spelling updates

Author: jasonrandrews

.wordlist.txt

@@ -5156,3 +5156,108 @@ transpile
 tsc
 typescriptlang
 vmlinux
+ATfE
+ATfL
+AlmaLinux
+Asher
+AsyncOpenAI
+Bálint
+CVE
+CircleCI's
+Couchbase
+Couchbase's
+DANDROID
+DKLEIDICV
+DataType
+EdgeXpert
+EleutherAI
+Facter
+GDDR
+GEMMs
+GSM
+Gnuplot
+HD
+HellaSwag
+Hiera
+HwCaps
+InceptionV
+Infineon
+Jett
+KRaft's
+Kiro
+KleidiCV's
+LangChain
+LlamaIndex
+MMLU
+MULTIVERSION
+MVT
+Menuconfig
+MobileNetV
+NHWC
+ORM
+Phoronix
+PointwiseConv
+PyBind
+QL
+RecursiveCharacterTextSplitter
+ResNet
+SDOT
+SMES
+SentenceTransformer
+Silabs
+TPUs
+UDOT
+XDCR
+XNNBatchMatrixMultiply
+XNNConv
+XNNFullyConnected
+XnnpackBackend
+Zhou
+acc
+agentless
+aten
+blockwise
+cbc
+couchbase
+ctrl
+datasheets
+decltype
+docx
+etdump
+etrecord
+facter
+faiss
+fg
+fibonacci
+gemm
+hiera
+ipc
+ivh
+js's
+kiro
+kirocli
+libclang
+libopencv
+llamacpp
+llmcompressor
+minmax
+mse
+multiversion
+phoronix
+pillowfight
+pkl
+pointwise
+pqs
+precisions
+proto
+pypdf
+qb
+qc
+qp
+rebalance
+rustup
+sSf
+tcmalloc
+tlsv
+vLLM's
+webp

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/1-get-started.md

@@ -107,7 +107,7 @@ For the compiler, you can use [Clang](https://www.llvm.org/) version 18 or later
 At the time of writing, macOS ships with `clang` version 17.0.0, which doesn't support SME2. Use a newer version, such as 21.1.4, available through Homebrew.
 {{% /notice%}}
 
-You can check your compiler version using the command: `clang --version` if it's alreay installed. If not, install `clang` using the instructions below, selecting either macOS or Linux/Ubuntu, depending on your setup:
+You can check your compiler version using the command: `clang --version` if it's already installed. If not, install `clang` using the instructions below, selecting either macOS or Linux/Ubuntu, depending on your setup:
 
 {{< tabpane code=true >}}
 

content/learning-paths/cross-platform/multiplying-matrices-with-sme2/2-check-your-environment.md

@@ -275,7 +275,7 @@ defined in `misc.c`.
 
 The `sme2_check` program then displays whether SVE, SME and SME2 are supported
 at line 24. The checking of SVE, SME and SME2 is done differently depending on
-`BAREMETAL`. This platform specific behaviour is abstracted by the
+`BAREMETAL`. This platform specific behavior is abstracted by the
 `display_cpu_features()`:
 - In baremetal mode, our program has access to system registers and can inspect system registers for SME2 support. The program will print the SVE field of the `ID_AA64PFR0_EL1` system register and the SME field of the `ID_AA64PFR1_EL1` system register.
 - In non baremetal mode, on an Apple platform the program needs to use a higher

content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/3_oot_module.md

@@ -223,7 +223,7 @@ The module above receives the size of a 2D array as a string through the `char_d
     ssh root@<your-target-ip>
     ```    
 
-4. Execute the following commads on the target to run the module:
+4. Execute the following commands on the target to run the module:
     ```bash
     insmod /root/mychardrv.ko
     mknod /dev/mychardrv c 42 0

content/learning-paths/embedded-and-microcontrollers/streamline-kernel-module/4_sl_profile_oot.md

@@ -70,7 +70,7 @@ If you are using an AArch32 target, use `arm` instead of `arm64`.
 
     ![Streamline command#center](./images/img04_streamline_cmd.png)
 
-8. In the Capture settings dialog, select Add image, add the absolut path of your kernel module file `mychardrv.ko` and click Save.
+8. In the Capture settings dialog, select Add image, add the absolute path of your kernel module file `mychardrv.ko` and click Save.
 ![Capture settings#center](./images/img05_capture_settings.png)
 
 9. Start the capture and enter a name and location for the capture file. Streamline will start collecting data and the charts will show activity being captured from the target.

content/learning-paths/servers-and-cloud-computing/circleci-on-aws/circleci-runner-installation.md

