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deps(lora-local-train): bump the lora-local-train-deps group across 1 directory with 5 updates - #1199

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Updates the requirements on torch, transformers, peft, datasets and tqdm to permit the latest version.
Updates torch to 2.14.0

Release notes

Sourced from torch's releases.

PyTorch 2.14.0 Release Notes

Highlights

For more details about these highlighted features, you can look at the release blogpost. Below are the full release notes for this release.

Backwards Incompatible Changes

torch.nn

  • torch.nn.LinearCrossEntropyOptions no longer accepts acc_policy="balanced"; use "compact" instead (#188283)

    The "balanced" policy was removed because "compact" provides the same weight-gradient accumulation precision with lower memory use on CUDA, already uses the equivalent scratch layout for mixed-precision inputs on other devices, and was never selected by "auto". Constructing the options with acc_policy="balanced" now raises ValueError: invalid acc_policy: 'balanced'; expected one of 'auto', 'accurate', 'compact'.

    Before:

    options = torch.nn.LinearCrossEntropyOptions(acc_policy="balanced")
loss = torch.nn.functional.linear_cross_entropy(
    input, linear_weight, target, options=options
)

    After:

    options = torch.nn.LinearCrossEntropyOptions(acc_policy="compact")
loss = torch.nn.functional.linear_cross_entropy(

... (truncated)

Changelog

Sourced from torch's changelog.

Releasing PyTorch

Release Compatibility Matrix

Following is the Release Compatibility Matrix for PyTorch releases:

... (truncated)

Commits
  • 2b3ec34 [release/2.14] Import SDPAParams in test_transformers to fix lint (#194970)
  • 08187d9 [cuDNN] Add guards for cuDNN SDPA decode (#194963)
  • 8ceea97 Pin cython < 3.3.0 for the Windows Triton wheel build (#194931)
  • 99ecebc [Cherry-pick][release/2.14] [inductor] Fix loop-local load CSE lifetime (#194...
  • ec283a7 Bump the Python 3.15 numpy pin to 2.5.2 (#194821)
  • 65890f3 Fix docker-release validate job to use the channel matching the pushed image ...
  • 1682388 Fix macOS py3.15 wheel builds: pin Cython < 3.3.0 and bump the cp315 numpy pi...
  • 9724418 Fix Windows py3.15 builds: constrain Cython < 3.3.0 and bump the cp315 numpy ...
  • f1b7554 [MPS] Fix pin_memory() recycling buffers still in use by the GPU (#194662)
  • 9f205f7 [MPS] fail loudly on large reductions (#194661)
  • Additional commits viewable in compare view

Updates transformers to 5.16.1

Release notes

Sourced from transformers's releases.

Release v5.16.1

This is a special release as we include GLM! (and a few small fixes)

GLM-5.3-Flash

GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.

GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.

Links: Documentation

Small patch fixes

Mainly BC behavior for TP and pinning a hf kernel for security reasons 🤗

Full Changelog: huggingface/transformers@v5.16.0...v5.16.1

Commits

Updates peft to 0.20.0

Release notes

Sourced from peft's releases.

v0.20.0

Highlights

This release adds no less than nine new PEFT methods and puts a lot of work into the surrounding infrastructure, for example adding a new image generation benchmark for the method comparison suite and greatly improving the documentation structure.

New Methods

HiRA

@​hqsiswiliam added "HiRA: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models" to PEFT (#2668). Instead of adding the low-rank product BA to the base weight, HiRA multiplies it elementwise (Hadamard product) with the frozen base weight. Because the base weight itself is full rank, the resulting update is no longer constrained to be low rank, while the trainable parameter count stays the same as LoRA's.

GLoRA

@​not-lain contributed GLoRA: "One-for-All: Generalized LoRA for Parameter-Efficient Fine-Tuning" in #3098. It is a flexible PEFT method that extends LoRA with configurable weight, activation, and bias adaptation, delivering richer fine-tuning with no extra inference cost. Use it when you need per-layer flexibility or stronger adaptation than vanilla LoRA. Skip it for non-Linear layers (e.g. Conv/Embedding) or when standard LoRA is already sufficient and simplicity matters.

