diff --git a/README.md b/README.md
index 53fa636..5859aef 100644
--- a/README.md
+++ b/README.md
@@ -37,6 +37,7 @@ Each playbook includes prerequisites, step-by-step instructions, troubleshooting
 - [Ollama](nvidia/ollama/)
 - [Use Open WebUI](nvidia/open-webui/)
 - [Use Open Fold](nvidia/protein-folding/)
+- [Fine tune with Pytorch](nvidia/pytorch-fine-tune/)
 - [RAG application in AI Workbench](nvidia/rag-ai-workbench/)
 - [SGLang Inference Server](nvidia/sglang/)
 - [Speculative Decoding](nvidia/speculative-decoding/)
diff --git a/nvidia/dgx-dashboard/README.md b/nvidia/dgx-dashboard/README.md
index 9aa4c54..811ec16 100644
--- a/nvidia/dgx-dashboard/README.md
+++ b/nvidia/dgx-dashboard/README.md
@@ -14,7 +14,7 @@
 
 ## Overview
 
-## Basic Idea
+## Basic idea
 
 The DGX Dashboard is a web application that runs locally on DGX Spark devices, providing a graphical interface for system updates, resource monitoring and an integrated JupyterLab environment. Users can access the dashboard locally from the app launcher or remotely through NVIDIA Sync or SSH tunneling. The dashboard is the easiest way to update system packages and firmware when working remotely.
 
diff --git a/nvidia/jax/README.md b/nvidia/jax/README.md
index 47e4e34..637a825 100644
--- a/nvidia/jax/README.md
+++ b/nvidia/jax/README.md
@@ -92,7 +92,7 @@ newgrp docker
 sudo systemctl restart docker
 ```
 
-## Step 3. Clone the playbook repository
+## Step 2. Clone the playbook repository
 
 ```bash
 git clone https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main
diff --git a/nvidia/pytorch-fine-tune/README.md b/nvidia/pytorch-fine-tune/README.md
new file mode 100644
index 0000000..e921c16
--- /dev/null
+++ b/nvidia/pytorch-fine-tune/README.md
@@ -0,0 +1,113 @@
+# Fine tune with Pytorch
+
+> Use Pytorch to fine-tune models locally
+
+## Table of Contents
+
+- [Overview](#overview)
+- [Instructions](#instructions)
+
+---
+
+## Overview
+
+## Basic Idea
+
+This playbook guides you through setting up and using Pytorch for fine-tuning large language models and vision-language models on NVIDIA Spark devices. NeMo AutoModel provides GPU-accelerated, end-to-end training for Hugging Face models with native PyTorch support, enabling instant fine-tuning without conversion delays. The framework supports distributed training across single GPU to multi-node clusters, with optimized kernels and memory-efficient recipes specifically designed for ARM64 architecture and Blackwell GPU systems.
+
+## What you'll accomplish
+
+You'll establish a complete fine-tuning environment for large language models (1-70B parameters) and vision-language models using NeMo AutoModel on your NVIDIA Spark device. By the end, you'll have a working installation that supports parameter-efficient fine-tuning (PEFT), supervised fine-tuning (SFT), and distributed training capabilities with FP8 precision optimizations, all while maintaining compatibility with the Hugging Face ecosystem.
+
+## What to know before starting
+
+
+
+## Prerequisites
+
+
+
+## Ancillary files
+
+
+
+## Time & risk
+
+**Time estimate:** 
+
+**Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting, distributed training setup complexity increases with multi-node configurations
+
+**Rollback:**
+
+## Instructions
+
+## Step 1. Verify system requirements
+
+Check your NVIDIA Spark device meets the prerequisites for NeMo AutoModel installation. This step runs on the host system to confirm CUDA toolkit availability and Python version compatibility.
+
+```bash
+## Verify CUDA installation
+nvcc --version
+
+## Verify GPU accessibility
+nvidia-smi
+
+## Check available system memory
+free -h
+```
+
+## Step 2. Get the container image
+
+```bash
+docker pull nvcr.io/nvidia/pytorch:25.08-py3
+```
+
+## Step 3. Launch Docker
+
+```bash
+docker run \
+  --gpus all \
+  --ulimit memlock=-1 \
+  -it --ulimit stack=67108864 \
+  --entrypoint /usr/bin/bash \
+  --rm nvcr.io/nvidia/pytorch:25.08-py3
+```
+
+
+
+
+
+## Step 10. Troubleshooting
+
+Common issues and solutions for NeMo AutoModel setup on NVIDIA Spark devices.
+
+| Symptom | Cause | Fix |
+|---------|--------|-----|
+| `nvcc: command not found` | CUDA toolkit not in PATH | Add CUDA toolkit to PATH: `export PATH=/usr/local/cuda/bin:$PATH` |
+| `pip install uv` permission denied | System-level pip restrictions | Use `pip3 install --user uv` and update PATH |
+| GPU not detected in training | CUDA driver/runtime mismatch | Verify driver compatibility: `nvidia-smi` and reinstall CUDA if needed |
+| Out of memory during training | Model too large for available GPU memory | Reduce batch size, enable gradient checkpointing, or use model parallelism |
+| ARM64 package compatibility issues | Package not available for ARM architecture | Use source installation or build from source with ARM64 flags |
+
+## Step 11. Cleanup and rollback
+
+Remove the installation and restore the original environment if needed. These commands safely remove all installed components.
+
+> **Warning:** This will delete all virtual environments and downloaded models. Ensure you have backed up any important training checkpoints.
+
+```bash
+## Remove virtual environment
+rm -rf .venv
+
+## Remove cloned repository
+cd ..
+rm -rf Automodel
+
+## Remove uv (if installed with --user)
+pip3 uninstall uv
+
+## Clear Python cache
+rm -rf ~/.cache/pip
+```
+
+## Step 12. Next steps