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

Updates torch from 2.6.0 to 2.8.0

Release notes

Sourced from torch's releases.

PyTorch 2.8.0 Release Notes

Highlights

... (truncated)

Commits
  • ba56102 Cherrypick: Add the RunLLM widget to the website (#159592)
  • c525a02 [dynamo, docs] cherry pick torch.compile programming model docs into 2.8 (#15...
  • a1cb3cc [Release Only] Remove nvshmem from list of preload libraries (#158925)
  • c76b235 Move out super large one off foreach_copy test (#158880)
  • 20a0e22 Revert "[Dynamo] Allow inlining into AO quantization modules (#152934)" (#158...
  • 9167ac8 [MPS] Switch Cholesky decomp to column wise (#158237)
  • 5534685 [MPS] Reimplement tri[ul] as Metal shaders (#158867)
  • d19e08d Cherry pick PR 158746 (#158801)
  • a6c044a [cherry-pick] Unify torch.tensor and torch.ops.aten.scalar_tensor behavior (#...
  • 620ebd0 [Dynamo] Use proper sources for constructing dataclass defaults (#158689)
  • Additional commits viewable in compare view

Updates transformers from 4.52.3 to 4.53.0

Release notes

Sourced from transformers's releases.

Release v4.53.0

Gemma3n

Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages.

Gemma 3n models use selective parameter activation technology to reduce resource requirements. This technique allows the models to operate at an effective size of 2B and 4B parameters, which is lower than the total number of parameters they contain. For more information on Gemma 3n's efficient parameter management technology, see the Gemma 3n page.

image

from transformers import pipeline
import torch
pipe = pipeline(
"image-text-to-text",
torch_dtype=torch.bfloat16,
model="google/gemma-3n-e4b",
device="cuda",
)
output = pipe(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg",
text="<image_soft_token> in this image, there is"
)
print(output)

Dia

image

Dia is an opensource text-to-speech (TTS) model (1.6B parameters) developed by Nari Labs. It can generate highly realistic dialogue from transcript including nonverbal communications such as laughter and coughing. Furthermore, emotion and tone control is also possible via audio conditioning (voice cloning).

Model Architecture: Dia is an encoder-decoder transformer based on the original transformer architecture. However, some more modern features such as rotational positional embeddings (RoPE) are also included. For its text portion (encoder), a byte tokenizer is utilized while for the audio portion (decoder), a pretrained codec model DAC is used - DAC encodes speech into discrete codebook tokens and decodes them back into audio.

Kyutai Speech-to-Text

Kyutai STT is a speech-to-text model architecture based on the Mimi codec, which encodes audio into discrete tokens in a streaming fashion, and a Moshi-like autoregressive decoder. Kyutai’s lab has released two model checkpoints:

... (truncated)

Commits

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Bumps the pip group with 2 updates: [torch](https://github.com/pytorch/pytorch) and [transformers](https://github.com/huggingface/transformers).


Updates `torch` from 2.6.0 to 2.8.0
- [Release notes](https://github.com/pytorch/pytorch/releases)
- [Changelog](https://github.com/pytorch/pytorch/blob/main/RELEASE.md)
- [Commits](pytorch/pytorch@v2.6.0...v2.8.0)

Updates `transformers` from 4.52.3 to 4.53.0
- [Release notes](https://github.com/huggingface/transformers/releases)
- [Commits](huggingface/transformers@v4.52.3...v4.53.0)

---
updated-dependencies:
- dependency-name: torch
  dependency-version: 2.8.0
  dependency-type: direct:production
  dependency-group: pip
- dependency-name: transformers
  dependency-version: 4.53.0
  dependency-type: direct:production
  dependency-group: pip
...

Signed-off-by: dependabot[bot] <[email protected]>
@dependabot dependabot bot added dependencies Pull requests that update a dependency file python Pull requests that update python code labels Aug 6, 2025
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