Bump the python-minor-and-patch group with 4 updates

[dependabot]

Bumps the python-minor-and-patch group with 4 updates: pypdf, huggingface-hub, transformers and accelerate.

Updates pypdf from 6.17.0 to 6.18.0

Release notes

Sourced from pypdf's releases.

Version 6.18.0, 2026-09-07

What's new

Please note that this release requires users which previously overwrote the default limits to migrate to the new approach: Docs

In short:

• Use apply_configuration as a context manager to temporarily overwrite configuration values.
• Use overwrite_configuration to change the configuration values for the whole session.

The old overwrites should continue to work in most cases. If you have migrated successfully and do not need the legacy handling anymore (which can cause some overhead), consider using the temporary disable_legacy_handling setting:

from pypdf import overwrite_configuration
overwrite_configuration(disable_legacy_handling=True)

In case you are experiencing some unexpected issues after the migration, consider opening a new issue (or commenting on an existing one) with the necessary details to reproduce it.

Security (SEC)

• Limit allowed length of indirect object tokens (#4055) by @​stefan6419846

Deprecations (DEP)

• Rework configuration value handling (#4044) by @​stefan6419846

New Features (ENH)

• Draw borders and backgrounds for appearance streams and annotations (#4033) by @​PJBrs

Full Changelog

Changelog

Sourced from pypdf's changelog.

Version 6.18.0, 2026-09-07

Security (SEC)

• Limit allowed length of indirect object tokens (#4055)

Deprecations (DEP)

• Rework configuration value handling (#4044)

New Features (ENH)

• Draw borders and backgrounds for appearance streams and annotations (#4033)

Full Changelog

Commits

• 18e4c22 REL: 6.18.0
• 82501d2 SEC: Limit allowed length of indirect object tokens (#4055)
• d35834b ENH: Draw borders and backgrounds for appearance streams and annotations (#4033)
• 4b16bbb STY: Rename variable for readability (#4049)
• a1ce7fb ROB: Do not crash when a page tree entry is not a dictionary (#4048)
• 06b97ce DEV: Bump zizmorcore/zizmor-action from 0.6.2 to 0.6.3 (#4053)
• d3f790c ROB: Do not crash when the annotations are not an array (#4046)
• 3dc716e STY: Enforce lowercase function variables (#4045)
• ec2406f DEP: Rework configuration value handling (#4044)
• See full diff in compare view

Updates huggingface-hub from 1.30.0 to 1.31.0

Release notes

Sourced from huggingface-hub's releases.

[v1.31.0] Custom labels for Sandboxes, More resilient downloads and more

🏷️ Custom labels for Sandboxes

Dedicated sandboxes now accept custom labels, attached to the underlying Job. This is useful for cost attribution, bookkeeping, or finding and reconnecting to the sandboxes created by a given controller run. Labels use the same -l / --label KEY=VALUE syntax as hf jobs run. They are merged with the labels the SDK uses internally, and invalid or reserved labels are rejected before a billable Job is started. Pool-based sandboxes are unchanged: custom labels are only accepted for dedicated sandboxes.

>>> from huggingface_hub import Sandbox
>>> sandbox = Sandbox.create(image="python:3.12", labels={"controller-run": "run-42"})

hf sandbox create --label controller-run=run-42 --label team=data-infra

• [Sandbox] Support custom Job labels by @​Wauplin in #4824

📥 More resilient downloads

A batch of fixes makes downloads more robust to unusual server responses, network hiccups and concurrent usage:

• A timeout while waiting for the response headers of a streamed download is now retried and resumed like a body read failure, instead of escaping http_get() while retries remain.

• Regular HTTP downloads no longer fail when the HEAD response has no Content-Length. The file size is validated against the GET response when available.

• snapshot_download now writes the refs/ cache file atomically, reusing the logic already used by hf_hub_download. This fixes a long-standing race when many concurrent snapshot_download calls target the same repo (seen in vLLM / llm-compressor).

• hf download --dry-run --local-dir ... no longer copies files from the Hub cache into the destination. On large files and slow disks this looked like a hang and could leave an incomplete file behind.

• [Download] Share retry handling for stream entry and body failures by @​Wauplin in #4826

• [Download] Tolerate missing HEAD Content-Length by @​Wauplin in #4805

• [Download] Write cache ref file atomically in snapshot_download by @​Wauplin in #4829

• [Download] Prevent cached file copies during local-dir dry runs by @​wakamex in #4817

🔌 httpx re-exported for library integrators

huggingface_hub now re-exports the HTTP library it uses as huggingface_hub.utils.httpx. Libraries built on top of huggingface_hub that need httpx types or exceptions (typically to catch errors) should import it from there rather than importing httpx directly. v1.x is built on httpx, and v2.x will move to its successor httpx2, so importing through huggingface_hub.utils keeps your code compatible with both. This is only for types and exceptions: to make requests to the Hub, keep using get_session().

from huggingface_hub.utils import httpx
try:
...
except httpx.HTTPError:
...

