diff --git a/nvidia/llama-factory/README.md b/nvidia/llama-factory/README.md
index 07ebac6..37867a6 100644
--- a/nvidia/llama-factory/README.md
+++ b/nvidia/llama-factory/README.md
@@ -5,20 +5,36 @@
 ## Table of Contents
 
 - [Overview](#overview)
+  - [What you'll accomplish](#what-youll-accomplish)
+  - [What to know before starting](#what-to-know-before-starting)
+  - [Prerequisites](#prerequisites)
+  - [Ancillary files](#ancillary-files)
+  - [Time & risk](#time-risk)
 - [Instructions](#instructions)
+  - [Step 1. Verify system prerequisites](#step-1-verify-system-prerequisites)
+  - [Step 2. Launch PyTorch container with GPU support](#step-2-launch-pytorch-container-with-gpu-support)
+  - [Step 3. Clone LLaMA Factory repository](#step-3-clone-llama-factory-repository)
   - [Step 4. Install LLaMA Factory with dependencies](#step-4-install-llama-factory-with-dependencies)
+  - [Step 5. Configure PyTorch for CUDA 12.9 (if needed)](#step-5-configure-pytorch-for-cuda-129-if-needed)
+  - [Step 6. Prepare training configuration](#step-6-prepare-training-configuration)
+  - [Step 7. Launch fine-tuning training](#step-7-launch-fine-tuning-training)
+  - [Step 8. Validate training completion](#step-8-validate-training-completion)
+  - [Step 9. Test inference with fine-tuned model](#step-9-test-inference-with-fine-tuned-model)
+  - [Step 10. Troubleshooting](#step-10-troubleshooting)
+  - [Step 11. Cleanup and rollback](#step-11-cleanup-and-rollback)
+  - [Step 12. Next steps](#step-12-next-steps)
 
 ---
 
 ## Overview
 
-## What you'll accomplish
+### What you'll accomplish
 
 You'll set up LLaMA Factory on NVIDIA Spark with Blackwell architecture to fine-tune large 
 language models using LoRA, QLoRA, and full fine-tuning methods. This enables efficient 
 model adaptation for specialized domains while leveraging hardware-specific optimizations.
 
-## What to know before starting
+### What to know before starting
 
 - Basic Python knowledge for editing config files and troubleshooting
 - Command line usage for running shell commands and managing environments  
@@ -28,7 +44,7 @@ model adaptation for specialized domains while leveraging hardware-specific opti
 - Dataset preparation: formatting text data into JSON structure for instruction tuning
 - Resource management: adjusting batch size and memory settings for GPU constraints
 
-## Prerequisites
+### Prerequisites
 
 - NVIDIA Spark device with Blackwell architecture
 
@@ -44,7 +60,7 @@ model adaptation for specialized domains while leveraging hardware-specific opti
 
 - Internet connection for downloading models from Hugging Face Hub
 
-## Ancillary files
+### Ancillary files
 
 - Official LLaMA Factory repository: https://github.com/hiyouga/LLaMA-Factory
 
@@ -54,7 +70,7 @@ model adaptation for specialized domains while leveraging hardware-specific opti
 
 - Documentation: https://llamafactory.readthedocs.io/en/latest/getting_started/data_preparation.html
 
-## Time & risk
+### Time & risk
 
 **Duration:** 30-60 minutes for initial setup, 1-7 hours for training depending on model size
 and dataset.
@@ -67,7 +83,7 @@ saved locally and can be deleted to reclaim storage space.
 
 ## Instructions
 
-## Step 1. Verify system prerequisites
+### Step 1. Verify system prerequisites
 
 Check that your NVIDIA Spark system has the required components installed and accessible.
 
@@ -79,7 +95,7 @@ python --version
 git --version
 ```
 
-## Step 2. Launch PyTorch container with GPU support
+### Step 2. Launch PyTorch container with GPU support
 
 Start the NVIDIA PyTorch container with GPU access and mount your workspace directory.
 > **Note:** This NVIDIA PyTorch container supports CUDA 13
@@ -88,7 +104,7 @@ Start the NVIDIA PyTorch container with GPU access and mount your workspace dire
 docker run --gpus all --ipc=host --ulimit memlock=-1 -it --ulimit stack=67108864 --rm -v "$PWD":/workspace nvcr.io/nvidia/pytorch:25.08-py3 bash
 ```
 
-## Step 3. Clone LLaMA Factory repository
+### Step 3. Clone LLaMA Factory repository
 
 Download the LLaMA Factory source code from the official repository.
 
