docs: readme simple init formatting

docs/megatron/models/dit/README.md

@@ -82,7 +82,7 @@ Done
 
 ### 🐳 Build Container
 
-Please follow the instructions in the [container](https://github.com/NVIDIA-NeMo/DFM#-built-your-own-container) section of the main README.
+Please follow the instructions in the [container](https://github.com/NVIDIA-NeMo/DFM#build-your-own-container) section of the main README.
 
 ---
 
@@ -106,12 +106,12 @@ The model architecture can be customized through parameters such as `num_layers`
 First, copy the example config file and update it with your own settings:
 
 ```bash
-cp examples/megatron/recipes/dit/conf/dit_pretrain_example.yaml examples/megatron/recipes/dit/conf/my_config.yaml
+cp examples/megatron/recipes/dit/conf/dit_pretrain.yaml examples/megatron/recipes/dit/conf/my_config.yaml
 # Edit my_config.yaml to set:
 # - model.vae_cache_folder: Path to VAE cache folder
 # - dataset.path: Path to your dataset folder
 # - checkpoint.save and checkpoint.load: Path to checkpoint folder
-# - train.global_batch_size: Set to match be divisible by NUM_GPUs
+# - train.global_batch_size: Set to be divisible by NUM_GPUs
 # - logger.wandb_exp_name: Your experiment name
 ```
 

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.
+
+## Supported Tasks
+
+| Task | Directory | Available Examples |
+|------|-----------|-------------------|
+| **Fine-tuning** | **[finetune](finetune/)** | • **[Wan 2.1 T2V](finetune/finetune.py)**: Fine-tuning with Flow Matching <br> • **[Multi-node](finetune/wan2_1_t2v_flow_multinode.yaml)**: Distributed training config |
+| **Generation** | **[generate](generate/)** | • **[Generate](generate/wan_generate.py)**: Run inference with Wan 2.1 <br> • **[Validate](generate/wan_validate.py)**: Run validation loop |
+| **Pre-training** | **[pretrain](pretrain/)** | • **[Wan 2.1 T2V](pretrain/pretrain.py)**: Pre-training from scratch |
+
+---
+
 # Diffusion Model Fine-tuning with Automodel Backend
 
 Train diffusion models with distributed training support using NeMo Automodel and flow matching.

examples/megatron/README.md

@@ -1,3 +1,17 @@
 # Megatron Examples
 
-Recipes and configuration overrides for megatron training.
+Advanced recipes and configuration overrides for training models using the Megatron-Core backend.
+
+## Available Model Recipes
+
+| Recipe | Key Scripts | Description |
+|--------|-------------|-------------|
+| **[DiT](recipes/dit/README.md)** | • [Pretrain](recipes/dit/pretrain_dit_model.py) <br> • [Inference](recipes/dit/inference_dit_model.py) | Diffusion Transformer (DiT) training on butterfly dataset |
+| **[Wan](recipes/wan/README.md)** | • [Pretrain](recipes/wan/pretrain_wan.py) <br> • [Inference](recipes/wan/inference_wan.py) | Wan 2.1 model pre-training and inference |
+
+## Directory Structure
+
+| Directory | Description |
+|-----------|-------------|
+| **[recipes](recipes/)** | Source code and scripts for the models above |
+| **[override_configs](override_configs/)** | Configuration overrides for customizing parallelism (TP/CP/SP) |

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 |
+|--------|-------------|
+| **[DiT](dit/)** | Diffusion Transformer (DiT) training on butterfly dataset |
+| **[Wan](wan/)** | Wan 2.1 model pre-training and inference |

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.).