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Bumps the ml-nlp-dependencies group with 4 updates in the / directory: scikit-learn, datasets, accelerate and peft.

Updates scikit-learn from 1.4.0 to 1.8.0

Release notes

Sourced from scikit-learn's releases.

Release 1.8.0

We're happy to announce the 1.8.0 release.

You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_1_8_0.html and the long version of the change log under https://scikit-learn.org/stable/whats_new/v1.8.html

This version supports Python versions 3.11 to 3.14 and features support of free-threaded CPython.

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Scikit-learn 1.7.2

We're happy to announce the 1.7.2 release.

This release contains a few bug fixes and is the first version supporting Python 3.14.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.7.html#version-1-7-2

You can upgrade with pip as usual:

pip install -U scikit-learn

The conda-forge builds can be installed using:

conda install -c conda-forge scikit-learn

Thanks to everyone who contributed to this release !

Scikit-learn 1.7.1

We're happy to announce the 1.7.1 release.

This release contains fixes for a few regressions introduced in 1.7.

You can see the changelog here: https://scikit-learn.org/stable/whats_new/v1.7.html#version-1-7-1

You can upgrade with pip as usual:

</tr></table>

... (truncated)

Commits
  • 646da0f [cd build]
  • 4f4f283 Generate changelog
  • 967dcde Set version
  • cb1424b DOC Release highlights for 1.8 (#32809)
  • 5645b27 🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 (#32859)
  • 6b9fb11 🔒 🤖 CI Update lock files for free-threaded CI build(s) 🔒 :rob...
  • a0f6d88 🔒 🤖 CI Update lock files for array-api CI build(s) 🔒 🤖 ...
  • c1de8fc FIX Make get_namespace handle pandas dataframe input (#32838)
  • 764249a Fix _safe_indexing with non integer arrays on array API inputs (#32840)
  • eca5e0a FIX Add new default max_samples=None in Bagging estimators (#32825)
  • Additional commits viewable in compare view

Updates datasets from 4.3.0 to 4.5.0

Release notes

Sourced from datasets's releases.

4.5.0

Dataset Features

  • Add lance format support by @​eddyxu in huggingface/datasets#7913

    • Support for both Lance dataset (including metadata / manifests) and standalone .lance files
    • e.g. with lance-format/fineweb-edu
    from datasets import load_dataset
ds = load_dataset("lance-format/fineweb-edu", streaming=True)
for example in ds["train"]:
...

What's Changed

New Contributors

Full Changelog: huggingface/datasets@4.4.2...4.5.0

4.4.2

Bug fixes

Minor additions

New Contributors

... (truncated)

Commits

Updates accelerate from 1.11.0 to 1.12.0

Release notes

Sourced from accelerate's releases.

v1.12.0: Deepspeed Ulysses/ALST

Deepspeed Ulysses/ALST integration

Deepspeed Ulysses/ALST is an efficient way of training on long sequences by employing sequence parallelism and attention head parallelism. You can learn more about this technology in this paper https://arxiv.org/abs/2506.13996 or this deepspeed tutorial https://www.deepspeed.ai/tutorials/ulysses-alst-sequence-parallelism/.

To enable Deepspeed Ulysses, you first need to create ParallelismConfig and setting sp related args:

parallelism_config = ParallelismConfig(
    sp_backend="deepspeed",
    sp_size=2,
    sp_handler=DeepSpeedSequenceParallelConfig(...),
)

Then, you need to make sure to compute the correct loss as described on our docs

        ...
        losses_per_rank = torch.distributed.nn.functional.all_gather(loss, group=sp_group)
        good_tokens = (shift_labels != -100).view(-1).sum()
        good_tokens_per_rank = torch.distributed.nn.functional.all_gather(good_tokens, group=sp_group)
        total_loss = sum(
            losses_per_rank[rank] * good_tokens_per_rank[rank]
            for rank in range(sp_world_size)
            if good_tokens_per_rank[rank] > 0
        )
        total_good_tokens = sum(good_tokens_per_rank)
        loss = total_loss / max(total_good_tokens, 1)

Thanks @​S1ro1 for starting this work and for @​stas00 for finishing this work. Also thanks @​kashif for adding docs and reviewing/testing this PR !

This feature will also be available in HF Trainer thanks for this PR from @​stas00: huggingface/transformers#41832

Minor changes

New Contributors

... (truncated)

Commits

Updates peft from 0.17.1 to 0.18.1

Release notes

Sourced from peft's releases.

