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**](ht
 
 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 the model 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**](https://github.com/arm/neural-graphics-model-gym). To learn more about NSS, you can check out the [resources on Hugging Face](https://huggingface.co/Arm/neural-super-sampling). Additonally, Arm has developed a set of Vulkan Samples to get started. Specifically, `.vgf` format is introduced in the `postprocessing_with_vgf` one. The Vulkan Samples and over-all developer resources for neural graphics is covered in the [introductory Learning Path](/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample).
+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 the model 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**](https://github.com/arm/neural-graphics-model-gym). To learn more about NSS, you can check out the [resources on Hugging Face](https://huggingface.co/Arm/neural-super-sampling). Additonally, Arm has developed a set of Vulkan Samples to get started. Specifically, `.vgf` format is introduced in the `postprocessing_with_vgf` one. The Vulkan Samples and over-all developer resources for neural graphics is covered in the [introductory Learning Path](/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample).
 
 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/2-devenv.md

@@ -6,7 +6,7 @@ weight: 3
 layout: learningpathall
 ---
 
-In this section, you will install a few dependencies into your Ubuntu environment. You'll need a working Python 3.10+ environment with some ML and system dependencies. Make sure Python is installed by verifying the version is printed:
+In this section, you will install a few dependencies into your Ubuntu environment. You'll need a working Python 3.10+ environment with some ML and system dependencies. Make sure Python is installed by verifying that the version is >3.10:
 
 ```bash
 python3 --version

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

@@ -22,15 +22,7 @@ The VGF adapter extends Model Explorer to support `.vgf` files exported from the
 pip install vgf-adapter-model-explorer
 ```
 
-Or install the prebuilt wheel from GitHub:
-
-```bash
-PYTHON_VERSION_TAG=311
-gh release download \
-  --repo arm/vgf-adapter-model-explorer \
-  --pattern "*py${PYTHON_VERSION_TAG}*.whl"
-pip install *py${PYTHON_VERSION_TAG}*.whl
-```
+The source code is available on [GitHub](https://github.com/arm/vgf-adapter-model-explorer).
 
 ### Install Model Explorer
 

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

@@ -9,7 +9,7 @@ learning_objectives:
     - Understand the principles of neural graphics and how it’s applied to game performance
     - Learn how to fine-tune and evaluate a neural network for Neural Super Sampling (NSS)
     - Use the Model Gym Python API and CLI to configure and train neural graphics models
-    - Visualize and inspect `.vgf` models using the Model Explorer tool
+    - Visualize and inspect .vgf models using the Model Explorer tool
 
 prerequisites:
     - Basic understanding of PyTorch and machine learning concepts
@@ -35,17 +35,17 @@ further_reading:
         link: https://github.com/arm/neural-graphics-model-gym
         type: code
     - resource:
-        title: How Arm Neural Super Sampling works
-        link: https://community.arm.com/arm-community-blogs/b/mobile-graphics-and-gaming-blog/posts/how-arm-neural-super-sampling-works
-        type: blog
+        title: NSS Fine-Tuning Guide
+        link: https://developer.arm.com/documentation/111141/latest
+        type: documentation
     - resource:
         title: Neural Graphics Development Kit
         link: https://developer.arm.com/mobile-graphics-and-gaming/neural-graphics
         type: website
     - resource:
-        title: NSS Use Case Guide
-        link: https://developer.arm.com/documentation/111009/latest/
-        type: documentation
+        title: NSS on HuggingFace
+        link: https://huggingface.co/Arm/neural-super-sampling
+        type: website
 
 
 ### FIXED, DO NOT MODIFY