BEFT

@​whubaichuan added "BEFT: Bias-Efficient Fine-Tuning of Language Models" in #3195. BEFT builds on the observation that fine-tuning bias terms alone can be competitive in low-data regimes, but goes further: rather than training all biases, it targets the value projection by default, as the authors found this to be most efficient. This brings the trainable parameter count down to roughly 0.01% of the total parameters.

MonteCLoRA

@​victor7246 integrated MonteCLoRA, "Robust and Efficient Fine-tuning of LLMs with Bayesian Reparameterization of Low-Rank Adaptation" in #2943. LoRA is known to be sensitive to hyperparameters like learning rate and batch size. This new LoRA variant addresses this by treating the low-rank parameters as a distribution rather than a point estimate, using Monte Carlo estimation to obtain a low-variance posterior estimate. This should make training noticeably more robust, at the cost of only O(rank) additional parameters.

VeLoRA

@​roymiles added VeLoRA: "Memory Efficient Training using Rank-1 Sub-Token Projections" in #3159. Unlike most PEFT methods, this LoRA variant targets activation memory rather than parameter count: intermediate activations are split into sub-tokens and compressed with a fixed rank-1 projection before being cached for the backward pass, then reconstructed during backpropagation. Conceptually, it is similar to gradient checkpointing, with a lower memory saving but running faster.

Uni-LoRA

@​KaiyangLi1992 contributed Uni-LoRA: "One Vector is All You Need" in #3257. The paper shows that parameter-efficient LoRA variants like VeRA and VB-LoRA can all be described as projecting the full LoRA parameter space down to a much smaller subspace, differing only in the choice of projection. Uni-LoRA uses a single global projection across the whole model instead of layer-wise ones, which allows cross-layer parameter sharing and thus very low parameter counts.

FRoD

@​Bane-Elvin added FRoD, "Full-Rank Efficient Fine-Tuning with Rotational Degrees for Fast Convergence", in #3270. Instead of adding low-rank deltas like LoRA, it reconstructs selected weights with shared rotational subspaces and sparse trainable coefficients. It is especially useful when fast convergence and a higher full-rank capacity ceiling are important, and its large sparse rotational subspace may also be promising for model merging. The main tradeoffs are the costly joint-decomposition initialization and slightly slower forward/backward passes than LoRA, so it may be less attractive for a one-off single-task fine-tune.

MiCA

@​sr-networks contributed MiCA, "MiCA Learns More Knowledge Than LoRA and Full Fine-Tuning", in #3260. MiCA is a LoRA variant that initializes B from the SVD of the base weight, taking the left singular vectors belonging to the smallest singular values, and then trains only A. The idea is that adapting these underused directions integrates new knowledge more effectively than adapting the dominant subspace, with less interference with existing capabilities.

DEFT

@​MAXNORM8650 added DEFT, "Decompositional Efficient Fine-Tuning for Text-to-Image Models", in #3342. This PEFT method splits a weight update into two learned low-rank parts: a projection that removes a sub-space of the frozen weight, and a low-rank update that injects new content in its place. Use DEFT for personalizing a text-to-image model from a few images while retaining the base model's instruction-following/editability with minimal forgetting. It is less of a fit if you don't need to preserve the base model's other capabilities (a plain additive adapter is simpler) or for layers beyond Linear/Conv1D. The PaRa method is also supported as a special case of DEFT.

Enhancements

Automatic LoRA target selection

@​oswaldoludwig added KappaTuneSelector in #3106 based on "The Condition Number as a Scale-Invariant Proxy for Information Encoding in Neural Units". The PR adds a new function, find_kappa_target_modules, which automatically identifies which modules best to target based on the condition number of the base weight matrices. This removes some of the guesswork from choosing target_modules when configuring LoRA or other PEFT methods.

... (truncated)

Commits

Updates datasets to 5.0.1

Release notes

Sourced from datasets's releases.

