Bump transformers from 4.32.1 to 4.42.3

[dependabot[bot]]

Bumps transformers from 4.32.1 to 4.42.3.

Release notes

Sourced from transformers's releases.

Patch release v4.42.3

Make sure we have attention softcapping for "eager" GEMMA2 model

After experimenting, we noticed that for the 27b model mostly, softcapping is a must. So adding it back (it should have been there, but an error on my side made it disappear) sorry all! 😭

• Gemma capping is a must for big models (#31698)

Patch release v4.42.2

Patch release

Thanks to our 2 contributors for their prompt fixing mostly applies for training and FA2!

• Fix Gemma2 4d attention mask (#31674) by @​hiyouga
• don't zero out the attention_mask when using sliding window with flash attention (#31670) by @​winglian

v4.42.1: Patch release

Patch release for commit:

• [HybridCache] Fix get_seq_length method (#31661)

v4.42.0: Gemma 2, RTDETR, InstructBLIP, LLAVa Next, New Model Adder

New model additions

Gemma-2

The Gemma2 model was proposed in Gemma2: Open Models Based on Gemini Technology and Research by Gemma2 Team, Google. Gemma2 models are trained on 6T tokens, and released with 2 versions, 2b and 7b.

The abstract from the paper is the following:

This work introduces Gemma2, a new family of open language models demonstrating strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma2 outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of our model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations

• Add gemma 2 by @​ArthurZucker in #31659

RTDETR

The RT-DETR model was proposed in DETRs Beat YOLOs on Real-time Object Detection by Wenyu Lv, Yian Zhao, Shangliang Xu, Jinman Wei, Guanzhong Wang, Cheng Cui, Yuning Du, Qingqing Dang, Yi Liu.

RT-DETR is an object detection model that stands for “Real-Time DEtection Transformer.” This model is designed to perform object detection tasks with a focus on achieving real-time performance while maintaining high accuracy. Leveraging the transformer architecture, which has gained significant popularity in various fields of deep learning, RT-DETR processes images to identify and locate multiple objects within them.

• New model support RTDETR by @​SangbumChoi in #29077

InstructBlip

The InstructBLIP model was proposed in InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning by Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale Fung, Steven Hoi. InstructBLIP leverages the BLIP-2 architecture for visual instruction tuning.

... (truncated)

Commits

• b7ee1e8 v4.42.3
• da50b41 Gemma capping is a must for big models (#31698)
• 086c74e v4.42.2
• 8691867 Fix Gemma2 4d attention mask (#31674)
• 7edc993 don't zero out the attention_mask when using sliding window with flash attent...
• e3cb841 v4.42.1
• b2455e5 [HybridCache] Fix get_seq_length method (#31661)
• 6c1d0b0 Release: v4.42.0
• 69b0f44 Add gemma 2 (#31659)
• be50a03 change anchor_image_size None for compatibility (#31640)
• Additional commits viewable in compare view

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