first pass on litespark-inference

Author: anupras-mohapatra-arm

content/install-guides/litespark-inference.md

@@ -1,6 +1,5 @@
 ---
 title: Litespark-Inference
-draft: true
 
 description: Install Litespark-Inference on Arm or x86 Linux and Apple silicon macOS to run BitNet ternary LLMs on the CPU.
 
@@ -44,18 +43,24 @@ single `pip install` reads your CPU's feature flags and compiles the
 right C++ kernel for it using NEON and SDOT on Arm or AVX-512/VNNI and AVX2+FMA
 on x86. This saves you from needing to pick the right C++ kernel for best performance. 
 
-## What do I need before installing Litespark-Inference?
+You can install Litespark-Inference on Linux and macOS.
 
-- Python 3.10 or newer, you can run `python3 --version` to check your version
+## Before you begin 
+
+Before installing Litespark-Inference, make sure your local machine has the following: 
+
+- Python 3.10 or later
 - A C++ toolchain such as `clang` or `g++`
 - About 5 GB of free disk
 
+To check the Python version on your machine, run `python3 --version`.
+
 The BitNet-2B model is downloaded from Hugging Face on your first run.
 
-It is good practice to install into a clean virtual environment so the
-install does not conflict with anything else on your machine:
+It's a good practice to install into a clean virtual environment so the
+install doesn't conflict with anything else on your machine:
 
-If you don't have Python virtual environment support installed run:
+If you don't have Python virtual environment support installed, run:
 
 ```bash
 sudo apt update
@@ -70,24 +75,28 @@ source .venv/bin/activate
 python -m pip install --upgrade pip wheel setuptools
 ```
 
-Next, follow the section for your platform, Linux or macOS.
-
-## How do I install Litespark-Inference on Arm Linux?
+## Install Litespark-Inference on Arm Linux
 
 The released package builds the correct kernel for your CPU
-automatically. It uses NEON and 
-SDOT on Arm (Graviton 2/3/4, Ampere, Neoverse N1/N2/V1/V2, Raspberry
-Pi 5). 
+automatically. It uses Neon and 
+SDOT on Arm. 
 
-For Ubuntu/Debian distributions, install the C++ toolchain, then the Litespark-Inference Python package:
+Supported CPUs include:
+
+- AWS Graviton 2, 3, or 4
+- Ampere
+- Neoverse N1, N2, V1, or V2
+- Raspberry Pi 5 
+
+For Ubuntu or Debian distributions, install the C++ toolchain, then the Litespark-Inference Python package:
 
 ```bash
 sudo apt-get update
 sudo apt-get install -y build-essential clang ninja-build git python3-pip
 pip install litespark-inference
 ```
 
-For Red Hat, Fedora, or RHEL, install the toolchain with `dnf` instead:
+For Red Hat, Fedora, or RHEL, install the toolchain and package with `dnf` instead:
 
 ```console
 sudo dnf install -y gcc-c++ clang ninja-build git python3-pip
@@ -100,7 +109,7 @@ Confirm the package imports and reports the kernel selected for your CPU:
 python3 -c "import litespark_inference; print(litespark_inference.__version__)"
 ```
 
-The version is printed:
+The output is similar to:
 
 ```output
 1.0.3
@@ -123,15 +132,14 @@ litespark_inference.torchless
   Accelerate: False
 ```
 
-## How do I install Litespark-Inference on Apple silicon macOS?
+## Install Litespark-Inference on Apple silicon macOS
 
-Apple's CPUs have NEON SDOT and Litespark-Inference uses it directly.
+Apple's CPUs have Neon SDOT, and Litespark-Inference uses it directly.
 
-The only extra step versus Linux is installing `libomp`, Apple's
-toolchain does not ship a built-in OpenMP runtime, and Litespark uses
-OpenMP for multi-threading inside the kernel.
+Litespark uses OpenMP for multi-threading inside the kernel. However, Apple's
+toolchain doesn't ship a built-in OpenMP runtime. To address this, you'll need to install `libomp`.
 
-Install Xcode command-line tools, the full Xcode is not required: 
+Install Xcode command-line tools, `libomp`, and the Litespark-Inference Python package. The full set of Xcode tools isn't required: 
 
 ```console
 xcode-select --install
@@ -145,7 +153,7 @@ Verify the install:
 python -m litespark_inference.torchless info
 ```
 
-The expected output includes:
+The output is similar to:
 
 ```output
 litespark_inference.torchless
@@ -156,13 +164,13 @@ litespark_inference.torchless
   Accelerate: True
 ```
 
-If you see `OpenMP : False`, the build did not find Homebrew's `libomp`. The
+If you see `OpenMP : False`, the build didn't find Homebrew's `libomp`. The
 most common cause is that Homebrew is installed under `/opt/homebrew`
-(the Apple Silicon default) but `pip install` ran in an environment that
+(the Apple silicon default), but `pip install` ran in an environment that
 hides it. Re-run `pip install litespark-inference` from a normal
 shell to fix it.
 
-## How do I install from source?
+## Install Litespark-Inference from source
 
 To modify the runtime or kernels, install from source instead of from
 PyPI:
@@ -173,17 +181,20 @@ cd Litespark-Inference
 pip install -e .
 ```
 
-## Sanity-check
-
-On all platforms, the same command can be used. 
+## Verify the Litespark-Inference installation
 
-The first run downloads the model weights (around 4.5 GB into
-`~/.cache/huggingface/hub`). Subsequent runs do not need to download
-again.
+To verify that Litespark-Inference works as expected, run the following command:
 
 ```console
 litespark-inference generate "Hello, world!" --max-tokens 16
 ```
+The same command works on both Linux and macOS. 
+
+{{% notice Note %}}
+The first run downloads the model weights, around 4.5 GB, into
+`~/.cache/huggingface/hub`. Subsequent runs don't need to download
+again.
+{{% /notice %}}
 
 The output is similar to:
 
@@ -200,6 +211,8 @@ Generated 9 tokens in 0.29s (31.58 tok/s)
 Hello! How can I assist you today?
 ```
 
+## Next steps
+
 You are now ready to run BitNet-2B.
 
-Continue with [Accelerate LLM inference on Arm CPUs with Litespark-Inference](/learning-paths/laptops-and-desktops/litespark-inference/) to learn more. 
+To learn more, see the [Accelerate LLM inference on Arm CPUs with Litespark-Inference](/learning-paths/laptops-and-desktops/litespark-inference/) Learning Path.