@@ -105,7 +121,9 @@ Install the package in editable mode with metrics support for training evaluatio
 pip install -e ".[metrics]"
 ```
 
-## Step 5. Configure PyTorch for CUDA 12.9 (skip if using Docker container from Step 2)
+### Step 5. Configure PyTorch for CUDA 12.9 (if needed)
+
+#### If using standalone Python (skip if using Docker container)
 
 In a python virtual environment, uninstall existing PyTorch and reinstall with CUDA 12.9 support for ARM64 architecture.
 
@@ -114,7 +132,7 @@ pip uninstall torch torchvision torchaudio
 pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu129
 ```
 
-*If using Docker container*
+#### If using Docker container
 
 PyTorch is pre-installed with CUDA support. Verify installation:
 
@@ -122,7 +140,7 @@ PyTorch is pre-installed with CUDA support. Verify installation:
 python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"
 ```
 
-## Step 6. Prepare training configuration
+### Step 6. Prepare training configuration
 
 Examine the provided LoRA fine-tuning configuration for Llama-3.
 
@@ -130,7 +148,7 @@ Examine the provided LoRA fine-tuning configuration for Llama-3.
 cat examples/train_lora/llama3_lora_sft.yaml
 ```
 
-## Step 7. Launch fine-tuning training
+### Step 7. Launch fine-tuning training
 
 > **Note:** Login to your hugging face hub to download the model if the model is gated
 Execute the training process using the pre-configured LoRA setup.
@@ -152,7 +170,7 @@ Example output:
 Figure saved at: saves/llama3-8b/lora/sft/training_loss.png
 ```
 
-## Step 8. Validate training completion
+### Step 8. Validate training completion
 
 Verify that training completed successfully and checkpoints were saved.
 
@@ -168,7 +186,7 @@ Expected output should show:
 - Training metrics showing decreasing loss values
 - Training loss plot saved as PNG file
 
-## Step 9. Test inference with fine-tuned model
+### Step 9. Test inference with fine-tuned model
 
 Run a simple inference test to verify the fine-tuned model loads correctly.
 
@@ -176,7 +194,7 @@ Run a simple inference test to verify the fine-tuned model loads correctly.
 llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
 ```
 
-## Step 10. Troubleshooting
+### Step 10. Troubleshooting
 
 | Symptom | Cause | Fix |
 |---------|--------|-----|
@@ -184,7 +202,7 @@ llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
 | Model download fails or is slow | Network connectivity or Hugging Face Hub issues | Check internet connection, try using `HF_HUB_OFFLINE=1` for cached models |
 | Training loss not decreasing | Learning rate too high/low or insufficient data | Adjust `learning_rate` parameter or check dataset quality |
 
-## Step 11. Cleanup and rollback
+### Step 11. Cleanup and rollback
 
 > **Warning:** This will delete all training progress and checkpoints.
 
