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Bumps the pip group with 2 updates in the / directory: torch and transformers.

Updates torch from 2.6.0 to 2.7.1

Release notes

Sourced from torch's releases.

PyTorch 2.7.1 Release, bug fix release

This release is meant to fix the following issues (regressions / silent correctness):

Torch.compile

Fix Excessive cudagraph re-recording for HF LLM models (#152287) Fix torch.compile on some HuggingFace models (#151154) Fix crash due to Exception raised inside torch.autocast (#152503) Improve Error logging in torch.compile (#149831) Mark mutable custom operators as cacheable in torch.compile (#151194) Implement workaround for a graph break with older version einops (#153925) Fix an issue with tensor.view(dtype).copy_(...) (#151598)

Flex Attention

Fix assertion error due to inductor permuting inputs to flex attention (#151959) Fix performance regression on nanogpt speedrun (#152641)

Distributed

Fix extra CUDA context created by barrier (#149144) Fix an issue related to Distributed Fused Adam in Rocm/APEX when using nccl_ub feature (#150010) Add a workaround random hang in non-blocking API mode in NCCL 2.26 (#154055)

MacOS

Fix MacOS compilation error with Clang 17 (#151316) Fix binary kernels produce incorrect results when one of the tensor arguments is from a wrapped scalar on MPS devices (#152997)

Other

Improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396) Fix fmsub function definition (#152075) Fix Floating point exception in torch.mkldnn_max_pool2d (#151848) Fix abnormal inference output with XPU:1 device (#153067) Fix Illegal Instruction Caused by grid_sample on Windows (#152613) Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826) Fix error with dynamic linking of libgomp library (#150084) Fix segfault in profiler with Python 3.13 (#153848)

PyTorch 2.7.0 Release Notes

Highlights

... (truncated)

Commits

Updates transformers from 4.50.3 to 4.52.1

Release notes

Sourced from transformers's releases.

Patch release v4.51.3

A mix of bugs were fixed in this patch; very exceptionally, we diverge from semantic versioning to merge GLM-4 in this patch release.

  • Handle torch ver in flexattn (#37400)
  • handle torch version edge cases (#37399)
  • Add glm4 (#37388)

Patch Release 4.51.2

This is another round of bug fixes, but they are a lot more minor and outputs were not really affected!

Patch release v4.51.1

Since the release of Llama 4, we have fixed a few issues that we are now releasing in patch v4.51.1

  • Fixing flex attention for torch=2.6.0 (#37285)
  • more fixes for post-training llama4 (#37329)
  • Remove HQQ from caching allocator warmup (#37347)
  • fix derived berts _init_weights (#37341)
  • Fix init empty weights without accelerate (#37337)
  • Fix deepspeed with quantization (#37324)
  • fix llama4 training (#37319)
  • fix flex attn when optional args aren't passed (#37327)
  • Multiple llama4 fixe (#37353)

Thanks all for your patience

v4.51.0: Llama 4, Phi4-Multimodal, DeepSeek-v3, Qwen3

New Model Additions

Llama 4

image

Llama 4, developed by Meta, introduces a new auto-regressive Mixture-of-Experts (MoE) architecture.This generation includes two models:

  • The highly capable Llama 4 Maverick with 17B active parameters out of ~400B total, with 128 experts.
  • The efficient Llama 4 Scout also has 17B active parameters out of ~109B total, using just 16 experts.

Both models leverage early fusion for native multimodality, enabling them to process text and image inputs. Maverick and Scout are both trained on up to 40 trillion tokens on data encompassing 200 languages (with specific fine-tuning support for 12 languages including Arabic, Spanish, German, and Hindi).

For deployment, Llama 4 Scout is designed for accessibility, fitting on a single server-grade GPU via on-the-fly 4-bit or 8-bit quantization, while Maverick is available in BF16 and FP8 formats. These models are released under the custom Llama 4 Community License Agreement, available on the model repositories

Getting started with Llama 4 using transformers is straightforward. Make sure you have transformers v4.51.0 or later installed:

pip install -U transformers[hf_xet]
</tr></table>

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@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Jul 30, 2025
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update Python code labels Jul 30, 2025
@dependabot
Bump the pip group across 1 directory with 2 updates
21bfea3 
Bumps the pip group with 2 updates in the / directory: [torch](https://github.com/pytorch/pytorch) and [transformers](https://github.com/huggingface/transformers).


Updates `torch` from 2.6.0 to 2.7.1
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.6.0...v2.7.1)

Updates `transformers` from 4.50.3 to 4.52.1
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.50.3...v4.52.1)

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.7.1
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 4.52.1
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot force-pushed the dependabot/pip/pip-bece3aff9a branch from d9a933a to 21bfea3 Compare July 30, 2025 11:09
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