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-
-Lightning Transformers
-======================
-
-`Lightning Transformers <https://lightning-transformers.readthedocs.io/en/latest/>`_ offers a flexible interface for training and fine-tuning SOTA Transformer models
-using the :doc:`PyTorch Lightning Trainer <../common/trainer>`.
-
-.. code-block:: bash
-
-    pip install lightning-transformers
-
-In Lightning Transformers, we offer the following benefits:
-
-- Powered by `PyTorch Lightning <https://www.pytorchlightning.ai/>`_ - Accelerators, custom Callbacks, Loggers, and high performance scaling with minimal changes.
-- Backed by `HuggingFace Transformers <https://huggingface.co/transformers/>`_ models and datasets, spanning multiple modalities and tasks within NLP/Audio and Vision.
-- Task Abstraction for Rapid Research & Experimentation - Build your own custom transformer tasks across all modalities with little friction.
-- Powerful config composition backed by `Hydra <https://hydra.cc/>`_ - simply swap out models, optimizers, schedulers task, and many more configurations without touching the code.
-- Seamless Memory and Speed Optimizations - Out-of-the-box training optimizations such as `DeepSpeed ZeRO <https://pytorch-lightning.readthedocs.io/en/stable/advanced/model_parallel.html#deepspeed>`_ or `FairScale Sharded Training <https://pytorch-lightning.readthedocs.io/en/stable/advanced/model_parallel.html#fairscale-sharded-training>`_ with no code changes.
-
------------------
-
-Using Lightning-Transformers
-----------------------------
-
-Lightning Transformers has a collection of tasks for common NLP problems such as `language_modeling <https://lightning-transformers.readthedocs.io/en/latest/tasks/nlp/language_modeling.html#language-modeling>`_,
-`translation <https://lightning-transformers.readthedocs.io/en/latest/tasks/nlp/translation.html#translation>`_ and more. To use, simply:
-
-1. Pick a task to train (passed to ``train.py`` as ``task=``)
-
-2. Pick a dataset (passed to ``train.py`` as ``dataset=``)
-
-3. Customize the backbone, optimizer, or any component within the config
-
-4. Add any :doc:`Lightning supported parameters and optimizations <../common/trainer>`
-
-.. code-block:: bash
-
-    python train.py \
-        task=<TASK> \
-        dataset=<DATASET>
-        backbone.pretrained_model_name_or_path=<BACKBONE> # Optionally change the HF backbone
-        optimizer=<OPTIMIZER> # Optionally specify optimizer (Default AdamW)
-        trainer.<ANY_TRAINER_FLAGS> # Optionally specify Lightning trainer arguments
-
-
-To learn more about Lightning Transformers, please refer to the `Lightning Transformers documentation <https://lightning-transformers.readthedocs.io/en/latest/>`_.