Update transformers requirement from !=4.23.0,!=4.23.1,<4.41.0,>=4.19.0 to >=4.19.0,!=4.23.0,!=4.23.1,<4.48.0

[dependabot]

Updates the requirements on transformers to permit the latest version.

Release notes

Sourced from transformers's releases.

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.

Commits:

• Add OLMo November 2024 by @​2015aroras in #34551
• Rename OLMo November to OLMo2 by @​2015aroras in #34864

Layer-Skip Llama

We add support for Meta's Layer-Skip Llama 3.2 1B model.

The Llama3.2 1B model was continually pretrained with LayerSkip recipe, early exit loss and layer dropout, as presented in Layer Skip: Enabling Early Exit Inference and Self-Speculative Decoding and is capable of performing self-speculative decoding: decode with earlier layers and verify with remaining layers.

• Self-speculation (Layer-Skip Llama) by @​ArthurZucker in #34240

Tensor Parallel implementation

This PR uses the torch.distributed.tensor.parallel subpackage to implement Tensor Parallel for Llama (as an example).

... (truncated)

Commits

• 5d7739f Release: v4.47.0
• a5bb528 Fix signatures for processing kwargs (#35105)
• e27465c Adaptive dynamic number of speculative tokens (#34156)
• b0a51e5 Fix flaky Hub CI (test_trainer.py) (#35062)
• a928d9c [trainer] fix the GA model_accepts_loss_kwargs (#34915)
• e682c17 BLIP: this is correct now (#35081)
• 50189e3 Add I-JEPA (#33125)
• 95a855e Deprecate quanto and switch to optimum-quanto (#35001)
• 482cb28 Fix tie_word_embeddings handling for GGUF models (#35085)
• 3544705 Update Mistral conversion script (#34829)
• Additional commits viewable in compare view

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