5.0.1

Bug fixes

Docs

New Contributors

... (truncated)

Commits
  • 921c2a7 release: 5.0.1 (#8370)
  • c6fc5cd Preserve nullable integer columns in to_json/to_csv/to_sql (#8366)
  • 6747b87 Fix DatasetDict.push_to_hub leaving removed splits in the dataset card (#8367)
  • b305031 fix buckets on windows (#8369)
  • 030a3e5 Decode Json() columns in Dataset.to_pandas() (#8344)
  • 0f207a0 Rebatch arrow source before formatting in IterableDataset.filter to fix resum...
  • adad35d Keep integers on the python read path for fixed-shape ArrayXD columns with nu...
  • 8966746 Fix CSV loader dropping on_bad_lines/encoding_errors on pandas 2.0-2.2 (#8358)
  • b8e861a Fix bucket dataset card handling and push metadata accounting (#8354)
  • 521a590 Keep flat numeric columns with nulls numeric in numpy format (#8352)
  • Additional commits viewable in compare view

Updates tqdm to 4.70.0

Release notes

Sourced from tqdm's releases.

tqdm v4.70.0 stable

  • contrib.concurrent: major improvements
    • support process_map(mp_context, max_tasks_per_child), thread_map(thread_name_prefix) (#1265)
    • fix total based on shortest iterable length (#1473)
    • use default max_workers (#1543 <- #1530, #1518)
    • support timeout, buffersize (#1576)
    • improve ETA (#1708 <- #1161)
    • update as_completed (#1709 <- #1565)
    • add tqdm.concurrent.intepreter_map (#1777)
  • asyncio: support iterables with only __aiter__ (#1714 <- #1686)
  • support reset(float("inf")) (#1783 <- #1781, #651)
  • framework: test & reduce wheel size (#1782)
Commits
  • 96f2e60 Merge pull request #1777 from shermansiu/feat/interpreter-pool
  • c27393e misc tidy
  • 061c623 Disable tqdm.monitor_interval for subinterpreters because they do not have ...
  • 9fc160b Update how we check for interpreter_map support in the tests
  • b42463a Ensure that subinterpreters can import tqdm while unpickling the initializer
  • 16d5486 Add support for nested progress bars
  • 9f5890f Add initial implementation for interpreter_map
  • 321f920 Merge pull request #1783 from LuShadowX/reset-inf-total
  • 4664b57 minor tidy
  • 426a098 Treat inf total as unknown in reset() too
  • Additional commits viewable in compare view

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@dependabot
deps(lora-local-train): bump the lora-local-train-deps group across 1…
961c015 
… directory with 5 updates

Updates the requirements on [torch](https://github.com/pytorch/pytorch), [transformers](https://github.com/huggingface/transformers), [peft](https://github.com/huggingface/peft), [datasets](https://github.com/huggingface/datasets) and [tqdm](https://github.com/tqdm/tqdm) to permit the latest version.

Updates `torch` to 2.14.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@ciflow/torchtitan/125995...v2.14.0)

Updates `transformers` to 5.16.1
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v5.13.0...v5.16.1)

Updates `peft` to 0.20.0
- [Release notes](https://github.com/huggingface/peft/releases)
- [Commits](huggingface/peft@v0.19.1...v0.20.0)

Updates `datasets` to 5.0.1
- [Release notes](https://github.com/huggingface/datasets/releases)
- [Commits](huggingface/datasets@5.0.0...5.0.1)

Updates `tqdm` to 4.70.0
- [Release notes](https://github.com/tqdm/tqdm/releases)
- [Commits](tqdm/tqdm@v4.68.4...v4.70.0)

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.14.0
  dependency-type: direct:production
  dependency-group: lora-local-train-deps
- dependency-name: transformers
  dependency-version: 5.16.1
  dependency-type: direct:production
  dependency-group: lora-local-train-deps
- dependency-name: peft
  dependency-version: 0.20.0
  dependency-type: direct:production
  dependency-group: lora-local-train-deps
- dependency-name: datasets
  dependency-version: 5.0.1
  dependency-type: direct:production
  dependency-group: lora-local-train-deps
- dependency-name: tqdm
  dependency-version: 4.70.0
  dependency-type: direct:production
  dependency-group: lora-local-train-deps
...

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@dependabot
dependabot Bot requested a review from Bryan-Roe as a code owner September 7, 2026 04:14
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dependabot Bot commented on behalf of github Sep 14, 2026

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Looks like these dependencies are updatable in another way, so this is no longer needed.

@dependabot dependabot Bot closed this Sep 14, 2026
@dependabot
dependabot Bot deleted the dependabot/pip/ai-projects/lora-training/microsoft_phi-silica-3.6_v1/local_train/lora-local-train-deps-e206450459 branch September 14, 2026 04:16
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