More doc nits (#1611)

docs/source/api_ref_rlhf.rst

@@ -4,7 +4,7 @@ torchtune.rlhf
 
 .. currentmodule:: torchtune.rlhf
 
-Components and losses for RLHF algorithms like PPO and DPO:
+Components and losses for RLHF algorithms like PPO and DPO.
 
 .. autosummary::
    :toctree: generated/

docs/source/deep_dives/recipe_deepdive.rst

@@ -14,9 +14,6 @@ This deep-dive will walk you through the design of training-recipes in torchtune
       * What are the core components that make up a recipe?
       * How should I structure a new recipe?
 
-
-What are Recipes?
------------------
 Recipes are the primary entry points for torchtune users. These can be thought of
 as "targeted" end-to-end pipelines for training and optionally evaluating LLMs.
 Each recipe implements a training method (eg: full fine-tuning) with a set of meaningful

docs/source/index.rst

@@ -123,12 +123,12 @@ torchtune tutorials.
    :hidden:
 
    tutorials/llama3
+   tutorials/chat
    tutorials/lora_finetune
    tutorials/qlora_finetune
    tutorials/qat_finetune
    tutorials/e2e_flow
    tutorials/datasets
-   tutorials/chat
    tutorials/memory_optimizations
 
 .. toctree::
@@ -138,9 +138,9 @@ torchtune tutorials.
    :hidden:
 
    deep_dives/checkpointer
-   deep_dives/comet_logging
    deep_dives/configs
    deep_dives/recipe_deepdive
+   deep_dives/comet_logging
    deep_dives/wandb_logging
 
 .. toctree::

docs/source/install.rst

@@ -9,7 +9,7 @@ Pre-requisites
 --------------
 
 torchtune requires PyTorch, so please install for your proper host and environment
-using the `Start Locally <https://pytorch.org/get-started/locally/>`_ page. You should also install
+using the `"Start Locally" <https://pytorch.org/get-started/locally/>`_ page. You should also install
 torchvision (for multimodal LLMs) and torchao (for quantization APIs). You can install either stable or
 nightly versions with the following commands:
 
@@ -18,8 +18,8 @@ nightly versions with the following commands:
     # Install stable version of PyTorch libraries using pip
     pip install torch torchvision torchao
 
-    # Nightly install for latest features
-    pip install --pre torch torchvision torchao --index-url https://download.pytorch.org/whl/nightly/cu121
+    # Or nightly install for latest features
+    pip install --pre torch torchvision torchao --index-url https://download.pytorch.org/whl/nightly/cu121 # full options are cpu/cu118/cu121/cu124
 
 
 Install via PyPI
@@ -65,6 +65,9 @@ you can also install the package locally with the following command.
     cd torchtune
     pip install -e .
 
+    # or for a developer installation
+    pip install -e .["dev"]
+
 |
 
 Install nightly build

docs/source/overview.rst

@@ -23,16 +23,16 @@ torchtune provides:
 - Interoperability with popular model zoos through checkpoint-conversion utilities
 - Training recipes for a variety of fine-tuning techniques
 - Integration with `Hugging Face Datasets <https://huggingface.co/docs/datasets/en/index>`_ for training and `EleutherAI's Eval Harness <https://github.com/EleutherAI/lm-evaluation-harness>`_ for evaluation
-- Support for distributed training using `FSDP <https://pytorch.org/docs/stable/fsdp.html>`_
+- Support for distributed training using `FSDP2 <https://github.com/pytorch/torchtitan/blob/main/docs/fsdp.md>`_
 - YAML configs for easily configuring training runs
 
 Excited? To get started, checkout some of our tutorials, including:
 
-- our :ref:`quickstart guide <finetune_llama_label>` to finetune your first LLM using torchtune.
-- our :ref:`LoRA tutorial <lora_finetune_label>` to learn about parameter-efficient finetuning with torchtune.
-- our :ref:`QLoRA tutorial <qlora_finetune_label>` to attain maximal memory efficiency with torchtune.
+- Our :ref:`quickstart guide <finetune_llama_label>` to finetune your first LLM using torchtune.
+- Our :ref:`LoRA tutorial <lora_finetune_label>` to learn about parameter-efficient finetuning with torchtune.
+- Our :ref:`QLoRA tutorial <qlora_finetune_label>` to attain maximal memory efficiency with torchtune.
 
-Eager for more? Check out our :ref:`recipes index<recipes_overview_label>` to see all the fine-tuning techniques we support.
+You can check out our :ref:`recipes overview<recipes_overview_label>` to see all the fine-tuning techniques we support.
 
