huggingface · sgugger · Jul 13, 2023 · Jul 11, 2023 · Jul 12, 2023 · Jul 12, 2023
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 # BORT
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, so we won't accept any new PRs changing its code.
+
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
+You can do so by running the following command: `pip install -U transformers==4.30.0`.
+
+</Tip>
+
 ## Overview
 
 The BORT model was proposed in [Optimal Subarchitecture Extraction for BERT](https://arxiv.org/abs/2010.10499) by

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 # M-CTC-T
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, so we won't accept any new PRs changing its code.
+
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
+You can do so by running the following command: `pip install -U transformers==4.30.0`.
+
+</Tip>
+
 ## Overview
 
 The M-CTC-T model was proposed in [Pseudo-Labeling For Massively Multilingual Speech Recognition](https://arxiv.org/abs/2111.00161) by Loren Lugosch, Tatiana Likhomanenko, Gabriel Synnaeve, and Ronan Collobert. The model is a 1B-param transformer encoder, with a CTC head over 8065 character labels and a language identification head over 60 language ID labels. It is trained on Common Voice (version 6.1, December 2020 release) and VoxPopuli. After training on Common Voice and VoxPopuli, the model is trained on Common Voice only. The labels are unnormalized character-level transcripts (punctuation and capitalization are not removed). The model takes as input Mel filterbank features from a 16Khz audio signal.

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 # RetriBERT
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, so we won't accept any new PRs changing its code.
+
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
+You can do so by running the following command: `pip install -U transformers==4.30.0`.
+
+</Tip>
+
 ## Overview
 
 The RetriBERT model was proposed in the blog post [Explain Anything Like I'm Five: A Model for Open Domain Long Form

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 # TAPEX
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, so we won't accept any new PRs changing its code.
+
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
+You can do so by running the following command: `pip install -U transformers==4.30.0`.
+
+</Tip>
+
 ## Overview
 
 The TAPEX model was proposed in [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://arxiv.org/abs/2107.07653) by Qian Liu,

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 # Trajectory Transformer
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, so we won't accept any new PRs changing its code.
+
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
+You can do so by running the following command: `pip install -U transformers==4.30.0`.
+
+</Tip>
+
 ## Overview
 
 The Trajectory Transformer model was proposed in [Offline Reinforcement Learning as One Big Sequence Modeling Problem](https://arxiv.org/abs/2106.02039)  by Michael Janner, Qiyang Li, Sergey Levine.

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 # VAN
 
+<Tip warning={true}>
+
+This model is in maintenance mode only, so we won't accept any new PRs changing its code.
+
+If you run into any issues running this model, please reinstall the last version that supported this model: v4.30.0.
+You can do so by running the following command: `pip install -U transformers==4.30.0`.
+
+</Tip>
+
 ## Overview
 
 The VAN model was proposed in [Visual Attention Network](https://arxiv.org/abs/2202.09741) by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.