build(deps): bump the uv group across 1 directory with 2 updates

[dependabot]

Bumps the uv group with 2 updates in the / directory: h2 and transformers.

Updates h2 from 4.3.0 to 4.4.1

Changelog

Sourced from h2's changelog.

4.4.1 (2026-08-03)

Bugfixes

• Performance improvement: remove consumed frames in-place from data buffer.
• Reject duplicate Host headers in request headers. Thanks to Sunand Mohan for the report.

4.4.0 (2026-07-23)

API Changes (Backward Incompatible)

• Support for Python 3.9 has been removed.
• Support for PyPy 3.9 has been removed.
• Stream.end_stream() now raises NoSuchStreamError or StreamClosedError exceptions, instead of a generic KeyError.
• Duplicate content-length headers with different values now raise ProtocolError. Previously, the first content-length header was accepted and later conflicting values were ignored. Thanks to Harshal Parekh for the report.
• Parse content-length headers according to RFC9110 grammar for numbers (1*DIGIT). Thanks to Arkadiusz Marta for the report.
• backfill from v4.3.0 Convert emitted events into Python dataclass, which introduces new constructors with required arguments. Instantiating these events without arguments, as previously commonly used API pattern, will no longer work.

API Changes (Backward Compatible)

• Support for Python 3.14 has been added.
• H2Connection.receive_data now accepts any byte-like object that implements the buffer protocol, such as bytes, bytearray, and memoryview. Existing bytes callers are unaffected.
• Align CONNECT pseudo-header validation with RFC 9113 s8.3 and RFC 8441 s4. Ordinary CONNECT now requires :method=CONNECT and :authority, and forbids :scheme/:path. Extended CONNECT (e.g., WebSocket) requires :scheme, :path, :authority plus :protocol. (PR #1309)
• Fix incorrect substring matching of secure header in cookie and :method.

Bugfixes

• Fix to allow sending 0 bytes on a stream even if the flow control window is negative.
• Reject non-zero SETTINGS_ENABLE_PUSH values received from servers.

Commits

• bc239af v4.4.1
• 92b925e add test for duplicate host headers
• 292a408 reject duplicate Host headers in request headers
• 04d3b87 update changelog
• 439b970 prepare for next release cycle
• 9a7ff74 performance: remove consumed frames in place from data buffer (#1321)
• 6cce763 v4.4.0
• dfafda3 Bump pytest from 8.4.2 to 9.0.3 (#1320)
• b45207c dependencies and packaging++
• c40145f parse content-length headers according to RFC9110 grammar for numbers (1*DI...
• Additional commits viewable in compare view

Updates transformers from 5.6.2 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.

• who needs encoders? (#46385) by @​douglas-reid @​sgerrard @​vasqu @​molbap

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

• Add Sapiens2 Model (#45919) by @​guarin in #45919

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

• Add Deepseek-OCR-2 model (#45075) by @​thisisiron in #45075

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

• feat: Add support for JetBrains' Mellum v2 code generation model (#46112) by @​shadeMe in #46112

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.

• 🚨 Fix float16 overflow in Gemma4 vision pooler (#46277) by @​Bluear7878

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.

• 🚨 [ALM] Add base model without head (#45534) by @​eustlb

... (truncated)

Commits

• 90c3ae5 Patch because we had to yank 5.10 because the release branch was not up to date
• 0bd94b3 v5.10.0
• 1423d22 who needs encoders? (#46385)
• 50eb20a Fix dsv4 dequant + tp/ep (#46378)
• 74464e8 Fix wrong changes produced by style/repo. check bot (#46371)
• 1b8ec34 Fix path traversal when saving Bark voice preset embeddings (#46237)
• e820678 Add Sapiens2 Model (#45919)
• 595721c Pass library_name/version to Hub calls via a shared HfApi (#46318)
• 0f0036c docs: update ACL Anthology URL in CITATION.cff (#46352)
• fa6c830 DeepGEMM BF16 + mixed FP8/FP4 + MegaMoE + refactor (#45634)
• Additional commits viewable in compare view

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