Bumps [transformers](https://github.com/huggingface/transformers) from
4.36.2 to 4.37.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/huggingface/transformers/releases">transformers's
releases</a>.</em></p>
<blockquote>
<h2>v4.37 Qwen2, Phi-2, SigLIP, ViP-LLaVA, Fast2SpeechConformer, 4-bit
serialization, Whisper longform generation</h2>
<h2>Model releases</h2>
<h3>Qwen2</h3>
<p>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.</p>
<p>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.</p>
<ul>
<li>Add qwen2 by <a
href="https://github.com/JustinLin610"><code>@JustinLin610</code></a>
in <a
href="https://redirect.github.com/huggingface/transformers/issues/28436">#28436</a></li>
</ul>
<h3>Phi-2</h3>
<p>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.</p>
<ul>
<li>[Phi2] Add support for phi2 models by <a
href="https://github.com/susnato"><code>@susnato</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/issues/28211">#28211</a></li>
<li>[Phi] Extend implementation to use GQA/MQA. by <a
href="https://github.com/gugarosa"><code>@gugarosa</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/issues/28163">#28163</a></li>
<li>update docs to add the <code>phi-2</code> example by <a
href="https://github.com/susnato"><code>@susnato</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/issues/28392">#28392</a></li>
<li>Fixes default value of <code>softmax_scale</code> in
<code>PhiFlashAttention2</code>. by <a
href="https://github.com/gugarosa"><code>@gugarosa</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/issues/28537">#28537</a></li>
</ul>
<h3>SigLIP</h3>
<p>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.</p>
<ul>
<li>Add SigLIP by <a
href="https://github.com/NielsRogge"><code>@NielsRogge</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/issues/26522">#26522</a></li>
<li>[SigLIP] Don't pad by default by <a
href="https://github.com/NielsRogge"><code>@NielsRogge</code></a> in <a
href="https://redirect.github.com/huggingface/transformers/issues/28578">#28578</a></li>
</ul>
<h3>ViP-LLaVA</h3>
<p>The VipLlava model was proposed in Making Large Multimodal Models
Understand Arbitrary Visual Prompts by Mu Cai, Haotian Liu, Siva Karthik
Mustikovela, Gregory P. Meyer, Yuning Chai, Dennis Park, Yong Jae
Lee.</p>
<p>VipLlava enhances the training protocol of Llava by marking images
and interact with the model using natural cues like a “red bounding box”
or “pointed arrow” during training.</p>
<ul>
<li>Adds VIP-llava to transformers by <a
href="https://github.com/younesbelkada"><code>@younesbelkada</code></a>
in <a
href="https://redirect.github.com/huggingface/transformers/issues/27932">#27932</a></li>
<li>Fix Vip-llava docs by <a
href="https://github.com/younesbelkada"><code>@younesbelkada</code></a>
in <a
href="https://redirect.github.com/huggingface/transformers/issues/28085">#28085</a></li>
</ul>
<h3>FastSpeech2Conformer</h3>
<p>The FastSpeech2Conformer model was proposed with the paper Recent
Developments On Espnet Toolkit Boosted By Conformer by Pengcheng Guo,
Florian Boyer, Xuankai Chang, Tomoki Hayashi, Yosuke Higuchi, Hirofumi
Inaguma, Naoyuki Kamo, Chenda Li, Daniel Garcia-Romero, Jiatong Shi,
Jing Shi, Shinji Watanabe, Kun Wei, Wangyou Zhang, and Yuekai Zhang.</p>
<p>FastSpeech 2 is a non-autoregressive model for text-to-speech (TTS)
synthesis, which develops upon FastSpeech, showing improvements in
training speed, inference speed and voice quality. It consists of a
variance adapter; duration, energy and pitch predictor and waveform and
mel-spectrogram decoder.</p>
<ul>
<li>Add FastSpeech2Conformer by <a
href="https://github.com/connor-henderson"><code>@connor-henderson</code></a>
in <a
href="https://redirect.github.com/huggingface/transformers/issues/23439">#23439</a></li>
</ul>
<h3>Wav2Vec2-BERT</h3>
<p>The Wav2Vec2-BERT model was proposed in Seamless: Multilingual
Expressive and Streaming Speech Translation by the Seamless
Communication team from Meta AI.</p>
<p>This model was pre-trained on 4.5M hours of unlabeled audio data
covering more than 143 languages. It requires finetuning to be used for
downstream tasks such as Automatic Speech Recognition (ASR), or Audio
Classification.</p>
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</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="8e3e145b42"><code>8e3e145</code></a>
[<code>GPTNeoX</code>] Fix BC issue with 4.36 (<a
href="https://redirect.github.com/huggingface/transformers/issues/28602">#28602</a>)</li>
<li><a
href="344943b88a"><code>344943b</code></a>
Fix <code>_speculative_sampling</code> implementation (<a
href="https://redirect.github.com/huggingface/transformers/issues/28508">#28508</a>)</li>
<li><a
href="5fc3e60cd8"><code>5fc3e60</code></a>
[SigLIP] Don't pad by default (<a
href="https://redirect.github.com/huggingface/transformers/issues/28578">#28578</a>)</li>
<li><a
href="5ee9fcb5cc"><code>5ee9fcb</code></a>
Fix wrong xpu device in DistributedType.MULTI_XPU mode (<a
href="https://redirect.github.com/huggingface/transformers/issues/28386">#28386</a>)</li>
<li><a
href="e156abd05a"><code>e156abd</code></a>
[Whisper] Finalize batched SOTA long-form generation (<a
href="https://redirect.github.com/huggingface/transformers/issues/27658">#27658</a>)</li>
<li><a
href="a485e469f6"><code>a485e46</code></a>
Add w2v2bert to pipeline (<a
href="https://redirect.github.com/huggingface/transformers/issues/28585">#28585</a>)</li>
<li><a
href="d381d85466"><code>d381d85</code></a>
Release: v4.37.0</li>
<li><a
href="db9a7e9d3d"><code>db9a7e9</code></a>
Don't save <code>processor_config.json</code> if a processor has no
extra attribute (<a
href="https://redirect.github.com/huggingface/transformers/issues/2">#2</a>...</li>
<li><a
href="772307be76"><code>772307b</code></a>
Making CTC training example more general (<a
href="https://redirect.github.com/huggingface/transformers/issues/28582">#28582</a>)</li>
<li><a
href="186aa6befe"><code>186aa6b</code></a>
[Whisper] Fix audio classification with weighted layer sum (<a
href="https://redirect.github.com/huggingface/transformers/issues/28563">#28563</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/huggingface/transformers/compare/v4.36.2...v4.37.0">compare
view</a></li>
</ul>
</details>
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Bumps [types-setuptools](https://github.com/python/typeshed) from
69.0.0.20240106 to 69.0.0.20240115.
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