feat: add tutorials

tutorials/README.md

@@ -1,6 +1,38 @@
 # NeMo AutoModel Tutorials
 
+End-to-end tutorials covering the LLM customization lifecycle using
+[NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel).
 
-| Tutorial | Type | Brev Instance | Launch on Brev |
-| --- | --- | --- | --- |
-| [**Nemotron Parse fine-tune**](https://github.com/NVIDIA-NeMo/Automodel/blob/main/tutorials/nemotron-parse/finetune.ipynb) | Fine-tuning | L40S | [![Launch on Brev](https://brev-assets.s3.us-west-1.amazonaws.com/nv-lb-dark.svg)](https://brev.nvidia.com/launchable/deploy/now?launchableID=env-3C6LDKU2DfOvpVTFhjw3YQ4djPM) |
+| Tutorial | Dataset | Description | Launch on Brev |
+|----------|---------|-------------|----------------|
+| [Domain Adaptive Pre-Training (DAPT)](./dapt) | Domain-specific text corpus | Continued pre-training of a foundation model on domain data to improve in-domain performance (inspired by [ChipNeMo](https://arxiv.org/abs/2311.00176)). | 🚧 |
+| [Supervised Fine-Tuning (SFT)](./sft-peft) | [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) | Full-parameter SFT to adapt a pre-trained model to follow instructions. | 🚧 |
+| [Parameter-Efficient Fine-Tuning (PEFT)](./sft-peft) | [SQuAD](https://huggingface.co/datasets/rajpurkar/squad) | Memory-efficient LoRA fine-tuning for task adaptation. | 🚧 |
+| [Evaluation](./evaluation) | Standard benchmarks (MMLU, HellaSwag, IFEval, etc.) | Evaluate AutoModel checkpoints with lm-evaluation-harness. | 🚧 |
+| [Reasoning SFT](./reasoning-sft) | Reasoning instruction data (OpenAI chat format) | Fine-tune a model to selectively enable chain-of-thought reasoning via system prompt control. | 🚧 |
+| [Nemotron Parse Fine-Tuning](./nemotron-parse) | [Invoices](https://huggingface.co/datasets/katanaml-org/invoices-donut-data-v1) | Fine-tune Nemotron Parse v1.1 for structured document extraction. | [![Launch on Brev](https://brev-assets.s3.us-west-1.amazonaws.com/nv-lb-dark.svg)](https://brev.nvidia.com/launchable/deploy/now?launchableID=env-3C6LDKU2DfOvpVTFhjw3YQ4djPM) |
+
+## Prerequisites
+
+- [NeMo AutoModel](https://github.com/NVIDIA-NeMo/Automodel) installed
+  (see the AutoModel README for setup instructions).
+- NVIDIA GPU(s) with sufficient memory (specific requirements noted per tutorial).
+- Hugging Face account and API token for gated models (e.g., Llama).
+
+## Pipeline Overview
+
+These tutorials cover four stages of the LLM customization lifecycle:
+
+```
+Foundation Model ──> DAPT ──> SFT / PEFT ──> Evaluation
+       |
+       └──────────> Reasoning SFT ──────────> Evaluation
+```
+
+- **DAPT**: Inject domain knowledge via continued pre-training.
+- **SFT / PEFT**: Teach the model to follow instructions or solve specific tasks.
+- **Reasoning SFT**: Teach the model chain-of-thought reasoning with on/off control.
+- **Evaluation**: Measure quality on standard benchmarks after each stage.
+
+For reinforcement learning from human feedback (RLHF / DPO / PPO), see
+[NeMo-RL](https://github.com/NVIDIA/NeMo-RL).