📚 Documentation: Utilities — The httpx module

• [httpx migration] Expose httpx as a huggingface_hub submodule by @​Wauplin in #4803

🔒 Security

HfFileSystem.get() now validates remote filenames before writing anything locally. A server-side filename such as folder/..\..\outside.txt could previously escape the destination directory on Windows during a recursive download. The same check already protected hf_hub_download, snapshot_download and bucket sync. Unsafe filenames now raise ValueError on all platforms, including when downloading to an explicitly named file or a file object.

... (truncated)

Commits

• 495b17c Release: v1.31.0
• 0f50db0 Release: v1.31.0.rc1
• d592df8 Revert "[Jobs] Add network groups to hf jobs run (#4833)"
• 3c08b69 Release: v1.31.0.rc0
• f148666 [CLI] Fix truncated command descriptions in the CLI reference (#4849)
• c4f076c (LFS)Fix SliceFileObj.iter yielding only the first 4MB chunk (#4844)
• 22fe960 Fix dotenv parser truncating unquoted values containing '#' (#4842)
• 6cae778 [Jobs] Add network groups to hf jobs run (#4833)
• 063b37b [CLI] Raise explicit error for shell-script extensions on Windows (#4846)
• b9c14d9 [Download] Write cache ref file atomically in snapshot_download (#4829)
• Additional commits viewable in compare view

Updates transformers from 5.16.1 to 5.17.0

Release notes

Sourced from transformers's releases.

Release 5.17.0

Release v5.17.0

New Model additions

HYV4

Hy4-Preview is a 780B-parameter mixture-of-experts language model that activates 49B parameters per token. Each MoE layer holds 256 routed experts plus one always-active shared expert and routes every token to 8 of them. The context window is 1M tokens.

The architecture combines four features:

• Multi-head Latent Attention (MLA) compresses keys and values into a low-rank latent (kv_lora_rank) that kv_b_proj expands back to one key/value per query head.
• DeepSeek Sparse Attention (DSA) selects index_topk keys per query with a lightweight indexer. Following IndexShare, only the layers marked "full" in indexer_types run an indexer; "shared" layers reuse the previous full layer's selection.
• Gated MLA with learnable attention sinks, where each head owns a sink logit that participates in the softmax and contributes no value, as in GPT-OSS.
• Independent Hyper-Connections (iHC) replace the plain residual path with hc_mult parallel residual streams that are collapsed before, and redistributed after, every sublayer.

The implementation does not execute the multi-token prediction (MTP) layers. Released checkpoints keep those weights so that other runtimes can use them for speculative decoding; they are ignored at load time.

Links: Documentation

• Add h4 (#48473) by @​ArthurZucker in #48473

VibeVoice

VibeVoice is a novel framework for synthesizing high-fidelity, long-form speech with multiple speakers by employing a next-token diffusion approach within a Large Language Model (LLM) structure. It's designed to capture the authentic conversational "vibe" and is particularly suited for generating audio content like podcasts and multi-participant audiobooks.

Links: Documentation

• Implement VibeVoice (#40546) by @​pengzhiliang in #40546

NeoMME

NeoMME is a family of efficient 260M and 800M parameter multimodal-native multilingual foundation encoders from H Company. It processes multilingual text tokens and raw image patches in a single bidirectional Transformer encoder, without a separately pretrained vision tower or causal language model.

NeoMME-Retriever is a model fine-tuned from the NeoMME backbone for visual document retrieval with joint late-interaction and dense objectives. It takes text queries and documents (text or page screenshots) and produces multi-vector embeddings for MeanMaxSim scoring (late-interaction) and mean-pooled embeddings for cosine similarity (dense).

Links: Documentation

... (truncated)

Commits

• 856157a v5.17.0
• 5b7dcb0 MRoPE continued (#48594)
• 50bbcc6 [fix] Update stale expected strings in HunYuanVL integration tests (#48646)
• e8bcd79 [Quantizaiton]support 5/6/7 bits in AutoRound (#48481)
• 3283d5f [fix] Update stale golden values and fix expected_logits shape in FlavaForPre...
• 5f47b5a [tests] Fix integration test golden values broken by fast image processor def...
• fc50134 Add Fun-ASR-Nano model (#46180)
• d9fe823 Fix YOLOS device mismatch with device_map="auto" (#46886)
• cbc1651 [Generate] Avoid unconditionally downloading remote hub file (#48620)
• bd05a4b Honor shift_labels in decoder-only LLM/VLM losses (#48493)
• Additional commits viewable in compare view

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

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