Add overview sections to setup and PyTorch fine-tuning guides; update notice in testing guide

Author: madeline-underwood

content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/1-setup.md

@@ -6,6 +6,8 @@ weight: 2
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+## Overview
+
 The NVIDIA DGX Spark pairs an Arm-based Grace CPU with a Blackwell GPU in a compact desktop form factor. The GPU handles the compute-intensive training passes while the Grace CPU manages data preprocessing and orchestration, making the system well suited for fine-tuning large language models locally without sending data to the cloud.
 
 To get started, you'll configure Docker, pull a pre-built PyTorch container, and install the libraries you need for fine-tuning.
@@ -81,7 +83,7 @@ These packages serve specific purposes:
 - `trl` (Transformer Reinforcement Learning) includes training utilities and recipes for language models
 - `bitsandbytes` enables 4-bit and 8-bit quantization for memory-efficient training
 
-The installation typically takes a few minutes as pip downloads and installs each package along with their dependencies.
+The installation can take a few minutes as pip downloads and installs each package along with their dependencies.
 
 ## Authenticate with Hugging Face
 

content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/3-pytorch.md

@@ -6,6 +6,8 @@ weight: 4
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+## Overview
+
 Now that you understand how fine-tuning works, it's time to look at the actual code. In this section, you'll walk through the key parts of the `Llama3_3B_full_finetuning.py` script and run it with the Raspberry Pi dataset to produce your own fine-tuned Llama model.
 
 

content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/4-testing.md

@@ -110,7 +110,7 @@ The base model confidently reports the RP2350 has "256MB of memory," which is of
 
 Now test your fine-tuned model to see how training on Raspberry Pi datasheet content improved its factual accuracy. Stop the current vLLM server (press Ctrl+C in the container terminal) before launching the fine-tuned model.
 
-{{% notice Dependency Conflict %}}
+{{% notice Note %}}
 As of this writing, vLLM does not support version 5 of the `transformers` library that was used when fine-tuning the model, so you need to patch its `tokenizer_config.json`. Run the following command to update the `tokenizer_class` to `PreTrainedTokenizerFast`, which is compatible with the older `transformers` version bundled in the vLLM container:
 
 ```bash