 Key Concepts
 ------------
@@ -46,10 +46,9 @@ See the ":ref:`All About Configs<config_tutorial_label>`" deep-dive for more inf
 **Recipes.** Recipes can be thought of
 as targeted end-to-end pipelines for training and optionally evaluating LLMs.
 Each recipe implements a training method (eg: full fine-tuning) with a set of meaningful
-features (eg: FSDP + Activation Checkpointing + Gradient Accumulation + Reduced Precision training)
-applied to a given model family (eg: Llama2). See the ":ref:`What Are Recipes?<recipe_deepdive>`" deep-dive for more information.
+features (eg: FSDP2 + Activation Checkpointing + Gradient Accumulation + Reduced Precision training)
+applied to a given model family (eg: Llama3.1). See the ":ref:`What Are Recipes?<recipe_deepdive>`" deep-dive for more information.
 
-|
 
 .. _design_principles_label:
 

docs/source/tutorials/chat.rst

@@ -1,7 +1,7 @@
 .. _chat_tutorial_label:
 
 =================================
-Fine-tuning Llama3 with Chat Data
+Fine-Tuning Llama3 with Chat Data
 =================================
 
 Llama3 Instruct introduced a new prompt template for fine-tuning with chat data. In this tutorial,

docs/source/tutorials/first_finetune_tutorial.rst

@@ -66,11 +66,10 @@ Each recipe consists of three components:
 
 .. note::
 
-  Check out our :ref:`recipes index<recipes_overview_label>` to see all the fine-tuning techniques we support.
   To learn more about the concept of "recipes", check out our technical deep-dive: :ref:`recipe_deepdive`.
 
 torchtune provides built-in recipes for finetuning on single device, on multiple devices with `FSDP <https://pytorch.org/blog/introducing-pytorch-fully-sharded-data-parallel-api/>`_,
-using memory efficient techniques like `LoRA <https://arxiv.org/abs/2106.09685>`_, and more! Check out all our built-in recipes in our :ref:`recipe index<recipes_overview_label>`. You can also utilize the
+using memory efficient techniques like `LoRA <https://arxiv.org/abs/2106.09685>`_, and more! Check out all our built-in recipes in our :ref:`recipes overview<recipes_overview_label>`. You can also utilize the
 :code:`tune ls` command to print out all recipes and corresponding configs.
 
 .. code-block:: bash
@@ -88,7 +87,7 @@ using memory efficient techniques like `LoRA <https://arxiv.org/abs/2106.09685>`
   ...
 
 For the purposes of this tutorial, you'll will be using the recipe for finetuning a Llama2 model using `LoRA <https://arxiv.org/abs/2106.09685>`_ on
-a single device. For a more in-depth discussion on LoRA in torchtune, you can see the complete :ref:`lora_finetune_label` tutorial.
+a single device. For a more in-depth discussion on LoRA in torchtune, you can see the complete ":ref:`lora_finetune_label`" tutorial.
 
 .. note::
 
@@ -132,15 +131,15 @@ changing the LoRA rank, update batch size, etc.
 
 .. note::
 
-  Check out :ref:`config_tutorial_label` for a deeper dive on configs in torchtune.
+  Check out ":ref:`config_tutorial_label`" for a deeper dive on configs in torchtune.
 
 |
 
 Training a model
 ----------------
 Now that you have a model in the proper format and a config that suits your needs, let's get training!
 
-Just like all the other steps, you will be using the :ref:`tune <cli_label>` CLI tool to launch your finetuning run.
+Just like all the other steps, you will be using the tune CLI tool to launch your finetuning run.
 
 .. code-block:: bash
 
@@ -165,4 +164,4 @@ Next steps
 ----------
 
 Now that you have trained your model and set up your environment, let's take a look at what we can do with our
-new model by checking out the :ref:`E2E Workflow Tutorial<e2e_flow>`.
+new model by checking out the ":ref:`E2E Workflow Tutorial<e2e_flow>`".

docs/source/tutorials/lora_finetune.rst

@@ -1,8 +1,8 @@
 .. _lora_finetune_label:
 
-===========================
-Finetuning Llama2 with LoRA
-===========================
+============================
+Fine-Tuning Llama2 with LoRA
+============================
 
 This guide will teach you about `LoRA <https://arxiv.org/abs/2106.09685>`_, a parameter-efficient finetuning technique,
 and show you how you can use torchtune to finetune a Llama2 model with LoRA.

docs/source/tutorials/qat_finetune.rst

@@ -1,7 +1,7 @@
 .. _qat_finetune_label:
 
 ===========================
-Finetuning Llama3 with QAT
+Fine-Tuning Llama3 with QAT
 ===========================
 
 Quantization-Aware Training (QAT) is a common technique for users to quantize their

docs/source/tutorials/qlora_finetune.rst

@@ -1,7 +1,7 @@
 .. _qlora_finetune_label:
 
 =============================
-Finetuning Llama2 with QLoRA
+Fine-Tuning Llama2 with QLoRA
 =============================
 
 In this tutorial, we'll learn about `QLoRA <https://arxiv.org/abs/2305.14314>`_, an enhancement on top of