docs: readme simple init formatting

Signed-off-by: Lawrence Lane <llane@nvidia.com>

Author: lbliii

examples/README.md

@@ -1,3 +1,18 @@
-# Examples
+# DFM Examples
 
-Training and inference examples for dtensor and megatron models
+Collection of examples and recipes for training and inference using the Data Foundation Model (DFM) framework.
+
+## Quick Start
+
+If you are new to DFM, start with the **[Automodel Examples](automodel/)** for high-level API usage.
+
+## Examples by Category
+
+| Category | Description | Key Examples |
+|----------|-------------|--------------|
+| **[Automodel](automodel/)** | High-level API for seamless training and inference | [Wan 2.1 Fine-tuning](automodel/README.md), [Pretraining](automodel/pretrain/), [Generation](automodel/generate/) |
+| **[Megatron](megatron/)** | Advanced recipes and configurations using Megatron-Core | [DiT Recipes](megatron/recipes/dit/), [Wan Recipes](megatron/recipes/wan/) |
+
+## Support
+
+For issues or questions, please open a GitHub issue or refer to the main documentation.

examples/automodel/README.md

@@ -1,3 +1,17 @@
+# Automodel Examples
+
+High-level API examples for training, fine-tuning, and generating with DFM models.
+
+## Directory Structure
+
+| Directory | Description |
+|-----------|-------------|
+| **[finetune](finetune/)** | Scripts and configs for fine-tuning models |
+| **[generate](generate/)** | Inference and validation scripts |
+| **[pretrain](pretrain/)** | Pre-training workflows |
+
+---
+
 # Diffusion Model Fine-tuning with Automodel Backend
 
 Train diffusion models with distributed training support using NeMo Automodel and flow matching.
@@ -219,7 +233,7 @@ fsdp:
 ## Features
 
 - ✅ **Flow Matching**: Pure flow matching training
-- ✅ **Distributed**: FSDP2 + Tensor Parallelism
+- **Distributed**: FSDP2 + Tensor Parallelism
 - ✅ **Mixed Precision**: BF16 by default
 - ✅ **WandB**: Automatic logging
 - ✅ **Checkpointing**: consolidated, and sharded formats

examples/megatron/README.md

@@ -1,3 +1,14 @@
 # Megatron Examples
 
-Recipes and configuration overrides for megatron training.
+Advanced recipes and configuration overrides for training models using the Megatron-Core backend.
+
+## Contents
+
+| Directory | Description |
+|-----------|-------------|
+| **[recipes](recipes/)** | Complete training recipes for specific models (DiT, Wan) |
+| **[override_configs](override_configs/)** | Configuration overrides for customizing Megatron behavior |
+
+## Getting Started
+
+Navigate to a specific recipe in the `recipes/` directory to see model-specific instructions.

examples/megatron/override_configs/README.md

@@ -1,3 +1,13 @@
-# Override Configs
+# Configuration Overrides
 
-Parallelism configuration overrides for different CP/TP/SP sizes.
+Collection of YAML configuration files used to override default settings in Megatron training recipes. These are typically used for specifying parallelization strategies (Tensor Parallelism, Context Parallelism, Sequence Parallelism) or data configurations.
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `wan_pretrain_sample_data.yaml` | Sample data configuration for Wan pre-training. |
+
+## Usage
+
+These configs can be passed to the training script arguments to override defaults.

examples/megatron/recipes/README.md

@@ -1,3 +1,10 @@
-# Recipe
+# Model Recipes
 
-Training recipes for Wan2.1 pretraining, finetuning, and weight verification.
+Collection of end-to-end training recipes for specific model architectures.
+
+## Available Recipes
+
+| Recipe | Description | Status |
+|--------|-------------|--------|
+| **[DiT](dit/)** | Diffusion Transformer (DiT) training on butterfly dataset | ✅ Stable |
+| **[Wan](wan/)** | Wan 2.1 model pre-training and inference | ✅ Stable |

examples/megatron/recipes/wan/README.md

@@ -0,0 +1,13 @@
+# Wan Recipes
+
+Recipes for training and inferencing Wan models using Megatron-Core.
+
+## Files
+
+- `pretrain_wan.py`: Main pre-training script.
+- `inference_wan.py`: Inference script.
+- `prepare_energon_dataset_wan.py`: Dataset preparation utility.
+
+## Performance Testing
+
+See **[Performance Test Guide](README_perf_test.md)** for details on running performance benchmarks on different hardware (H100, GB200, etc.).