Bump transformers from 4.46.2 to 4.47.1

[dependabot]

Bumps transformers from 4.46.2 to 4.47.1.

Release notes

Sourced from transformers's releases.

v4.47.1

Patch release v4.47.1

We waited a little bit to make sure it was stable, thanks @​winglian for double checking and everyone for the fixes!

• Fix GA loss bugs and add unit test (#35121) Contributed by @​techkang and @​ArthurZucker.

• Fix num_items_in_batch not being an integer (#35115) Contributed by @​xspirus.

• Fix FSDP no longer working (#35212) Contributed by @​muellerzr.

• Don't use no_sync when DeepSpeed doesn't support it for certain ZeRO configurations (#35212) Contributed by @​winglian.

• Only import torch.distributed if it is available (#35133) Contributed by @​GaetanLepage.

• [Whisper] Patch float type on MPS (#35295) Contributed by @​eustlb. 🔜 we should probably have MPS CIs to avoid repeating this!

v4.47.0: PaliGemma-2, I-JEPA, OLMo-2, LayerSkip, Tensor Parallel

New models

PaliGemma-2

PaliGemma 2 and PaliGemma are lightweight open vision-language models (VLM) inspired by PaLI-3, and based on open components like the SigLIP vision model and the Gemma language model. PaliGemma takes both images and text as inputs and can answer questions about images with detail and context, meaning that PaliGemma can perform deeper analysis of images and provide useful insights, such as captioning for images and short videos, object detection, and reading text embedded within images.

PaliGemma 2 is available in 3B, 10B, and 28B parameter sizes, which are based on Gemma 2 2B, 9B, and 27B models, respectively. The original PaliGemma models are available in the 3B size. For more information on Gemma model variants, see the Gemma models list. PaliGemma model variants support different pixel resolutions for image inputs, including 224 x 224, 448 x 448, and 896 x 896 pixels.

I-JEPA

The I-JEPA model was proposed in Image-based Joint-Embedding Predictive Architecture by Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, Yann LeCun, Nicolas Ballas. I-JEPA is a self-supervised learning method that predicts the representations of one part of an image based on other parts of the same image. This approach focuses on learning semantic features without relying on pre-defined invariances from hand-crafted data transformations, which can bias specific tasks, or on filling in pixel-level details, which often leads to less meaningful representations.

• Add I-JEPA by @​jmtzt in #33125

OLMo 2

The OLMo2 model is the successor of the OLMo model, which was proposed in OLMo: Accelerating the Science of Language Models.

The architectural changes from the original OLMo model to this model are:

• RMSNorm is used instead of standard layer norm.
• Norm is applied to attention queries and keys.
• Norm is applied after attention/feedforward layers rather than before.

... (truncated)

Commits

• 241c04d [Whisper] patch float type on mps (#35295)
• 1b6cb1e don't use no_sync when deepspeed doesn't support it for certain zero stages (...
• b1d5d6d Fix FSDP no longer working (#35212)
• bf5d7c3 Only import torch.distributed if it is available (#35133)
• 4995230 v4.47.1
• d5ccfcc Fix num_items_in_batch not being an integer (#35115)
• 0485b6e Fix GA loss bugs and add unit test (#35121)
• 5d7739f Release: v4.47.0
• a5bb528 Fix signatures for processing kwargs (#35105)
• e27465c Adaptive dynamic number of speculative tokens (#34156)
• Additional commits viewable in compare view

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