0.18.1

Small patch release containing the following changes:

  • #2934: Small fixes required for some special cases to work with the upcoming transformers v5 release
  • #2963: Fix to enable PEFT to run with AMD ROCm thanks to @​vladmandic
  • #2976: Fix a regression that inadvertently required transformers >= 4.52

0.18.0: RoAd, ALoRA, Arrow, WaveFT, DeLoRA, OSF, and more

Highlights

FIXME update list of all changes, so some more commits were added

New Methods

RoAd

@​ppetrushkov added RoAd: 2D Rotary Adaptation to PEFT in #2678. RoAd learns 2D rotation matrices that are applied using only element-wise multiplication, thus promising very fast inference with adapters in unmerged state.

Remarkably, besides LoRA, RoAd is the only PEFT method that supports mixed adapter batches. This means that when you have loaded a model with multiple RoAd adapters, you can use all of them for different samples in the same batch, which is much more efficient than switching adapters between batches:

model = PeftModel.from_pretrained(base_model, <path-to-road-adapter-A>, adapter_name="adapter-A")
model.add_adapter("adapter-B", <path-to-road-adapter-B>)
inputs = ...  # input with 3 samples
apply adapter A to sample 0, adapter B to sample 1, and use the base model for sample 2:
adapter_names = ["adapter-A", "adapter-B", "base"]
output_mixed = model(**inputs, adapter_names=adapter_names)
gen_mixed = model.generate(**inputs, adapter_names=adapter_names)

ALoRA

Activated LoRA is a technique added by @​kgreenewald in #2609 for causal language models, allowing to selectively enable LoRA adapters depending on a specific token invocation sequence in the input. This has the major benefit of being able to re-use most of the KV cache during inference when the adapter is only used to generate part of the response, after which the base model takes over again.

Arrow & GenKnowSub

@​TheTahaaa contributed not only support for Arrow, a dynamic routing algorithm between multiple loaded LoRAs in #2644, but also GenKnowSub, a technique built upon Arrow where the 'library' of LoRAs available to Arrow is first modified by subtracting general knowledge adapters (e.g., trained on subsets of Wikipedia) to enhance task-specific performance.

WaveFT

Thanks to @​Bilican, Wavelet Fine-Tuning (WaveFT) was added to PEFT in #2560. This method trains sparse updates in the wavelet domain of residual matrices, which is especially parameter efficient. It is very interesting for image generation, as it promises to generate diverse outputs while preserving subject fidelity.

DeLoRA

Decoupled Low-rank Adaptation (DeLoRA) was added by @​mwbini in #2780. This new PEFT method is similar to DoRA in so far as it decouples the angle and magnitude of the learned adapter weights. However, DeLoRA implements this in a way that promises to better prevent divergence. Moreover, it constrains the deviation of the learned weight by imposing an upper limit of the norm, which can be adjusted via the delora_lambda parameter.

OSF

... (truncated)

Commits

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@dependabot dependabot Bot added the dependencies Dependency version update label Feb 2, 2026
@dependabot
pip dependency update: bump the ml-nlp-dependencies group across 1 di…
199550b 
…rectory with 4 updates

Bumps the ml-nlp-dependencies group with 4 updates in the / directory: [scikit-learn](https://github.com/scikit-learn/scikit-learn), [datasets](https://github.com/huggingface/datasets), [accelerate](https://github.com/huggingface/accelerate) and [peft](https://github.com/huggingface/peft).


Updates `scikit-learn` from 1.4.0 to 1.8.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.4.0...1.8.0)

Updates `datasets` from 4.3.0 to 4.5.0
- [Release notes](https://github.com/huggingface/datasets/releases)
- [Commits](huggingface/datasets@4.3.0...4.5.0)

Updates `accelerate` from 1.11.0 to 1.12.0
- [Release notes](https://github.com/huggingface/accelerate/releases)
- [Commits](huggingface/accelerate@v1.11.0...v1.12.0)

Updates `peft` from 0.17.1 to 0.18.1
- [Release notes](https://github.com/huggingface/peft/releases)
- [Commits](huggingface/peft@v0.17.1...v0.18.1)

---
updated-dependencies:
- dependency-name: scikit-learn
  dependency-version: 1.8.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: ml-nlp-dependencies
- dependency-name: datasets
  dependency-version: 4.5.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: ml-nlp-dependencies
- dependency-name: accelerate
  dependency-version: 1.12.0
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: ml-nlp-dependencies
- dependency-name: peft
  dependency-version: 0.18.1
  dependency-type: direct:production
  update-type: version-update:semver-minor
  dependency-group: ml-nlp-dependencies
...

Signed-off-by: dependabot[bot] <support@github.com>
@dependabot dependabot Bot force-pushed the dependabot/pip/ml-nlp-dependencies-ea2005170d branch from f6f979b to 199550b Compare February 16, 2026 09:12
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