docs: fix VLLM_USE_PRECOMPILED env var usage, fix typos/rewording

Signed-off-by: Daniele Trifirò <[email protected]>

docs/source/getting_started/installation.rst

@@ -89,25 +89,25 @@ Build from source
 Python-only build (without compilation)
 ---------------------------------------
 
-If you only need to change Python code, you can build and install vLLM without compilation.
+If you only need to change Python code, you can build and install vLLM without compilation. Using `pip's ``--editable`` flag <https://pip.pypa.io/en/stable/topics/local-project-installs/#editable-installs>`_, changes you make to the code will be reflected when you run vLLM:
 
 .. code-block:: console
 
     $ git clone https://github.com/vllm-project/vllm.git
     $ cd vllm
-    $ VLLM_USE_PRE_COMPILED=1 pip install --editable .
+    $ VLLM_USE_PRECOMPILED=1 pip install --editable .
 
-This will download and the latest available nightly wheel and include the compiled libraries from there. The wheel that is used to retrieve the prebuilt libraries can be set using the ``VLLM_PRECOMPILED_WHEEL_LOCATION`` environment variable, for example, to use the `PyPi wheel <https://pypi.org/project/vllm/#files>`:
+This will download the latest nightly wheel and use the compiled libraries from there in the install.
+
+The wheel that is used to retrieve the prebuilt libraries (e.g. the `PyPi wheel <https://pypi.org/project/vllm/#files>`_) can be set using the ``VLLM_PRECOMPILED_WHEEL_LOCATION`` environment variable:
 
 .. code-block:: console
 
    $ export VLLM_PRECOMPILED_WHEEL_LOCATION=https://files.pythonhosted.org/packages/4a/4c/ee65ba33467a4c0de350ce29fbae39b9d0e7fcd887cc756fa993654d1228/vllm-0.6.3.post1-cp38-abi3-manylinux1_x86_64.whl
-   $ VLLM_USE_PRE_COMPILED=1 pip install --editable .
+   $ VLLM_USE_PRECOMPILED=1 pip install --editable .
 
 You can find more information about vLLM's wheels `above <#install-the-latest-code>`_.
 
-Python code changes, will reflected when you run vLLM thanks to pip's ``--editable`` flag.
-
 .. note::
 
     There is a possibility that your source code may have a different commit ID compared to the latest vLLM wheel, which could potentially lead to unknown errors.
@@ -132,7 +132,7 @@ If you want to modify C++ or CUDA code, you'll need to build vLLM from source. T
     As long as ``which ccache`` command can find the ``ccache`` binary, it will be used automatically by the build system. After the first build, subsequent builds will be much faster.
 
     `sccache <https://github.com/mozilla/sccache>` works similarly to ``ccache``, but has the capability to utilize caching in remote storage environments.
-    The following env vars can be set for to configure the vLLM ``sccache`` remote: ``SCCACHE_BUCKET=vllm-build-sccache SCCACHE_REGION=us-west-2 SCCACHE_S3_NO_CREDENTIALS=1``. We also recommend setting ``SCCACHE_IDLE_TIMEOUT=0``
+    The following environment variables can be set to configure the vLLM ``sccache`` remote: ``SCCACHE_BUCKET=vllm-build-sccache SCCACHE_REGION=us-west-2 SCCACHE_S3_NO_CREDENTIALS=1``. We also recommend setting ``SCCACHE_IDLE_TIMEOUT=0``.
 
 
 Use an existing PyTorch installation
@@ -164,8 +164,8 @@ to be run simultaneously, via the environment variable ``MAX_JOBS``. For example
     $ export MAX_JOBS=6
     $ pip install -e .
 
-This is especially useful when you are building on less powerful machines. For example, when you use WSL it only `assigns 50% of the total memory by default <https://learn.microsoft.com/en-us/windows/wsl/wsl-config#main-wsl-settings>`_, so using ``export MAX_JOBS=1`` can avoid compiling multiple files simultaneously and running out of memory. 
-A side effect is a much slower build process. 
+This is especially useful when you are building on less powerful machines. For example, when you use WSL it only `assigns 50% of the total memory by default <https://learn.microsoft.com/en-us/windows/wsl/wsl-config#main-wsl-settings>`_, so using ``export MAX_JOBS=1`` can avoid compiling multiple files simultaneously and running out of memory.
+A side effect is a much slower build process.
 
 Additionally, if you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.