@@ -202,7 +220,7 @@ exit  # Exit container
 docker container prune -f
 ```
 
-## Step 12. Next steps
+### Step 12. Next steps
 
 Test your fine-tuned model with custom prompts:
 
diff --git a/nvidia/multi-modal-inference/README.md b/nvidia/multi-modal-inference/README.md
index eba93aa..8b33be9 100644
--- a/nvidia/multi-modal-inference/README.md
+++ b/nvidia/multi-modal-inference/README.md
@@ -35,14 +35,14 @@ FP8, FP4).
 
 ## Prerequisites
 
-- NVIDIA Spark device with Blackwell GPU architecture
-- Docker installed and accessible to current user
-- NVIDIA Container Runtime configured
-- Hugging Face account with valid token
-- At least 48GB VRAM available for FP16 Flux.1 Schnell operations
-- Verify GPU access: `nvidia-smi`
-- Check Docker GPU integration: `docker run --rm --gpus all nvidia/cuda:12.0-base-ubuntu20.04 nvidia-smi`
-- Confirm HF token access with permissions to FLUX repos: `echo $HF_TOKEN`, Sign in to your huggingface account You can create the token from create your token here (make sure you provide permissions to the token): https://huggingface.co/settings/tokens , Note the permissions to be checked and the repos: black-forest-labs/FLUX.1-dev and black-forest-labs/FLUX.1-dev-onnx (search for these repos when creating the user token) to be added.
+- [ ] NVIDIA Spark device with Blackwell GPU architecture
+- [ ] Docker installed and accessible to current user
+- [ ] NVIDIA Container Runtime configured
+- [ ] Hugging Face account with valid token
+- [ ] At least 48GB VRAM available for FP16 Flux.1 Schnell operations
+- [ ] Verify GPU access: `nvidia-smi`
+- [ ] Check Docker GPU integration: `docker run --rm --gpus all nvidia/cuda:12.0-base-ubuntu20.04 nvidia-smi`
+- [ ] Confirm HF token access with permissions to FLUX repos: `echo $HF_TOKEN`, Sign in to your huggingface account You can create the token from create your token here (make sure you provide permissions to the token): https://huggingface.co/settings/tokens , Note the permissions to be checked and the repos: black-forest-labs/FLUX.1-dev and black-forest-labs/FLUX.1-dev-onnx (search for these repos when creating the user token) to be added.
 
 ## Ancillary files
 
diff --git a/nvidia/nemo-fine-tune/README.md b/nvidia/nemo-fine-tune/README.md
index 641c571..a6a8b2c 100644
--- a/nvidia/nemo-fine-tune/README.md
+++ b/nvidia/nemo-fine-tune/README.md
@@ -35,22 +35,22 @@ You'll establish a complete fine-tuning environment for large language models (1
 
 ## Prerequisites
 
-- NVIDIA Spark device with Blackwell architecture GPU access
-- CUDA toolkit 12.0+ installed and configured
+- [ ] NVIDIA Spark device with Blackwell architecture GPU access
+- [ ] CUDA toolkit 12.0+ installed and configured
   ```bash
   nvcc --version
   ```
-- Python 3.10+ environment available
+- [ ] Python 3.10+ environment available
   ```bash
   python3 --version
   ```
-- Minimum 32GB system RAM for efficient model loading and training
-- Active internet connection for downloading models and packages
-- Git installed for repository cloning
+- [ ] Minimum 32GB system RAM for efficient model loading and training
+- [ ] Active internet connection for downloading models and packages
+- [ ] Git installed for repository cloning
   ```bash
   git --version
   ```
-- SSH access to your NVIDIA Spark device configured
+- [ ] SSH access to your NVIDIA Spark device configured
 
 ## Ancillary files
 
diff --git a/nvidia/nim-llm/README.md b/nvidia/nim-llm/README.md
index b482c8b..9093709 100644
--- a/nvidia/nim-llm/README.md
+++ b/nvidia/nim-llm/README.md
@@ -1,6 +1,6 @@
 # Use a NIM on Spark
 
-> Run an LLM NIM on Spark
+> Run a NIM on Spark
 
 ## Table of Contents
 
@@ -40,19 +40,19 @@ completions.
 
 ### Prerequisites
 
-- DGX Spark device with NVIDIA drivers installed
+- [ ] DGX Spark device with NVIDIA drivers installed
   ```bash
   nvidia-smi
   ```
-- Docker with NVIDIA Container Toolkit configured, instructions here: https://******.nvidia.com/dgx-docs/review/621/dgx-spark/latest/nvidia-container-runtime-for-docker.html
+- [ ] Docker with NVIDIA Container Toolkit configured, instructions here: https://******.nvidia.com/dgx-docs/review/621/dgx-spark/latest/nvidia-container-runtime-for-docker.html
   ```bash
   docker run -it --gpus=all nvcr.io/nvidia/cuda:13.0.1-devel-ubuntu24.04 nvidia-smi
   ```
-- NGC account with API key from https://ngc.nvidia.com/setup/api-key
+- [ ] NGC account with API key from https://ngc.nvidia.com/setup/api-key
   ```bash
   echo $NGC_API_KEY | grep -E '^[a-zA-Z0-9]{86}=='
   ```
-- Sufficient disk space for model caching (varies by model, typically 10-50GB)
+- [ ] Sufficient disk space for model caching (varies by model, typically 10-50GB)
   ```bash
   df -h ~
   ```
diff --git a/nvidia/nvfp4-quantization/README.md b/nvidia/nvfp4-quantization/README.md
index 046460b..f9ee9fd 100644
--- a/nvidia/nvfp4-quantization/README.md
+++ b/nvidia/nvfp4-quantization/README.md
@@ -40,11 +40,11 @@ inside a TensorRT-LLM container, producing an NVFP4 quantized model for deployme
 
 ## Prerequisites
 
-- NVIDIA Spark device with Blackwell architecture GPU
-- Docker installed with GPU support
-- NVIDIA Container Toolkit configured
-- At least 32GB of available storage for model files and outputs
-- Hugging Face account with access to the target model
+- [ ] NVIDIA Spark device with Blackwell architecture GPU
+- [ ] Docker installed with GPU support
+- [ ] NVIDIA Container Toolkit configured
+- [ ] At least 32GB of available storage for model files and outputs
+- [ ] Hugging Face account with access to the target model
 
 Verify your setup:
 ```bash
diff --git a/nvidia/ollama/README.md b/nvidia/ollama/README.md
index 776203d..ad0ffe8 100644
--- a/nvidia/ollama/README.md
+++ b/nvidia/ollama/README.md
@@ -6,6 +6,7 @@
 
