAM ViT-B vision encoder with a Qwen2 hybrid attention encoder, connected through an MLP projector to a DeepSeek-V2 Mixture-of-Experts (MoE) language model. The model features a hybrid attention mechanism that applies bidirectional attention over image tokens and causal attention over query tokens, enabling efficient and accurate document understanding. It supports both plain OCR tasks and grounding capabilities with coordinate-aware output for document conversion to markdown format.

Links: Documentation

Mellum

Mellum is a code-focused Mixture-of-Experts language model developed by JetBrains. It is derived from the Qwen3-MoE architecture with per-layer-type RoPE and interleaved sliding window attention. The model has 12B total parameters with 2.5B active parameters per token, using 64 routed experts with 8 activated per token across 28 layers.

Links: Documentation

Breaking changes

The Gemma4 vision pooler now casts inputs to float32 before scaling to prevent float16 overflow (inf saturation) with large checkpoints, which may cause minor numerical differences in outputs for users running Gemma-4 vision models in float16.

Audio Language Models (ALMs) now have a dedicated base model class without a language modeling head, aligning them with the design of Vision Language Models (VLMs); users relying on the previous model class structure should update their code to use the new base model class where appropriate.

... (truncated)

Commits

Updates transformers from 4.49.0 to 5.10.1

Release notes

Sourced from transformers's releases.

Release v5.10.1

v5.10.0 was yanked as we publish on a corrupted branch. Sorry everyone, this happens when we rush a release!!!

New Model additions

Gemma4 unified+ Gemma4 MTP

Gemma 4 12B Unified is an encoder-free multimodal model with pretrained and instruction-tuned variants. Unlike standard Gemma 4, which uses dedicated encoder towers, Gemma 4 12B Unified projects raw inputs directly into the language model's embedding space through lightweight linear pipelines. This results in a simpler architecture while maintaining strong multimodal performance.

Key differences from standard Gemma 4:

  • No Vision Tower: Raw pixel patches are projected directly into LM space via a Dense + LayerNorm pipeline with factorized 2D positional embeddings, replacing the vision encoder.
  • No Audio Tower: Raw 16 kHz waveform samples are chunked into fixed-length frames and projected through a simple RMSNorm → Linear pipeline, replacing the mel spectrogram + Conformer encoder.
  • Shared Multimodal Pipeline: Both vision and audio use the same Gemma4UnifiedMultimodalEmbedder (RMSNorm → Linear) for the final projection to text hidden space.

You can find the original Gemma 4 12B Unified checkpoints under the Gemma 4 release.

Sapiens2

Sapiens2 is a family of high-resolution vision transformers pretrained on ~1 billion curated human images, designed for human-centric computer vision tasks including pose estimation, body-part segmentation, surface normal estimation, and pointmap estimation. The models scale from 0.4B to 5B parameters and train at native 1K resolution, with hierarchical 4K variants for extended spatial reasoning. Sapiens2 achieves substantial improvements over its predecessor with +4 mAP in pose estimation, +24.3 mIoU in body-part segmentation, and 45.6% error reduction in normal estimation.

Links: Documentation | Paper

DeepSeek-OCR-2

DeepSeek-OCR-2 is an OCR-specialized vision-language model built on a distinctive architecture that combines a SAM ViT-B vision encoder with a Qwen2 hybrid attention encoder, connected through an MLP projector to a DeepSeek-V2 Mixture-of-Experts (MoE) language model. The model features a hybrid attention mechanism that applies bidirectional attention over image tokens and causal attention over query tokens, enabling efficient and accurate document understanding. It supports both plain OCR tasks and grounding capabilities with coordinate-aware output for document conversion to markdown format.

Links: Documentation

Mellum

Mellum is a code-focused Mixture-of-Experts language model developed by JetBrains. It is derived from the Qwen3-MoE architecture with per-layer-type RoPE and interleaved sliding window attention. The model has 12B total parameters with 2.5B active parameters per token, using 64 routed experts with 8 activated per token across 28 layers.

Links: Documentation

Breaking changes

The Gemma4 vision pooler now casts inputs to float32 before scaling to prevent float16 overflow (inf saturation) with large checkpoints, which may cause minor numerical differences in outputs for users running Gemma-4 vision models in float16.

Audio Language Models (ALMs) now have a dedicated base model class without a language modeling head, aligning them with the design of Vision Language Models (VLMs); users relying on the previous model class structure should update their code to use the new base model class where appropriate.

... (truncated)

Commits

Updates transformers from 4.49.0 to 5.10.1

Release notes

Sourced from transformers's releases.

Release v5.10.1

v5.10.0 was yanked as we publish on a corrupted branch. Sorry everyone, this happens when we rush a release!!!

