Update model-training-gym LP

Author: annietllnd

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/1-introduction.md

@@ -16,7 +16,7 @@ Arm enables neural graphics through the **Neural Graphics Development Kit**: a s
 
 At its core are the ML Extensions for Vulkan, which bring native ML inference into the GPU pipeline using structured compute graphs. These extensions (`VK_ARM_tensors` and `VK_ARM_data_graph`) allow real-time upscaling and similar effects to run efficiently alongside rendering tasks.
 
-The neural graphics models can be developed using well-known ML frameworks like **PyTorch**, and exported to deployment using Arm's hardware-aware pipeline. The workflow converts .pt model weights to Vulkan Graph Format (.vgf) via the TOSA intermediate representation, making it possible to do tailored model development for you game use-case. This Learning Path focuses on **Neural Super Sampling (NSS)** as the use case for training, evaluating, and deploying neural models using a toolkit called the **Neural Graphics Model Gym**.
+The neural graphics models can be developed using well-known ML frameworks like **PyTorch**, and exported to deployment using Arm's hardware-aware pipeline. The workflow converts .pt model weights to `.vgf` via the TOSA intermediate representation, making it possible to do tailored model development for you game use-case. This Learning Path focuses on **Neural Super Sampling (NSS)** as the use case for training, evaluating, and deploying neural models using a toolkit called the **Neural Graphics Model Gym**.
 
 Starting in 2026, Arm GPUs will feature dedicated neural accelerators, optimized for low-latency inference in graphics workloads. To help developers get started early, Arm provides the ML Emulation Layers for Vulkan that simulate future hardware behavior, so you can build and test models now.
 

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/3-model-training.md

@@ -17,7 +17,7 @@ The NSS model is available in two formats:
 | Model format | File extension | Used for                                                                 |
 |--------------|----------------|--------------------------------------------------------------------------|
 | PyTorch      | .pt            | training, fine-tuning, or evaluation in or scripts using the Model Gym  |
-| VGF          | .vgf           | for deployment using Vulkan ML on Arm-based hardware or emulation layers |
+| VGF          | .vgf           | for deployment using ML Extensions for Vulkan on Arm-based hardware or emulation layers |
 
 Both formats are available in the [NSS repository on Hugging Face](https://huggingface.co/Arm/neural-super-sampling). You'll also be able to explore config files, model metadata, usage details and detailed documentation on the use-case.
 

content/learning-paths/mobile-graphics-and-gaming/model-training-gym/4-model-explorer.md

@@ -10,7 +10,7 @@ layout: learningpathall
 
 TODO: verify .vgf flavor runs smoothly
 
-Model Explorer is a visualization tool for inspecting neural network structures and execution graphs. Arm provides a VGF adapter for Model Explorer, allowing you to visualize Vulkan Graph Format (`.vgf`) models created from your training and export pipeline.
+Model Explorer is a visualization tool for inspecting neural network structures and execution graphs. Arm provides a VGF adapter for Model Explorer, allowing you to visualize `.vgf` models created from your training and export pipeline.
 
 This lets you inspect model architecture, tensor shapes, and graph connectivity before deployment. This can be a powerful way to debug and understand your exported neural graphics models.