 - [Overview](#overview)
 - [Instructions](#instructions)
+- [Access with NVIDIA Sync](#access-with-nvidia-sync)
 
 ---
 
@@ -35,9 +36,12 @@ the powerful GPU capabilities of your Spark device without complex network confi
 
 ## Prerequisites
 
-- DGX Spark device set up and connected to your network
-- NVIDIA Sync installed and connected to your Spark
-- Terminal access to your local machine for testing API calls
+- [ ] DGX Spark device set up and connected to your network
+  - Verify with: `nvidia-smi` (should show Blackwell GPU information)
+- [ ] NVIDIA Sync installed and connected to your Spark
+  - Verify connection status in NVIDIA Sync system tray application
+- [ ] Terminal access to your local machine for testing API calls
+  - Verify with: `curl --version`
 
 
 
@@ -229,3 +233,7 @@ Monitor GPU and system usage during inference using the DGX Dashboard available
 
 Build applications using the Ollama API by integrating with your preferred programming language's
 HTTP client libraries.
+
+## Access with NVIDIA Sync
+
+## Step 1. (DRAFT)
diff --git a/nvidia/protein-folding/README.md b/nvidia/protein-folding/README.md
index 60f970b..e777cec 100644
--- a/nvidia/protein-folding/README.md
+++ b/nvidia/protein-folding/README.md
@@ -30,23 +30,23 @@ RTX Pro 6000 or DGX Spark workstation.
 
 ## Prerequisites
 
-- NVIDIA GPU (RTX Pro 6000 or DGX Spark recommended)
+- [ ] NVIDIA GPU (RTX Pro 6000 or DGX Spark recommended)
   ```bash
   nvidia-smi  # Should show GPU with CUDA ≥12.9
   ```
-- NVIDIA drivers and CUDA toolkit installed
+- [ ] NVIDIA drivers and CUDA toolkit installed
   ```bash
   nvcc --version  # Should show CUDA 12.9 or higher
   ```
-- Docker with NVIDIA Container Toolkit
+- [ ] Docker with NVIDIA Container Toolkit
   ```bash
   docker run --rm --gpus all nvidia/cuda:12.9.0-base-ubuntu22.04 nvidia-smi
   ```
-- Python 3.8+ environment
+- [ ] Python 3.8+ environment
   ```bash
   python3 --version  # Should show 3.8 or higher
   ```
-- Sufficient disk space for databases (>3TB recommended)
+- [ ] Sufficient disk space for databases (>3TB recommended)
   ```bash
   df -h  # Check available space
   ```
diff --git a/nvidia/pytorch-fine-tune/README.md b/nvidia/pytorch-fine-tune/README.md
index e921c16..9aeab87 100644
--- a/nvidia/pytorch-fine-tune/README.md
+++ b/nvidia/pytorch-fine-tune/README.md
@@ -13,101 +13,74 @@
 
 ## 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.
+This playbook guides you through setting up and using Pytorch for fine-tuning large language models on NVIDIA Spark devices.
 
 ## 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.
-
+You'll establish a complete fine-tuning environment for large language models (1-70B parameters) on your NVIDIA Spark device. By the end, you'll have a working installation that supports parameter-efficient fine-tuning (PEFT) and supervised fine-tuning (SFT)
 ## What to know before starting
 
 
 
 ## Prerequisites
-
+recipes are specifically for DIGITS SPARK. Please make sure that OS and drivers are latest.
 
 
 ## Ancillary files
 
-
+ALl files required for finetuning are included.
 
