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link tutorials and fix typos
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docs/source/api_ref_utilities.rst

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.. _dist_label:
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Distributed
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.. autosummary::
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:toctree: generated/
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.. _mp_label:
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Mixed Precision
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.. autosummary::
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metric_logging.DiskLogger
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Data
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.. autosummary::
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collate.padded_collate
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Checkpoint saving & loading
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:toctree: generated/

docs/source/examples/configs.rst

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.. _config_tutorial_label:
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=================
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Configs Deep-Dive
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=================
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* Be familiar with the :ref:`overview of TorchTune<overview_label>`
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* Make sure to :ref:`install TorchTune<install_label>`
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* Understand the :ref:`fundamentals of recipes<recipe_deepdive>``
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* Understand the :ref:`fundamentals of recipes<recipe_deepdive>`
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Where parameters are sourced

docs/source/examples/recipe_deepdive.rst

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- **Wrappers around external frameworks.** A recipe is **not** meant to be a wrapper around external frameworks. These are fully written in native-PyTorch using TorchTune building blocks. Dependencies are primarily in the form of additional utilities or interoperability with the surrounding ecosystem (eg: EluetherAI's evaluation harness).
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Configs
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-------
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If you're new to TorchTune or to LLMs generally, configs would be the first concept to understand and get familiar with.
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If you're an advanced user writing your own recipes, adding config files will improve your experimentation velocity and
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ability to collaborate on experiments.
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- TODO - point to config tutorial after this is landed
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Recipe Script
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-------------
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Running Recipes with Configs
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----------------------------
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To run a recipe with a set of user-defined parameters, you will need to write a config file.
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You can learn all about configs in our :ref:`config tutorial<config_tutorial_label>`.

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