[dependabot]

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

Updates accelerate from 1.14.0 to 1.15.0

Release notes

Sourced from accelerate's releases.

v1.15.0: FSDP2 activation memory, dtensor improvements

FSDP2

A large batch of FSDP2 work this release: two fixes that cut activation memory at long sequence lengths, tied-embedding support on torch >= 2.13, and a round of checkpointing correctness and scale fixes.

Activation checkpointing was wrapping each child of the matched layer (self_attn, mlp, the norms) instead of the layer itself, so every inter-child activation stayed saved for backward. It now wraps the layer.

There's also a new FSDP2-only activation_checkpointing_offload, which moves the remaining per-layer checkpoint inputs to pinned CPU memory. Gradients are exactly those of plain activation checkpointing:

# fsdp2.yaml
fsdp_config:
  fsdp_version: 2
  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
  fsdp_activation_checkpointing: true
  fsdp_activation_checkpointing_offload: true

accelerate launch --config_file fsdp2.yaml train.py

• FSDP2 activation checkpointing: wrap the matched transformer layer itself, not each of its children by @​qgallouedec in #4172
• Add FSDP2 activation_checkpointing_offload: offload checkpointed layer inputs to pinned CPU memory by @​qgallouedec in #4175
• Fix FSDP2 tied-embedding models on torch >= 2.13: put the output embedding in the same fully_shard group by @​qgallouedec in #4171
• Fix FSDP2/PEFT/FULL_STATE_DICT dropping every rank's adapter shard except rank 0 by @​AmineDiro in #4206
• FSDP2: per-rank torch.save/load for SHARDED_STATE_DICT to fix 2800+ NPU checkpoint timeout by @​gygdh-001 in #4105
• Fix FSDP sharded checkpoint path resolution by @​HaomingSong in #4119
• Raise a clear error when FSDP is enabled on a mesh with no shard dimension by @​qgallouedec in #4180

DTensor

Two fixes for DTensor-sharded models, which you hit with FSDP2, tensor parallelism, or any N-D parallelism setup: gradient clipping no longer fails on the foreach op when plain tensors and DTensors are mixed, and prepare_model leaves an already-sharded model where it is:

• Clip grad norm support for dtensors by @​michaelbenayoun in #4219
• prepare_model: don't move DTensor-sharded models to the device by @​qgallouedec in #4181

Offloading & Quantization

An entire model can now be dispatched to disk, including tied weights — useful for tools like llm-compressor that compress large models on machines that can't hold them:

• [Offloading] Support full disk offloading by @​kylesayrs in #4083
• Fix disk offload crash on FP8 tensors by @​shoemoney in #4151

Trackers

Custom trackers can be registered by name and then selected from log_with= like any built-in one:

from accelerate import Accelerator
from accelerate.tracking import register_tracker_class
</tr></table> 

... (truncated)

Commits

• 6afc1e5 Release: v1.15.0
• 636a934 Fix nightly CI: tensor parallel size detection and DeepSpeed warmup steps (#4...
• 385d9fb docs: fix three dead links left by the docs restructure (#4230)
• c424d0b docs: fix garbled sentence in CONTRIBUTING.md (#4231)
• 0f7e35f Clip grad norm support for dtensors (#4219)
• b2ac509 Fix FSDP2/ PEFT/FULL_STATE_DICT dropping every rank's adapter shard except ...
• 33e8bc4 Fix load_accelerator_state only restoring one RNG backend (#4217)
• 50de3ad Treat MPS out-of-memory errors as OOM in find_executable_batch_size (#4227)
• ab5fe8e feat: add the neuron device branch in state (#4218)
• 9baf95b Fix MLFLOW_NESTED_RUN never being able to turn nesting off (#4223)
• Additional commits viewable in compare view

Updates transformers from 5.8.1 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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[chottokun]

不採用（クローズ）の理由

検証フェーズにおいて、本PRで導入される transformers==5.10.1 と本リポジトリの torch==2.6.0+cu124 の組み合わせで致命的な互換性障害（デグレ）が発生することを確認しました。

障害詳細

• transformers 5.10.1 の transformers.integrations.finegrained_fp8 は未サポートのデータ型 torch.float8_e8m0fnu を参照しており、Docling パイプラインが利用する from transformers import AutoProcessor を実行した際に以下の例外が発生します。

  AttributeError: module 'torch' has no attribute 'float8_e8m0fnu'
  ModuleNotFoundError: Could not import module 'AutoProcessor'.
  

• この結果、PDF変換やDoclingの全モデルパイプラインの読み込みがクラッシュします。
• CI（GitHub Actions）ではDoclingに依存しない軽量テストのみが実行されていたためGreen表示となっていましたが、本番環境およびローカル実動テストでは利用不能となる重大なリグレッションです。

代替案

• PyTorchが float8_e8m0fnu を正式サポートするバージョンに更新されるまで、transformers のバージョンは 5.8 系に据え置く（または 5.10 系のバグフィックス版を待つ）。
• Dependabot に対して @dependabot ignore transformers または個別のバージョン制限を設定することを推奨します。

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