@@ -20,7 +20,7 @@ This ensures you can install the CircleCI Runner package directly using `apt`.
 curl -s https://packagecloud.io/install/repositories/circleci/runner/script.deb.sh?any=true | sudo bash
 ```
 
-- The `curl` command downloads and executes the repository setup script from CircleCI’s official package server.  
+- The `curl` command downloads and executes the repository setup script from CircleCI's official package server.  
 - It configures the repository on your system, allowing `apt` to fetch and install the CircleCI runner package.  
 - After successful execution, the CircleCI repository will be added under `/etc/apt/sources.list.d/`.
 

content/learning-paths/servers-and-cloud-computing/couchbase-on-gcp/baseline.md

@@ -33,7 +33,7 @@ http://<VM-Public-IP>:8091
 
 ![Finalize configuration](images/cluster-setup-4.png "Finalize configuration")
 
-Our default cluster is now created!  Please retain the passord you created for your "Administrator" account... you'll need that in the next steps. 
+Our default cluster is now created!  Please retain the password you created for your "Administrator" account... you'll need that in the next steps. 
 
 ### Verify Cluster Nodes
 This command checks if your Couchbase server (called a “node”) is running properly. Replace "password" with your specified Couchbase Administrator password.
@@ -87,4 +87,4 @@ Use the admin `username` (default is "Administrator") and `password` you created
 - The **benchmark** bucket will be used for **load testing** and **performance benchmarking**.  
 - Setting the **RAM Quota** ensures Couchbase allocates sufficient memory for **in-memory data operations**, improving overall speed.
 
-You can now proceed to the next section for benchmarking to measure Couchbase’s performance.
+You can now proceed to the next section for benchmarking to measure Couchbase's performance.

content/learning-paths/servers-and-cloud-computing/kafka-azure/baseline.md

@@ -47,7 +47,7 @@ This configuration file sets up a single Kafka server to act as both a controlle
 
 ## Format the storage directory
 
-Format the metadata storage directory using the kafka-storage.sh tool. This initializes KRaft’s internal Raft logs with a unique cluster ID.
+Format the metadata storage directory using the kafka-storage.sh tool. This initializes KRaft's internal Raft logs with a unique cluster ID.
 
 ```console
 bin/kafka-storage.sh format -t $(bin/kafka-storage.sh random-uuid) -c config/server.properties

content/learning-paths/servers-and-cloud-computing/vllm-acceleration/1-overview-and-build.md

@@ -21,7 +21,7 @@ You can use vLLM in two main ways:
 
 vLLM supports Hugging Face Transformer models out-of-the-box and scales seamlessly from single-prompt testing to production batch inference.
 
-## What'll you build
+## What you will build
 
 In this Learning Path, you'll build a CPU-optimized version of vLLM targeting the Arm64 architecture, integrated with oneDNN and the Arm Compute Library (ACL).
 This build enables high-performance LLM inference on Arm servers, leveraging specialized Arm math libraries and kernel optimizations.
@@ -39,7 +39,7 @@ vLLM achieves high performance on Arm servers by combining software and hardware
 
 These optimizations work together to deliver higher throughput and lower latency for LLM inference on Arm servers.
 
-vLLM’s performance on Arm servers is driven by both software optimization and hardware-level acceleration.
+vLLM's performance on Arm servers is driven by both software optimization and hardware-level acceleration.
 Each component of this optimized build contributes to higher throughput and lower latency during inference:
 
 - Optimized kernels: the aarch64 vLLM build uses direct oneDNN with the Arm Compute Library for key operations.
@@ -74,7 +74,7 @@ sudo apt-get install -y libtcmalloc-minimal4
 ```
 
 {{% notice Note %}}
-On aarch64, vLLM’s CPU backend automatically builds with the Arm Compute Library (ACL) through oneDNN.
+On aarch64, vLLM's CPU backend automatically builds with the Arm Compute Library (ACL) through oneDNN.
 This ensures optimized Arm kernels are used for matrix multiplications, layer normalization, and activation functions without additional configuration.
 {{% /notice %}}
 
@@ -160,7 +160,7 @@ python examples/offline_inference/basic/chat.py \
 Explanation:
 --dtype=bfloat16 runs inference in bfloat16 precision. Recent Arm processors support the BFloat16 (BF16) number format in PyTorch. For example, AWS Graviton3 and Graviton3 processors support BFloat16.
 --model specifies a small Hugging Face model for testing (TinyLlama-1.1B-Chat), ideal for functional validation before deploying larger models.
-You should see token streaming in the console, followed by a generated output confirming that vLLM’s inference pipeline is working correctly.
+You should see token streaming in the console, followed by a generated output confirming that vLLM's inference pipeline is working correctly.
 
 ```output
 Generated Outputs:

content/learning-paths/servers-and-cloud-computing/vllm-acceleration/2-quantize-model.md

@@ -8,7 +8,7 @@ layout: learningpathall
 ## Accelerate LLMs with 4-bit quantization
 
 You can accelerate many LLMs on Arm CPUs with 4‑bit quantization. In this section, you’ll quantize the deepseek-ai/DeepSeek-V2-Lite model to 4-bit integer (INT4) weights.
-The quantized model runs efficiently through vLLM’s INT4 inference path, which is accelerated by Arm KleidiAI microkernels.
+The quantized model runs efficiently through vLLM's INT4 inference path, which is accelerated by Arm KleidiAI microkernels.
 
 ## Install quantization tools