Bump transformers from 4.35.0 to 4.37.2

[dependabot]

Bumps transformers from 4.35.0 to 4.37.2.

Release notes

Sourced from transformers's releases.

Patch release v4.37.2

Selection of fixes

• Protecting the imports for SigLIP's tokenizer if sentencepiece isn't installed
• Fix permissions issue on windows machines when using trainer in multi-node setup
• Allow disabling safe serialization when using Trainer. Needed for Neuron SDK
• Fix error when loading processor from cache
• torch < 1.13 compatible torch.load

Commits

• [Siglip] protect from imports if sentencepiece not installed (#28737)
• Fix weights_only (#28725)
• Enable safetensors conversion from PyTorch to other frameworks without the torch requirement (#27599)
• Don't fail when LocalEntryNotFoundError during processor_config.json loading (#28709)
• Use save_safetensor to disable safe serialization for XLA (#28669)
• Fix windows err with checkpoint race conditions (#28637)
• [SigLIP] Only import tokenizer if sentencepiece available (#28636)

Patch release: v4.37.1

A patch release to resolve import errors from removed custom types in generation utils

• Add back in generation types #28681

v4.37 Qwen2, Phi-2, SigLIP, ViP-LLaVA, Fast2SpeechConformer, 4-bit serialization, Whisper longform generation

Model releases

Qwen2

Qwen2 is the new model series of large language models from the Qwen team. Previously, the Qwen series was released, including Qwen-72B, Qwen-1.8B, Qwen-VL, Qwen-Audio, etc.

Qwen2 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes.

• Add qwen2 by @​JustinLin610 in #28436

Phi-2

Phi-2 is a transformer language model trained by Microsoft with exceptionally strong performance for its small size of 2.7 billion parameters. It was previously available as a custom code model, but has now been fully integrated into transformers.

• [Phi2] Add support for phi2 models by @​susnato in #28211
• [Phi] Extend implementation to use GQA/MQA. by @​gugarosa in #28163
• update docs to add the phi-2 example by @​susnato in #28392
• Fixes default value of softmax_scale in PhiFlashAttention2. by @​gugarosa in #28537

SigLIP

The SigLIP model was proposed in Sigmoid Loss for Language Image Pre-Training by Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, Lucas Beyer. SigLIP proposes to replace the loss function used in CLIP by a simple pairwise sigmoid loss. This results in better performance in terms of zero-shot classification accuracy on ImageNet.

• Add SigLIP by @​NielsRogge in #26522
• [SigLIP] Don't pad by default by @​NielsRogge in #28578

ViP-LLaVA

... (truncated)

Commits

• 345b9b1 Release: v4.37.2
• ea5ca81 [Siglip] protect from imports if sentencepiece not installed (#28737)
• 711bed1 Fix weights_only (#28725)
• 56ee444 Enable safetensors conversion from PyTorch to other frameworks without the to...
• 8d8fdb5 Don't fail when LocalEntryNotFoundError during processor_config.json load...
• e393419 Use save_safetensor to disable safe serialization for XLA (#28669)
• 3001543 Fix windows err with checkpoint race conditions (#28637)
• b94f5fd [SigLIP] Only import tokenizer if sentencepiece available (#28636)
• d02d006 Release: v4.37.1
• b102ab2 Add back in generation types (#28681)
• Additional commits viewable in compare view

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