 ## Time & risk
 
-**Time estimate:** 
+**Time estimate:** 30-45 mins for setup and runing finetuning. Finetuning run time varies depending on model size 
 
-**Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting, distributed training setup complexity increases with multi-node configurations
+**Risks:** Model downloads can be large (several GB), ARM64 package compatibility issues may require troubleshooting.
 
 **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.
+## Step 1.  Pull the latest Pytorch container
 
 ```bash
-## Verify CUDA installation
-nvcc --version
-
-## Verify GPU accessibility
-nvidia-smi
-
-## Check available system memory
-free -h
+docker pull nvcr.io/nvidia/pytorch:25.09-py3
 ```
 
-## Step 2. Get the container image
+## Step 2. Launch Docker
 
 ```bash
-docker pull nvcr.io/nvidia/pytorch:25.08-py3
+docker run --gpus all -it --rm --ipc=host \
+-v $HOME/.cache/huggingface:/root/.cache/huggingface \
+-v ${PWD}:/workspace -w /workspace \
+nvcr.io/nvidia/pytorch:25.09-py3
+
 ```
 
-## Step 3. Launch Docker
+## Step 3. Install dependencies inside the contianer
 
 ```bash
-docker run \
-  --gpus all \
-  --ulimit memlock=-1 \
-  -it --ulimit stack=67108864 \
-  --entrypoint /usr/bin/bash \
-  --rm nvcr.io/nvidia/pytorch:25.08-py3
+pip install transformers peft datasets "trl==0.19.1" "bitsandbytes==0.48"
 ```
 
-
-
-
-
-## 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.
+## Step 4: authenticate with huggingface
 
 ```bash
-## Remove virtual environment
-rm -rf .venv
+huggingface-cli login
+##<input your huggingface token.
+##<Enter n for git credential>
 
-## Remove cloned repository
-cd ..
-rm -rf Automodel
+```
+To run LoRA on Llama3 use the following command:
 
-## Remove uv (if installed with --user)
-pip3 uninstall uv
-
-## Clear Python cache
-rm -rf ~/.cache/pip
+```bash
+python Llama3_8B_LoRA_finetuning.py
 ```
 
-## Step 12. Next steps
+To run qLoRA finetuning on llama3-70B use the following command:
+```bash
+python Llama3_70B_qLoRA_finetuning.py
+```
+To run full finetuning on llama3-3B use the following command:
+```bash
+python Llama3_3B_full_finetuning.py
+```
diff --git a/nvidia/sglang/README.md b/nvidia/sglang/README.md
index 895e927..230b35d 100644
--- a/nvidia/sglang/README.md
+++ b/nvidia/sglang/README.md
@@ -35,12 +35,12 @@ vision-language tasks using models like DeepSeek-V2-Lite.
 
 ## Prerequisites
 
-- NVIDIA Spark device with Blackwell architecture
-- Docker Engine installed and running: `docker --version`
-- NVIDIA GPU drivers installed: `nvidia-smi`
-- NVIDIA Container Toolkit configured: `docker run --rm --gpus all nvidia/cuda:12.9-base nvidia-smi`
-- Sufficient disk space (>20GB available): `df -h`
-- Network connectivity for pulling NGC containers: `ping nvcr.io`
+- [ ] NVIDIA Spark device with Blackwell architecture
+- [ ] Docker Engine installed and running: `docker --version`
+- [ ] NVIDIA GPU drivers installed: `nvidia-smi`
+- [ ] NVIDIA Container Toolkit configured: `docker run --rm --gpus all nvidia/cuda:12.9-base nvidia-smi`
+- [ ] Sufficient disk space (>20GB available): `df -h`
+- [ ] Network connectivity for pulling NGC containers: `ping nvcr.io`
 
 ## Ancillary files
 
diff --git a/nvidia/speculative-decoding/README.md b/nvidia/speculative-decoding/README.md
index 028f072..d23ec15 100644
--- a/nvidia/speculative-decoding/README.md
+++ b/nvidia/speculative-decoding/README.md
@@ -40,17 +40,17 @@ These examples demonstrate how to accelerate large language model inference whil
 