New Model additions

Gemma4 unified+ Gemma4 MTP

Gemma 4 12B Unified is an encoder-free multimodal model with pretrained and instruction-tuned variants. Unlike standard Gemma 4, which uses dedicated encoder towers, Gemma 4 12B Unified projects raw inputs directly into the language model's embedding space through lightweight linear pipelines. This results in a simpler architecture while maintaining strong multimodal performance.

Key differences from standard Gemma 4:

  • No Vision Tower: Raw pixel patches are projected directly into LM space via a Dense + LayerNorm pipeline with factorized 2D positional embeddings, replacing the vision encoder.
  • No Audio Tower: Raw 16 kHz waveform samples are chunked into fixed-length frames and projected through a simple RMSNorm → Linear pipeline, replacing the mel spectrogram + Conformer encoder.
  • Shared Multimodal Pipeline: Both vision and audio use the same Gemma4UnifiedMultimodalEmbedder (RMSNorm → Linear) for the final projection to text hidden space.

You can find the original Gemma 4 12B Unified checkpoints under the Gemma 4 release.

Sapiens2

Sapiens2 is a family of high-resolution vision transformers pretrained on ~1 billion curated human images, designed for human-centric computer vision tasks including pose estimation, body-part segmentation, surface normal estimation, and pointmap estimation. The models scale from 0.4B to 5B parameters and train at native 1K resolution, with hierarchical 4K variants for extended spatial reasoning. Sapiens2 achieves substantial improvements over its predecessor with +4 mAP in pose estimation, +24.3 mIoU in body-part segmentation, and 45.6% error reduction in normal estimation.

Links: Documentation | Paper

DeepSeek-OCR-2

DeepSeek-OCR-2 is an OCR-specialized vision-language model built on a distinctive architecture that combines a SAM ViT-B vision encoder with a Qwen2 hybrid attention encoder, connected through an MLP projector to a DeepSeek-V2 Mixture-of-Experts (MoE) language model. The model features a hybrid attention mechanism that applies bidirectional attention over image tokens and causal attention over query tokens, enabling efficient and accurate document understanding. It supports both plain OCR tasks and grounding capabilities with coordinate-aware output for document conversion to markdown format.

Links: Documentation

Mellum

Mellum is a code-focused Mixture-of-Experts language model developed by JetBrains. It is derived from the Qwen3-MoE architecture with per-layer-type RoPE and interleaved sliding window attention. The model has 12B total parameters with 2.5B active parameters per token, using 64 routed experts with 8 activated per token across 28 layers.

Links: Documentation

Breaking changes

The Gemma4 vision pooler now casts inputs to float32 before scaling to prevent float16 overflow (inf saturation) with large checkpoints, which may cause minor numerical differences in outputs for users running Gemma-4 vision models in float16.

Audio Language Models (ALMs) now have a dedicated base model class without a language modeling head, aligning them with the design of Vision Language Models (VLMs); users relying on the previous model class structure should update their code to use the new base model class where appropriate.

... (truncated)

Commits

Updates transformers from 4.49.0 to 5.10.1

Release notes

Sourced from transformers's releases.

Release v5.10.1

v5.10.0 was yanked as we publish on a corrupted branch. Sorry everyone, this happens when we rush a release!!!

New Model additions

Gemma4 unified+ Gemma4 MTP

Gemma 4 12B Unified is an encoder-free multimodal model with pretrained and instruction-tuned variants. Unlike standard Gemma 4, which uses dedicated encoder towers, Gemma 4 12B Unified projects raw inputs directly into the language model's embedding space through lightweight linear pipelines. This results in a simpler architecture while maintaining strong multimodal performance.

Key differences from standard Gemma 4:

  • No Vision Tower: Raw pixel patches are projected directly into LM space via a Dense + LayerNorm pipeline with factorized 2D positional embeddings, replacing the vision encoder.
  • No Audio Tower: Raw 16 kHz waveform samples are chunked into fixed-length frames and projected through a simple RMSNorm → Linear pipeline, replacing the mel spectrogram + Conformer encoder.
  • Shared Multimodal Pipeline: Both vision and audio use the same Gemma4UnifiedMultimodalEmbedder (RMSNorm → Linear) for the final projection to text hidden space.

You can find the original Gemma 4 12B Unified checkpoints under the Gemma 4 release.

Sapiens2

Sapiens2 is a family of high-resolution vision transformers pretrained on ~1 billion curated human images, designed for human-centric computer vision tasks including pose estimation, body-part segmentation, surface normal estimation, and pointmap estimation. The models scale from 0.4B to 5B parameters and train at native 1K resolution, with hierarchical 4K variants for extended spatial reasoning. Sapiens2 achieves substantial improvements over its predecessor with +4 mAP in pose estimation, +24.3 mIoU in body-part segmentation, and 45.6% error reduction in normal estimation.

