update typos (#3300)

Summary:

This diff solves part of Ali's comments in our tracer sheet (https://docs.google.com/spreadsheets/d/1PoJt7P9qMkFSaMmS9f9j8dVcTFhOmNHotQYpwBySydI/edit#gid=0). Specifically speaking：

"NanoGPT" -> "nanoGPT"
"CoreML" -> "Core ML"
"ExecuTorch Codebase" -> "ExecuTorch codebase"
"Android Phone" -> "Android phone"
"How to build Mobile Apps" -> "How to Build Mobile Apps"

also shorten the following two column names for avoid overlapping.
"occurrences_in_delegated_graphs" ->  "# in_delegated_graphs" "occurrences_in_non_delegated_graphs" -> # in_non_delegated_graphs

Differential Revision: D56513601

Author: Gasoonjia

docs/source/llm/getting-started.md

@@ -82,7 +82,7 @@ For more information, see [Setting Up ExecuTorch](https://pytorch.org/executorch
 
 ## Running a Large Language Model Locally
 
-This example uses Karpathy’s [NanoGPT](https://github.com/karpathy/nanoGPT), which is a minimal implementation of
+This example uses Karpathy’s [nanoGPT](https://github.com/karpathy/nanoGPT), which is a minimal implementation of
 GPT-2 124M. This guide is applicable to other language models, as ExecuTorch is model-invariant.
 
 There are two steps to running a model with ExecuTorch:
@@ -100,7 +100,7 @@ ExecuTorch runtime.
 
 Exporting takes a PyTorch model and converts it into a format that can run efficiently on consumer devices.
 
-For this example, you will need the NanoGPT model and the corresponding tokenizer vocabulary.
+For this example, you will need the nanoGPT model and the corresponding tokenizer vocabulary.
 
 ::::{tab-set}
 :::{tab-item} curl
@@ -377,12 +377,12 @@ specific hardware (delegation), and because it is doing all of the calculations
 While ExecuTorch provides a portable, cross-platform implementation for all
 operators, it also provides specialized backends for a number of different
 targets. These include, but are not limited to, x86 and ARM CPU acceleration via
-the XNNPACK backend, Apple acceleration via the CoreML backend and Metal
+the XNNPACK backend, Apple acceleration via the Core ML backend and Metal
 Performance Shader (MPS) backend, and GPU acceleration via the Vulkan backend.
 
 Because optimizations are specific to a given backend, each pte file is specific
 to the backend(s) targeted at export. To support multiple devices, such as
-XNNPACK acceleration for Android and CoreML for iOS, export a separate PTE file
+XNNPACK acceleration for Android and Core ML for iOS, export a separate PTE file
 for each backend.
 
 To delegate to a backend at export time, ExecuTorch provides the `to_backend()`
@@ -394,11 +394,11 @@ acceleration and optimization. Any portions of the computation graph not
 delegated will be executed by the ExecuTorch operator implementations.
 
 To delegate the exported model to the specific backend, we need to import its
-partitioner as well as edge compile config from ExecuTorch Codebase first, then
+partitioner as well as edge compile config from ExecuTorch codebase first, then
 call `to_backend` with an instance of partitioner on the `EdgeProgramManager`
 object `to_edge` function created.
 
-Here's an example of how to delegate NanoGPT to XNNPACK (if you're deploying to an Android Phone for instance):
+Here's an example of how to delegate nanoGPT to XNNPACK (if you're deploying to an Android phone for instance):
 
 ```python
 # export_nanogpt.py
@@ -417,7 +417,7 @@ from torch._export import capture_pre_autograd_graph
 
 from model import GPT
 
-# Load the NanoGPT model.
+# Load the nanoGPT model.
 model = GPT.from_pretrained('gpt2')
 
 # Create example inputs. This is used in the export process to provide
@@ -523,7 +523,7 @@ For more information regarding backend delegateion, see the ExecuTorch guides
 for the
 [XNNPACK Backend](https://pytorch.org/executorch/stable/tutorial-xnnpack-delegate-lowering.html)
 and
-[CoreML Backend](https://pytorch.org/executorch/stable/build-run-coreml.html).
+[Core ML Backend](https://pytorch.org/executorch/stable/build-run-coreml.html).
 
 ## Quantization
 
@@ -633,15 +633,15 @@ df = delegation_info.get_operator_delegation_dataframe()
 print(tabulate(df, headers="keys", tablefmt="fancy_grid"))
 ```
 
-For NanoGPT targeting the XNNPACK backend, you might see the following:
+For nanoGPT targeting the XNNPACK backend, you might see the following:
 ```
 Total  delegated  subgraphs:  86
 Number  of  delegated  nodes:  473
 Number  of  non-delegated  nodes:  430
 ```
 
 
-|    |  op_type                                 |  occurrences_in_delegated_graphs  |  occurrences_in_non_delegated_graphs  |
+|    |  op_type                                 |  # in_delegated_graphs  |  # in_non_delegated_graphs  |
 |----|---------------------------------|------- |-----|
 |  0  |  aten__softmax_default  |  12  |  0  |
 |  1  |  aten_add_tensor  |  37  |  0  |
@@ -663,7 +663,7 @@ print(print_delegated_graph(graph_module))
 This may generate a large amount of output for large models. Consider using "Control+F" or "Command+F" to locate the operator you’re interested in
 (e.g. “aten_view_copy_default”). Observe which instances are not under lowered graphs.
 
-In the fragment of the output for NanoGPT below, observe that embedding and add operators are delegated to XNNPACK while the sub operator is not.
+In the fragment of the output for nanoGPT below, observe that embedding and add operators are delegated to XNNPACK while the sub operator is not.
 
 ```
 %aten_unsqueeze_copy_default_22 : [num_users=1] = call_function[target=executorch.exir.dialects.edge._ops.aten.unsqueeze_copy.default](args = (%aten_arange_start_step_23, -2), kwargs = {})
@@ -879,7 +879,7 @@ def  replace_linear_with_custom_linear(module):
 
 The remaining steps are the same as the normal flow. Now you can run this module in eager mode as well as export to ExecuTorch.
 
-## How to build Mobile Apps
+## How to Build Mobile Apps
 You can execute an LLM using ExecuTorch on iOS and Android.
 
 **For iOS see the [iLLaMA App](https://pytorch.org/executorch/main/llm/llama-demo-ios.html).**