Merge pull request #2159 from jasonrandrews/review

Updates to Arcee Graviton4 Learning Path

Author: jasonrandrews

content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/01_launching_a_graviton4_instance.md

@@ -20,7 +20,7 @@ If you're new to EC2, check out the Learning Path [Getting Started with AWS](/le
 
 To deploy the Arcee AFM-4.5B model, you need an EC2 instance running on Arm-based Graviton4 hardware.
 
-To do this, start by signing in to the [AWS Management Console](https://console.aws.amazon.com), then navigate to the **EC2** service.
+To do this, start by signing in to the [AWS Management Console](https://console.aws.amazon.com), change to your preferred region, and navigate to the **EC2** service.
 
 From there, you can create an SSH key pair that allows you to connect to your instance securely.
 
@@ -39,7 +39,7 @@ mkdir -p ~/.ssh
 mv arcee-graviton4-key.pem ~/.ssh/
 chmod 400 ~/.ssh/arcee-graviton4-key.pem
 ```
-internet
+
 ## Launch and configure the EC2 instance
 
 In the left sidebar of the EC2 dashboard, select **Instances**, and then **Launch instances**.

content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/03_building_llama_cpp.md

@@ -87,4 +87,4 @@ After compilation, you'll find several key command-line tools in the `bin` direc
 
 You can find more tools and usage details in the llama.cpp [GitHub repository](https://github.com/ggml-org/llama.cpp/tree/master/tools).
 
-These binaries are specifically optimized for Arm64 architecture and will provide excellent performance on your Graviton4 instance.
+These binaries are specifically optimized for the Arm architecture and will provide excellent performance on your Graviton4 instance.

content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md

@@ -66,7 +66,7 @@ This command creates a 4-bit quantized version of the model:
 
 ## Arm optimization 
 
-Arm has contributed optimized kernels for Q4_0 that use Neoverse v2 instruction sets. These low-level routines accelerate math operations, delivering strong performance on Graviton4.
+Arm has contributed optimized kernels for Q4_0 that use Neoverse V2 instruction sets. These low-level routines accelerate math operations, delivering strong performance on Graviton4.
 
 These instruction sets allow Llama.cpp to run quantized operations significantly faster than generic implementations, making Arm processors a competitive choice for inference workloads.
 
@@ -85,7 +85,7 @@ This command creates an 8-bit quantized version of the model:
 
 ## Arm optimization
 
-Similar to Q4_0, Arm has contributed optimized kernels for Q8_0 quantization that take advantage of Neoverse v2 instruction sets. These optimizations provide excellent performance for 8-bit operations while maintaining higher accuracy compared to 4-bit quantization.
+Similar to Q4_0, Arm has contributed optimized kernels for Q8_0 quantization that take advantage of Neoverse V2 instruction sets. These optimizations provide excellent performance for 8-bit operations while maintaining higher accuracy compared to 4-bit quantization.
 
 ## Model files ready for inference
 

content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/08_conclusion.md

@@ -36,7 +36,7 @@ The benchmarking results demonstrate the power of quantization and Arm-based com
 
 ## The AWS Graviton4 advantage
 
-AWS Graviton4 processors, built on the Arm Neoverse-V2 architecture, provide:
+AWS Graviton4 processors, built on the Arm Neoverse V2 architecture, provide:
 
 - Superior performance per watt compared to x86 alternatives
 - Cost savings of 20–40% for compute-intensive workloads

content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md

@@ -18,7 +18,6 @@ learning_objectives:
 
 prerequisites:
     - An [AWS account](https://aws.amazon.com/) with permission to launch Graviton4 (`c8g.4xlarge` or larger) instances
-    - At least 128 GB of available storage
     - Basic familiarity with Linux and SSH
 
 author: Julien Simon