 ## Prerequisites
 
-- NVIDIA Spark device with sufficient GPU memory available (80GB+ recommended for GPT-OSS 120B)
-- Docker with GPU support enabled
+- [ ] NVIDIA Spark device with sufficient GPU memory available (80GB+ recommended for GPT-OSS 120B)
+- [ ] Docker with GPU support enabled
   ```bash
   docker run --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi
   ```
-- Access to NVIDIA's internal container registry (for Eagle3 example)
-- HuggingFace authentication configured (if needed for model downloads)
+- [ ] Access to NVIDIA's internal container registry (for Eagle3 example)
+- [ ] HuggingFace authentication configured (if needed for model downloads)
   ```bash
   huggingface-cli login
   ```
-- Network connectivity for model downloads
+- [ ] Network connectivity for model downloads
 
 
 ## Time & risk
diff --git a/nvidia/tailscale/README.md b/nvidia/tailscale/README.md
index 73311aa..574c10c 100644
--- a/nvidia/tailscale/README.md
+++ b/nvidia/tailscale/README.md
@@ -51,13 +51,13 @@ all traffic automatically encrypted and NAT traversal handled transparently.
 
 ## Prerequisites
 
-- NVIDIA Spark device running Ubuntu (ARM64/AArch64)
-- Client device (Mac, Windows, or Linux) for remote access
-- Internet connectivity on both devices
-- Valid email account for Tailscale authentication (Google, GitHub, Microsoft)
-- SSH server availability check: `systemctl status ssh`
-- Package manager working: `sudo apt update`
-- User account with sudo privileges on Spark device
+- [ ] NVIDIA Spark device running Ubuntu (ARM64/AArch64)
+- [ ] Client device (Mac, Windows, or Linux) for remote access
+- [ ] Internet connectivity on both devices
+- [ ] Valid email account for Tailscale authentication (Google, GitHub, Microsoft)
+- [ ] SSH server availability check: `systemctl status ssh`
+- [ ] Package manager working: `sudo apt update`
+- [ ] User account with sudo privileges on Spark device
 
 ## Time & risk
 
diff --git a/nvidia/trt-llm/README.md b/nvidia/trt-llm/README.md
index 47c3465..359955f 100644
--- a/nvidia/trt-llm/README.md
+++ b/nvidia/trt-llm/README.md
@@ -54,13 +54,13 @@ inference through kernel-level optimizations, efficient memory layouts, and adva
 
 ## Prerequisites
 
-- NVIDIA Spark device with Blackwell architecture GPUs
-- NVIDIA drivers compatible with CUDA 12.x: `nvidia-smi`
-- Docker installed and GPU support configured: `docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi`
-- Hugging Face account with token for model access: `echo $HF_TOKEN`
-- Sufficient GPU VRAM (16GB+ recommended for 70B models)
-- Internet connectivity for downloading models and container images
-- Network: open TCP ports 8355 (LLM) and 8356 (VLM) on host for OpenAI-compatible serving
+- [ ] NVIDIA Spark device with Blackwell architecture GPUs
+- [ ] NVIDIA drivers compatible with CUDA 12.x: `nvidia-smi`
+- [ ] Docker installed and GPU support configured: `docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi`
+- [ ] Hugging Face account with token for model access: `echo $HF_TOKEN`
+- [ ] Sufficient GPU VRAM (16GB+ recommended for 70B models)
+- [ ] Internet connectivity for downloading models and container images
+- [ ] Network: open TCP ports 8355 (LLM) and 8356 (VLM) on host for OpenAI-compatible serving
 
 ## Model Support Matrix
 
diff --git a/nvidia/unsloth/README.md b/nvidia/unsloth/README.md
index 49f69de..a96c201 100644
--- a/nvidia/unsloth/README.md
+++ b/nvidia/unsloth/README.md
@@ -36,10 +36,10 @@ parameter-efficient fine-tuning methods like LoRA and QLoRA.
 
