pre-publish review

.wordlist.txt

@@ -6205,3 +6205,20 @@ Antigravity
 Moby
 antigravity
 callout
+Cypher
+Marek
+Mayne
+Memgraph
+Memgraph's
+Micha
+NetworkX
+PageRank
+RedHatAI
+Sabika
+TPOT
+TTFT
+Tasneem
+cypher
+memgraph
+mgconsole
+owski

content/install-guides/memgraph-on-arm.md

@@ -32,7 +32,7 @@ In this install guide, you'll learn two installation paths:
 - Docker: the quickest way to try Memgraph, and the portable option for macOS, Windows, and any Linux distribution.
 - Native Linux packages: a good choice when Docker is unavailable or not preferred, or when you want to benchmark Memgraph directly on the host.
 
-At the end of each path, you'll run a few Cypher queries with `mgconsole`, then optionally add [MAGE](https://memgraph.com/docs/advanced-algorithms), Memgraph’s graph-algorithm and query-module extension library.
+At the end of each path, you'll run a few Cypher queries with `mgconsole`, then optionally add [MAGE](https://memgraph.com/docs/advanced-algorithms), Memgraph's graph-algorithm and query-module extension library.
 
 ## Before you begin
 
@@ -97,7 +97,7 @@ The following are Docker images that you can choose depending on the Memgraph fe
 | `memgraph/mgconsole` | Standalone CLI client. |
 | `memgraph/lab` | Memgraph Lab web UI. |
 
-Start with `memgraph/memgraph` to get the Memgraph database. If you later want PageRank, community detection, node embeddings, NetworkX integration, or other advanced query modules, see the [MAGE section](#install-the-mage-graph-algorithm-library).
+Start with `memgraph/memgraph` to get the Memgraph database. If you later want PageRank, community detection, node embeddings, NetworkX integration, or other advanced query modules, see the [MAGE section](#optional-install-the-mage-graph-algorithm-library).
 
 ### Connect with mgconsole inside the container
 
@@ -117,7 +117,7 @@ Quit the shell by typing Ctrl-D(eof) or :quit
 memgraph>
 ```
 
-For example `mgconsole` queries, see the [example queries](#how-do-i-run-example-cypher-queries) section.
+For example `mgconsole` queries, see the [example queries](#run-example-cypher-queries-through-mgconsole) section.
 
 ## Install Memgraph natively on Linux
 
@@ -237,7 +237,7 @@ This setting is applied on the Linux host, so it is relevant for both the native
 
 ## (Optional) Install mgconsole separately
 
-`mgconsole` is Memgraph’s command-line client for executing Cypher queries. It is already included in the Memgraph Linux packages, and in the `memgraph/memgraph:3.10.1` and `memgraph/memgraph-mage:3.10.1` Docker images. You need a separate install only if you want to run it from a different machine.
+`mgconsole` is Memgraph's command-line client for executing Cypher queries. It is already included in the Memgraph Linux packages, and in the `memgraph/memgraph:3.10.1` and `memgraph/memgraph-mage:3.10.1` Docker images. You need a separate install only if you want to run it from a different machine.
 
 To install it standalone, download the binary for your platform from the [Memgraph Download Hub](https://memgraph.com/download#individual), or pull the Docker image.
 
@@ -334,7 +334,7 @@ mgconsole --host localhost --port 7687
 
 ### Pattern-matching example
 
-Memgraph’s strength is pattern matching. Try a slightly richer graph:
+Memgraph's strength is pattern matching. Try a slightly richer graph:
 
 ```bash { target="ubuntu:latest" }
 mgconsole --host localhost --port 7687 <<'EOF'

content/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/1-overview-and-setup.md

@@ -12,7 +12,7 @@ layout: learningpathall
 
 ## Understand the models you'll use
 
-You'll use LLama 3.1 8B and Whisper large V3 in this Learning Path.
+You'll use Llama 3.1 8B and Whisper large V3 in this Learning Path.
 
 Llama 3.1 8B is an open-weight, text-only LLM with 8 billion parameters that can understand and generate text. You can view the model card at https://huggingface.co/meta-llama/Llama-3.1-8B.
 
@@ -68,7 +68,7 @@ pip install https://github.com/vllm-project/vllm/releases/download/v${VLLM_VERSI
 If you want to build vLLM from source, follow the instructions in the [Build and Run vLLM on Arm Servers Learning Path](/learning-paths/servers-and-cloud-computing/vllm/vllm-setup/).
 
 
-### Set up access to LLama3.1-8B models
+### Set up access to Llama3.1-8B models
 
 To access the Llama models hosted by Hugging Face, install the Hugging Face CLI and authenticate with your access token. Install the CLI:
 ```bash

content/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/3-run-inference.md

@@ -6,7 +6,7 @@ weight: 4
 layout: learningpathall
 ---
 
-## Run inference on LLama3.1-8B
+## Run inference on Llama3.1-8B
 
 vLLM serves an OpenAI-compatible API that you'll use to run inference on Llama3.1-8B and confirm that the local environment is set up correctly.
 
@@ -260,6 +260,6 @@ The quantized Whisper model completes the same transcription in roughly a quarte
 
 ## What you've accomplished and what's next
 
-You've now installed vLLM and run inference on your LLama and Whisper models. You've seen speedups in task completion between quantized and non-quantized models.
+You've now installed vLLM and run inference on your Llama and Whisper models. You've seen speedups in task completion between quantized and non-quantized models.
 
 Next, you'll benchmark the Llama models and compare their performance.

content/learning-paths/servers-and-cloud-computing/vllm-benchmark-quantisation/4-benchmarking.md

@@ -202,6 +202,6 @@ Run without `--limit` for a statistically representative accuracy comparison.
 
 ## What you've accomplished
 
-You've now successfully benchmarked quantized and non-quantized LLama 3.1 8B models for throughput and accuracy. The results suggest that quantization improves a model's throughput but can reduce its accuracy.
+You've now successfully benchmarked quantized and non-quantized Llama 3.1 8B models for throughput and accuracy. The results suggest that quantization improves a model's throughput but can reduce its accuracy.
 
 Now that your environment is set up for running inference, benchmarking, and quantizing different models, you can experiment further. Try benchmarking accuracy with different tasks, different quantization techniques, or different models. Your results will allow you to balance accuracy and performance when making decisions about model deployment.

themes/arm-design-system-hugo-theme/layouts/stats/list.html

@@ -175,10 +175,6 @@ <h2>Authors</h2>
 }
 </script>
 
-<script>
-/* Content Growth - using static image from roadmap repo */
-</script>
-
 <script>
 /* Category breakdown - horizontal bar */
 var catLabels = ["Servers And Cloud Computing", "Laptops And Desktops", "Embedded And Microcontrollers", "Mobile Graphics And Gaming", "Automotive"];