Update PyTorch fine-tuning documentation for clarity and accuracy

- Adjusted wording in setup instructions for downloading the PyTorch container.
- Standardized output terminology in fine-tuning section.
- Updated dataset references and model version in training instructions.
- Enhanced clarity in testing instructions for the fine-tuned model.
- Removed draft status and added additional resources in the index file.

Author: madeline-underwood

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

@@ -39,7 +39,7 @@ Pull the latest PyTorch container from NVIDIA's container registry:
 docker pull nvcr.io/nvidia/pytorch:25.11-py3
 ```
 
-This command downloads the November 2025 release of the PyTorch container, which includes PyTorch, CUDA libraries, cuDNN, and other essential tools pre-configured for optimal performance on NVIDIA hardware. The download size is several gigabytes, so this step might take a few minutes depending on your internet connection.
+This command downloads the November 2025 release of the PyTorch container, which includes PyTorch, CUDA libraries, cuDNN, and other essential tools pre-configured for optimal performance on NVIDIA hardware. The download size is several gigabytes, so this step can take a few minutes depending on your internet connection.
 
 ## Launch container instance
 

content/learning-paths/laptops-and-desktops/pytorch-finetuning-on-spark/2-finetuning.md

@@ -36,7 +36,7 @@ The NVIDIA playbook scripts use the Alpaca prompt format, which structures each
 
 **Input** -- optional additional context (left empty for most questions)
 
-**Output** -- the correct answer sourced from official datasheets
+**output** -- the correct answer sourced from official datasheets
 
 Here's an example from the dataset you'll use:
 

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

@@ -23,7 +23,7 @@ from transformers import AutoModelForCausalLM, AutoTokenizer
 
 The `DATASET_PROMPT_TEMPLATE` defines the instruction-following format for training data with three fields: instruction, input, and response. Each training example is formatted using this template so the model learns to recognize the pattern and produce structured answers.
 
-The `get_dataset()` function loads the dataset from (Hugging Face by default) and formats each example using the template, appending the EOS (End of String) token. You'll pass in the Raspberry Pi dataset from a local JSONL file instead.
+The `get_dataset()` function loads the dataset from Hugging Face by default and formats each example using the template, appending the EOS (End of Sequence) token. You'll pass in the Raspberry Pi dataset from a local JSONL file instead.
 
 ```python
 # Define prompt templates
@@ -154,13 +154,13 @@ cp /workspace/raspberry_pi_qa.jsonl .
 
 ## Run the fine-tuning
 
-With the dataset patch applied, you're ready to run the fine-tuning. The command below trains the Llama 3.1 8B model using full fine-tuning on the Raspberry Pi dataset:
+With the dataset in place, you're ready to run the fine-tuning. The command below trains the Llama 3.2 3B model using full fine-tuning on the Raspberry Pi dataset:
 
 ```bash
 python Llama3_3B_full_finetuning.py \
 --model_name "meta-llama/Llama-3.2-3B-Instruct" \
 --dataset "json" \
---dataset_files="/workspace/projects/pytorch-finetuning/raspberry_pi_qa.jsonl" \
+--dataset_files="raspberry_pi_qa.jsonl" \
 --dataset_size 300 \
 --output_dir "/workspace/models/Llama-3.2-3B-FineTuned"
 ```

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

@@ -6,6 +6,8 @@ weight: 5
 layout: learningpathall
 ---
 
+## Overview
+
 Now that you've fine-tuned your model on Raspberry Pi datasheet content, it's time to compare its behavior against the original. You'll serve both versions using vLLM, a high-performance inference server optimized for large language models, and observe how fine-tuning on domain-specific data changes the model's factual accuracy.
 
 ## Download vLLM container
@@ -64,8 +66,7 @@ Wait for the server to fully load the model and display the message indicating i
 
 ### Test prompt
 
-
-From a new terminal window (outside the container), send a Raspberry Pi hardware question to the model using the Alpaca instruction format. For this example, use a question about the memory size:
+From a new terminal window (outside the container), use `curl` to send an HTTP request to the model server. The request contains a Raspberry Pi hardware question formatted using the Alpaca instruction template. For this example, ask about the RP2350 memory size:
 
 ```bash
 curl http://localhost:8000/v1/completions \
@@ -132,7 +133,7 @@ The only change from the previous command is the `--model` parameter, which now
 
 ### Test prompt
 
-Send the same Raspberry Pi question to your fine-tuned model:
+Send the same Raspberry Pi question to your fine-tuned model using the same `curl` command format:
 
 ```bash
 curl http://localhost:8000/v1/completions \

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

@@ -1,18 +1,14 @@
 ---
 title: Fine-tune PyTorch models on DGX Spark
 
-draft: true
-cascade:
-    draft: true
-
 minutes_to_complete: 60
 
 who_is_this_for: This is an advanced topic for AI developers and ML engineers who want to fine-tune large language models using PyTorch and Hugging Face on the NVIDIA DGX Spark platform.
 
 learning_objectives: 
     - Understand how fine-tuning teaches a model domain-specific knowledge
     - Prepare a custom JSONL dataset for supervised fine-tuning
-    - Fine-tune Llama 3.1 8B on Raspberry Pi datasheet content using PyTorch and Hugging Face
+    - Fine-tune Llama 3.2 3B on Raspberry Pi datasheet content using PyTorch and Hugging Face
     - Compare base and fine-tuned model responses to verify factual accuracy improvements
 
 prerequisites:
@@ -55,6 +51,10 @@ further_reading:
         title: PyTorch Training Documentation
         link: https://pytorch.org/tutorials/beginner/introyt/trainingyt.html
         type: documentation
+    - resource:
+        title: Build a serverless LLM inference application with AWS Lambda and Arm processors
+        link: /learning-paths/servers-and-cloud-computing/llama-cpu/
+        type: website
 
 ### FIXED, DO NOT MODIFY
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