 ## Prerequisites
 
-- NVIDIA Spark device with Blackwell GPU architecture
-- `nvidia-smi` shows a summary of GPU information
-- CUDA 13.0 installed: `nvcc --version`
-- Internet access for downloading models and datasets
+- [ ] NVIDIA Spark device with Blackwell GPU architecture
+- [ ] `nvidia-smi` shows a summary of GPU information
+- [ ] CUDA 13.0 installed: `nvcc --version`
+- [ ] Internet access for downloading models and datasets
 
 ##Ancillary files
 
diff --git a/nvidia/vllm/README.md b/nvidia/vllm/README.md
index ec8c6a3..24bd32f 100644
--- a/nvidia/vllm/README.md
+++ b/nvidia/vllm/README.md
@@ -5,9 +5,9 @@
 ## Table of Contents
 
 - [Overview](#overview)
-- [Instructions](#instructions)
 - [Run on two Sparks](#run-on-two-sparks)
   - [Step 14. (Optional) Launch 405B inference server](#step-14-optional-launch-405b-inference-server)
+- [Access through terminal](#access-through-terminal)
 
 ---
 
@@ -29,14 +29,14 @@ support for ARM64.
 
 ## Prerequisites
 
-- DGX Spark device with ARM64 processor and Blackwell GPU architecture
-- CUDA 12.9 or CUDA 13.0 toolkit installed: `nvcc --version` shows CUDA toolkit version.  
-- Docker installed and configured: `docker --version` succeeds
-- NVIDIA Container Toolkit installed
-- Python 3.12 available: `python3.12 --version` succeeds
-- Git installed: `git --version` succeeds
-- Network access to download packages and container images
-
+- [ ] DGX Spark device with ARM64 processor and Blackwell GPU architecture
+- [ ] CUDA 12.9 or CUDA 13.0 toolkit installed: `nvcc --version` shows CUDA toolkit version.  
+- [ ] Docker installed and configured: `docker --version` succeeds
+- [ ] NVIDIA Container Toolkit installed
+- [ ] Python 3.12 available: `python3.12 --version` succeeds
+- [ ] Git installed: `git --version` succeeds
+- [ ] Network access to download packages and container images
+- [ ] > TODO: Verify memory and storage requirements for builds
 
 ## Time & risk
 
@@ -46,77 +46,6 @@ support for ARM64.
 
 **Rollback:** Container approach is non-destructive.
 
-## Instructions
-
-## Step 1. Pull vLLM container image 
-
-Find the latest container build from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=25.09-py3
-```
-docker pull nvcr.io/nvidia/vllm:25.09-py3
-```
-
-## Step 2. Test vLLM in container
-
-Launch the container and start vLLM server with a test model to verify basic functionality.
-
-```bash
-docker run -it --gpus all -p 8000:8000 \
-nvcr.io/nvidia/vllm:25.09-py3 \
-vllm serve "Qwen/Qwen2.5-Math-1.5B-Instruct"
-```
-
-Expected output should include:
-- Model loading confirmation
-- Server startup on port 8000
-- GPU memory allocation details
-
-In another terminal, test the server:
-
-```bash
-curl http://localhost:8000/v1/chat/completions \
--H "Content-Type: application/json" \
--d '{
-    "model": "Qwen/Qwen2.5-Math-1.5B-Instruct",
-    "messages": [{"role": "user", "content": "12*17"}],
-    "max_tokens": 500
-}'
-```
-
-Expected response should contain `"content": "204"` or similar mathematical calculation.
-
-## Step 3. Troubleshooting
-
-| Symptom | Cause | Fix |
-|---------|--------|-----|
-| CUDA version mismatch errors | Wrong CUDA toolkit version | Reinstall CUDA 12.9 using exact installer |
-| Container registry authentication fails | Invalid or expired GitLab token | Generate new auth token |
-| SM_121a architecture not recognized | Missing LLVM patches | Verify SM_121a patches applied to LLVM source |
-| Reduce MAX_JOBS to 1-2, add swap space |
-| Environment variables not set |
-
-## Step 4. Cleanup and rollback
-
-For container approach (non-destructive):
-
-```bash
-docker rm $(docker ps -aq --filter ancestor=******:5005/dl/dgx/vllm*)
-docker rmi ******:5005/dl/dgx/vllm:main-py3.31165712-devel
-```
-
-
-To remove CUDA 12.9:
-
-```bash
-sudo /usr/local/cuda-12.9/bin/cuda-uninstaller
-```
-
-## Step 5. Next steps
-
-- **Production deployment:** Configure vLLM with your specific model requirements
-- **Performance tuning:** Adjust batch sizes and memory settings for your workload  
-- **Monitoring:** Set up logging and metrics collection for production use
-- **Model management:** Explore additional model formats and quantization options
-
 ## Run on two Sparks
 