Links: Documentation | Paper

DeepSeek-OCR-2

DeepSeek-OCR-2 is an OCR-specialized vision-language model built on a distinctive architecture that combines a SAM ViT-B vision encoder with a Qwen2 hybrid attention encoder, connected through an MLP projector to a DeepSeek-V2 Mixture-of-Experts (MoE) language model. The model features a hybrid attention mechanism that applies bidirectional attention over image tokens and causal attention over query tokens, enabling efficient and accurate document understanding. It supports both plain OCR tasks and grounding capabilities with coordinate-aware output for document conversion to markdown format.

Links: Documentation

Mellum

Mellum is a code-focused Mixture-of-Experts language model developed by JetBrains. It is derived from the Qwen3-MoE architecture with per-layer-type RoPE and interleaved sliding window attention. The model has 12B total parameters with 2.5B active parameters per token, using 64 routed experts with 8 activated per token across 28 layers.

Links: Documentation

Breaking changes

The Gemma4 vision pooler now casts inputs to float32 before scaling to prevent float16 overflow (inf saturation) with large checkpoints, which may cause minor numerical differences in outputs for users running Gemma-4 vision models in float16.

Audio Language Models (ALMs) now have a dedicated base model class without a language modeling head, aligning them with the design of Vision Language Models (VLMs); users relying on the previous model class structure should update their code to use the new base model class where appropriate.

... (truncated)

Commits

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options

You can trigger Dependabot actions by commenting on this PR:

  • @dependabot rebase will rebase this PR
  • @dependabot recreate will recreate this PR, overwriting any edits that have been made to it
  • @dependabot show <dependency name> ignore conditions will show all of the ignore conditions of the specified dependency
  • @dependabot ignore <dependency name> major version will close this group update PR and stop Dependabot creating any more for the specific dependency's major version (unless you unignore this specific dependency's major version or upgrade to it yourself)
  • @dependabot ignore <dependency name> minor version will close this group update PR and stop Dependabot creating any more for the specific dependency's minor version (unless you unignore this specific dependency's minor version or upgrade to it yourself)
  • @dependabot ignore <dependency name> will close this group update PR and stop Dependabot creating any more for the specific dependency (unless you unignore this specific dependency or upgrade to it yourself)
  • @dependabot unignore <dependency name> will remove all of the ignore conditions of the specified dependency
  • @dependabot unignore <dependency name> <ignore condition> will remove the ignore condition of the specified dependency and ignore conditions
    You can disable automated security fix PRs for this repo from the Security Alerts page.

@dependabot
Bump the pip group across 6 directories with 2 updates
3b85d1c 
Bumps the pip group with 1 update in the /public/api/skills/google/cloud/agent-platform-tuning/references directory: [pyarrow](https://github.com/apache/arrow).
Bumps the pip group with 1 update in the /skills/google/cloud/agent-platform-tuning/references directory: [pyarrow](https://github.com/apache/arrow).
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/convnext-tiny-cifar10 directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/detr-resnet50-cppe5 directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/segformer-b0-foodseg103 directory: [transformers](https://github.com/huggingface/transformers).
Bumps the pip group with 1 update in the /skills/nvidia/tao-finetune-huggingface-model/examples/smolvlm-256m-vqav2 directory: [transformers](https://github.com/huggingface/transformers).


Updates `pyarrow` from 19.0.1 to 23.0.1
- [Release notes](https://github.com/apache/arrow/releases)
- [Commits](apache/arrow@apache-arrow-19.0.1...apache-arrow-23.0.1)

Updates `pyarrow` from 19.0.1 to 23.0.1
- [Release notes](https://github.com/apache/arrow/releases)
- [Commits](apache/arrow@apache-arrow-19.0.1...apache-arrow-23.0.1)

Updates `transformers` from 4.49.0 to 5.10.1
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.49.0...v5.10.1)

Updates `transformers` from 4.49.0 to 5.10.1
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.49.0...v5.10.1)

Updates `transformers` from 4.49.0 to 5.10.1
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.49.0...v5.10.1)

Updates `transformers` from 4.49.0 to 5.10.1
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.49.0...v5.10.1)

---
updated-dependencies:
- dependency-name: pyarrow
  dependency-version: 23.0.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: pyarrow
  dependency-version: 23.0.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.10.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.10.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.10.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 5.10.1
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot Bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Sep 2, 2026
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Reviewers

No reviews

Assignees

No one assigned

Labels

dependencies Pull requests that update a dependency file python Pull requests that update python code

Projects

None yet

Milestone

No milestone

Development

Successfully merging this pull request may close these issues.

0 participants