Release docs proofreading (#6124)

Release docs proofreading (#5909)

Summary: Pull Request resolved: #5909

Reviewed By: mergennachin

Differential Revision: D63930714

Pulled By: dvorjackz

fbshipit-source-id: 46134ac5750315606dffbf246da55e712dd192e8
(cherry picked from commit 17c2f36)

Co-authored-by: Jack Zhang <[email protected]>

Author: pytorchbot

examples/models/phi-3-mini-lora/README.md

@@ -11,7 +11,7 @@ To see how you can use the model exported for training in a fully involved finet
 - `./examples/models/phi-3-mini-lora/install_requirements.sh`
 
 ### Step 3: Export and run the model
-1. Export the inferenace and training models to ExecuTorch.
+1. Export the inference and training models to ExecuTorch.
 ```
 python export_model.py
 ```

extension/llm/README.md

@@ -2,8 +2,9 @@ This subtree contains libraries and utils of running generative AI, including La
 Below is a list of sub folders.
 ## export
 Model preparation codes are in _export_ folder. The main entry point is the _LLMEdgeManager_ class. It hosts a _torch.nn.Module_, with a list of methods that can be used to prepare the LLM model for ExecuTorch runtime.
-Note that ExecuTorch supports two [quantization APIs](https://pytorch.org/docs/stable/quantization.html#quantization-api-summary): eager mode quantization (aka source transform based quantization), and PyTorch 2 Export based quantization (aka pt2e quantization).
-Typical methods include:
+Note that ExecuTorch supports two [quantization APIs](https://pytorch.org/docs/stable/quantization.html#quantization-api-summary): eager mode quantization (aka source transform based quantization) and PyTorch 2 Export based quantization (aka pt2e quantization).
+
+Commonly used methods in this class include:
 - _set_output_dir_: where users want to save the exported .pte file.
 - _to_dtype_: override the data type of the module.
 - _source_transform_: execute a series of source transform passes. Some transform passes include
@@ -19,7 +20,7 @@ Typical methods include:
 
 Some usage of LLMEdgeManager can be found in executorch/examples/models/llama2, and executorch/examples/models/llava.
 
-When the .pte file is exported and saved, we can prepare a load and run it in a runner.
+When the .pte file is exported and saved, we can load and run it in a runner (see below).
 
 ## tokenizer
 Currently, we support two types of tokenizers: sentencepiece and Tiktoken.
@@ -28,20 +29,21 @@ Currently, we support two types of tokenizers: sentencepiece and Tiktoken.
   - _tokenizer.py_: rewrite a sentencepiece tokenizer model to a serialization format that the runtime can load.
 - In C++:
   - _tokenizer.h_: a simple tokenizer interface. Actual tokenizer classes can be implemented based on this. In this folder, we provide two tokenizer implementations:
-  - _bpe_tokenizer_. We need the rewritten version of tokenizer artifact (refer to _tokenizer.py_ above), for bpe tokenizer to work.
-  - _tiktokern_. It's for llama3 and llama3.1.
+    - _bpe_tokenizer_. Note: we need the rewritten version of tokenizer artifact (refer to _tokenizer.py_ above), for bpe tokenizer to work.
+    - _tiktoken_. For llama3 and llama3.1.
 
 ## sampler
 A sampler class in C++ to sample the logistics given some hyperparameters.
 
 ## custom_ops
-It hosts a custom sdpa operator. This sdpa operator implements CPU flash attention, it avoids copies by taking the kv cache as one of the arguments to this custom operator.
-- _sdpa_with_kv_cache.py_, _op_sdpa_aot.cpp_: custom op definition in PyTorch with C++ registration.
-- _op_sdpa.cpp_: the optimized operator implementation and registration of _sdpa_with_kv_cache.out_.
+Contains custom op, such as:
+- custom sdpa: implements CPU flash attention and avoids copies by taking the kv cache as one of its arguments.
+  - _sdpa_with_kv_cache.py_, _op_sdpa_aot.cpp_: custom op definition in PyTorch with C++ registration.
+  - _op_sdpa.cpp_: the optimized operator implementation and registration of _sdpa_with_kv_cache.out_.
 
 ## runner
 It hosts the libary components used in a C++ llm runner. Currently, it hosts _stats.h_ on runtime status like token numbers and latency.
 
-With the components above, an actual runner can be built for a model or a series of models. An exmaple is in //executorch/examples/models/llama2/runner, where a C++ runner code is built to run Llama 2, 3, 3.1 and other models using the same architecture.
+With the components above, an actual runner can be built for a model or a series of models. An example is in //executorch/examples/models/llama2/runner, where a C++ runner code is built to run Llama 2, 3, 3.1 and other models using the same architecture.
 
 Usages can also be found in the [torchchat repo](https://github.com/pytorch/torchchat/tree/main/runner).