 ## Step 1. Verify hardware connectivity
@@ -381,3 +310,74 @@ http://192.168.100.10:8265
 ## - Persistent model caching across restarts
 ## - Alternative quantization methods (FP8, INT4)
 ```
+
+## Access through terminal
+
+## Step 1. Pull vLLM container image 
+
+Find the latest container build from https://catalog.ngc.nvidia.com/orgs/nvidia/containers/vllm?version=25.09-py3
+```
+docker pull nvcr.io/nvidia/vllm:25.09-py3
+```
+
+## Step 2. Test vLLM in container
+
+Launch the container and start vLLM server with a test model to verify basic functionality.
+
+```bash
+docker run -it --gpus all -p 8000:8000 \
+nvcr.io/nvidia/vllm:25.09-py3 \
+vllm serve "Qwen/Qwen2.5-Math-1.5B-Instruct"
+```
+
+Expected output should include:
+- Model loading confirmation
+- Server startup on port 8000
+- GPU memory allocation details
+
+In another terminal, test the server:
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+-H "Content-Type: application/json" \
+-d '{
+    "model": "Qwen/Qwen2.5-Math-1.5B-Instruct",
+    "messages": [{"role": "user", "content": "12*17"}],
+    "max_tokens": 500
+}'
+```
+
+Expected response should contain `"content": "204"` or similar mathematical calculation.
+
+## Step 3. Troubleshooting
+
+| Symptom | Cause | Fix |
+|---------|--------|-----|
+| CUDA version mismatch errors | Wrong CUDA toolkit version | Reinstall CUDA 12.9 using exact installer |
+| Container registry authentication fails | Invalid or expired GitLab token | Generate new auth token |
+| SM_121a architecture not recognized | Missing LLVM patches | Verify SM_121a patches applied to LLVM source |
+| Reduce MAX_JOBS to 1-2, add swap space |
+| Environment variables not set |
+
+## Step 4. Cleanup and rollback
+
+For container approach (non-destructive):
+
+```bash
+docker rm $(docker ps -aq --filter ancestor=******:5005/dl/dgx/vllm*)
+docker rmi ******:5005/dl/dgx/vllm:main-py3.31165712-devel
+```
+
+
+To remove CUDA 12.9:
+
+```bash
+sudo /usr/local/cuda-12.9/bin/cuda-uninstaller
+```
+
+## Step 5. Next steps
+
+- **Production deployment:** Configure vLLM with your specific model requirements
+- **Performance tuning:** Adjust batch sizes and memory settings for your workload  
+- **Monitoring:** Set up logging and metrics collection for production use
+- **Model management:** Explore additional model formats and quantization options
diff --git a/nvidia/vss/README.md b/nvidia/vss/README.md
index 78b41d1..d457e12 100644
--- a/nvidia/vss/README.md
+++ b/nvidia/vss/README.md
@@ -43,14 +43,14 @@ You will deploy NVIDIA's VSS AI Blueprint on NVIDIA Spark hardware with Blackwel
 
 ## Prerequisites
 
-- NVIDIA Spark device with ARM64 architecture and Blackwell GPU
-- FastOS 1.81.38 or compatible ARM64 system
-- Driver version 580.82.09 installed: `nvidia-smi | grep "Driver Version"`
-- CUDA version 13.0 installed: `nvcc --version`
-- Docker installed and running: `docker --version && docker compose version`
-- Access to NVIDIA Container Registry with NGC API Key
-- [Optional] NVIDIA API Key for remote model endpoints (hybrid deployment only)
-- Sufficient storage space for video processing (>10GB recommended in `/tmp/`)
+- [ ] NVIDIA Spark device with ARM64 architecture and Blackwell GPU
+- [ ] FastOS 1.81.38 or compatible ARM64 system
+- [ ] Driver version 580.82.09 installed: `nvidia-smi | grep "Driver Version"`
+- [ ] CUDA version 13.0 installed: `nvcc --version`
+- [ ] Docker installed and running: `docker --version && docker compose version`
+- [ ] Access to NVIDIA Container Registry with NGC API Key
+- [ ] [Optional] NVIDIA API Key for remote model endpoints (hybrid deployment only)
+- [ ] Sufficient storage space for video processing (>10GB recommended in `/tmp/`)
 
 ## Ancillary files