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build(deps): bump transformers from 5.0.0 to 5.10.1 in /scripts/julia - #15

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Bumps transformers from 5.0.0 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)

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Bumps [transformers](https://github.com/huggingface/transformers) from 5.0.0 to 5.10.1.
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v5.0.0...v5.10.1)

---
updated-dependencies:
- dependency-name: transformers
  dependency-version: 5.10.1
  dependency-type: direct:production
...

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 Nov 3, 2026
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Codex Review Summary

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📝 Code Review ✅ Completed 2026-11-03T08:29:00.586302Z 1be1a14 PR opened
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@homanp homanp self-assigned this Nov 3, 2026

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💡 Codex Review

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Reviewed commit: 1be1a145bb

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Comment on lines 1 to 2
# Fully pinned runtime wheels; no source builds or model packaging hooks.
--require-hashes
--only-binary=:all:
--extra-index-url https://download.pytorch.org/whl/cpu

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P1 Badge Restore the binary-only installation guard

When a supported Python/platform combination lacks a matching wheel, this lock can now fall back to a hashed source distribution and execute its build hooks. Pip's install --help confirms that --only-binary :all: is what disables all source packages, and docs/vulnerability-catalog.md:134-149 explicitly promises wheel-only installation and prescribes that compile option; restore the removed directive so the lock still enforces that boundary.

Useful? React with 👍 / 👎.

--hash=sha256:fbefd73b521d3c75c5a1d7c77056b2c6339b81468d14d4e33c55f525418a5855
# via -r scripts/julia/requirements.in
torch==2.15.0+cpu ; sys_platform == 'linux' \
torch==2.15.0+cpu ; sys_platform == "linux" \

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P1 Badge Keep the macOS Torch entry in the universal lock

On macOS, pip skips this sole remaining Torch requirement because it is restricted to Linux, while requirements.in still requests torch==2.15.0 for Darwin and docs/vulnerability-catalog.md:106-107 declares macOS supported. A fresh macOS installation therefore has no Torch backend and the Julia classifier cannot load; regenerate the lock universally so the Darwin Torch wheels remain present.

Useful? React with 👍 / 👎.

@superagent-security

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Dependency Validation

🟠 The update didn't pass application checks

Finding classification uses a locally verified model and never downloads models or code, so these library issues cannot be triggered through the service. The updated library does break that classifier, though.

Advisories (4)
Package Version Advisory Affects your app
transformers 5.0.0 → 5.10.1 GHSA-29pf-2h5f-8g72 ✅ No
transformers 5.0.0 → 5.10.1 GHSA-fgcw-684q-jj6r ✅ No
transformers 5.0.0 → 5.10.1 GHSA-xrqw-3rrv-vx5w ✅ No
transformers 5.0.0 → 5.10.1 GHSA-x9r9-c232-4q39 ✅ No

View full investigation →

@homanp homanp closed this Nov 3, 2026
@dependabot @github

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OK, I won't notify you again about this release, but will get in touch when a new version is available. If you'd rather skip all updates until the next major or minor version, let me know by commenting @dependabot ignore this major version or @dependabot ignore this minor version.

If you change your mind, just re-open this PR and I'll resolve any conflicts on it.

@dependabot
dependabot Bot deleted the dependabot/pip/scripts/julia/transformers-5.10.1 branch November 3, 2026 14:45
@superagent-ai superagent-ai deleted a comment from superagent-security Bot Nov 3, 2026
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