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Chore(deps): bump transformers from 4.49.0 to 4.50.0 in /jupyter/trustyai/ubi9-python-3.11 #1127

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@dependabot dependabot bot commented on behalf of github May 20, 2025

Bumps transformers from 4.49.0 to 4.50.0.

Release notes

Sourced from transformers's releases.

Release v4.50.0

New Model Additions

Model-based releases

Starting with version v4.49.0, we have been doing model-based releases, additionally to our traditional, software-based monthly releases. These model-based releases provide a tag from which models may be installed.

Contrarily to our software-releases; these are not pushed to pypi and are kept on our GitHub. Each release has a tag attributed to it, such as:

  • v4.49.0-Gemma-3
  • v4.49.0-AyaVision

⚠️ As bugs are identified and fixed on each model, the release tags are updated so that installing from that tag always gives the best experience possible with that model.

Each new model release will always be based on the current state of the main branch at the time of its creation. This ensures that new models start with the latest features and fixes available.

For example, if two models—Gemma-3 and AyaVision—are released from main, and then a fix for gemma3 is merged, it will look something like this:

              o---- v4.49.0-Gemma-3 (includes AyaVision, plus main fixes)
            /                  \  
---o--o--o--o--o-- (fix for gemma3) --o--o--o main
       \          
        o---- v4.49.0-AyaVision

We strive to merge model specific fixes on their respective branches as fast as possible!

Gemma 3

image

Gemma 3 is heavily referenced in the following model-based release and we recommend reading these if you want all the information relative to that model.

The Gemma 3 model was proposed by Google. It is a vision-language model composed by a SigLIP vision encoder and a Gemma 2 language decoder linked by a multimodal linear projection.

It cuts an image into a fixed number of tokens same way as Siglip if the image does not exceed certain aspect ratio. For images that exceed the given aspect ratio, it crops the image into multiple smaller pacthes and concatenates them with the base image embedding.

One particularity is that the model uses bidirectional attention on all the image tokens. Also, the model interleaves sliding window local attention with full causal attention in the language backbone, where each sixth layer is a full causal attention layer.

Shield Gemma2

ShieldGemma 2 is built on Gemma 3, is a 4 billion (4B) parameter model that checks the safety of both synthetic and natural images against key categories to help you build robust datasets and models. With this addition to the Gemma family of models, researchers and developers can now easily minimize the risk of harmful content in their models across key areas of harm as defined below:

  • No Sexually Explicit content: The image shall not contain content that depicts explicit or graphic sexual acts (e.g., pornography, erotic nudity, depictions of rape or sexual assault).
  • No Dangerous Content: The image shall not contain content that facilitates or encourages activities that could cause real-world harm (e.g., building firearms and explosive devices, promotion of terrorism, instructions for suicide).
  • No Violence/Gore content: The image shall not contain content that depicts shocking, sensational, or gratuitous violence (e.g., excessive blood and gore, gratuitous violence against animals, extreme injury or moment of death).

We recommend using ShieldGemma 2 as an input filter to vision language models, or as an output filter of image generation systems. To train a robust image safety model, we curated training datasets of natural and synthetic images and instruction-tuned Gemma 3 to demonstrate strong performance.

... (truncated)

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@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels May 20, 2025
@openshift-ci openshift-ci bot requested review from andyatmiami and caponetto May 20, 2025 13:42
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Hi @dependabot[bot]. Thanks for your PR.

I'm waiting for a opendatahub-io member to verify that this patch is reasonable to test. If it is, they should reply with /ok-to-test on its own line. Until that is done, I will not automatically test new commits in this PR, but the usual testing commands by org members will still work. Regular contributors should join the org to skip this step.

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@openshift-ci openshift-ci bot added size/xxl and removed size/xxl labels May 21, 2025
@dependabot dependabot bot force-pushed the dependabot/pip/jupyter/trustyai/ubi9-python-3.11/transformers-4.50.0 branch from ed743d7 to 69e9f7f Compare May 21, 2025 02:59
@openshift-ci openshift-ci bot added size/xxl and removed size/xxl labels May 21, 2025
@dependabot dependabot bot force-pushed the dependabot/pip/jupyter/trustyai/ubi9-python-3.11/transformers-4.50.0 branch from 69e9f7f to 74506f8 Compare May 28, 2025 03:03
@openshift-ci openshift-ci bot added size/xxl and removed size/xxl labels May 28, 2025
@openshift-ci openshift-ci bot added size/xxl and removed size/xxl labels Jun 4, 2025
@dependabot dependabot bot force-pushed the dependabot/pip/jupyter/trustyai/ubi9-python-3.11/transformers-4.50.0 branch from 06cd081 to 9425229 Compare June 11, 2025 03:07
@openshift-ci openshift-ci bot added size/xxl and removed size/xxl labels Jun 11, 2025
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rebase

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Bumps [transformers](https://github.com/huggingface/transformers) from 4.49.0 to 4.50.0.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.49.0...v4.50.0)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 4.50.0
  dependency-type: direct:production
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot force-pushed the dependabot/pip/jupyter/trustyai/ubi9-python-3.11/transformers-4.50.0 branch from 9425229 to 6e6ae16 Compare June 13, 2025 19:39
@openshift-ci openshift-ci bot added size/xxl and removed size/xxl labels Jun 13, 2025
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