Add minitron pruning and distillation guidelines in pruning readme

CHANGELOG.rst

@@ -1,7 +1,7 @@
 Model Optimizer Changelog (Linux)
 =================================
 
-0.39 (2025-10-xx)
+0.39 (2025-11-xx)
 ^^^^^^^^^^^^^^^^^
 
 **Deprecations**
@@ -12,7 +12,11 @@ Model Optimizer Changelog (Linux)
 - Add LoRA mode support for MCore in a new peft submodule: ``modelopt.torch.peft.update_model(model, LORA_CFG)``.
 - Support PTQ and fakequant in vLLM for fast evaluation of arbitrary quantization formats. See ``examples/vllm_serve`` for more details.
 
-0.37 (2025-09-xx)
+**Documentation**
+
+- Add general guidelines for Minitron pruning and distillation. See `examples/pruning/README.md <https://github.com/NVIDIA/TensorRT-Model-Optimizer/tree/main/examples/pruning#pruning-guidelines>`_ for more details.
+
+0.37 (2025-10-08)
 ^^^^^^^^^^^^^^^^^
 
 **Deprecations**

examples/pruning/README.md

@@ -17,6 +17,8 @@ This section focuses on applying Model Optimizer's state-of-the-art complementar
 | Pre-Requisites | Required & optional packages to use this technique | \[[Link](#pre-requisites)\] | |
 | Getting Started | Learn how to use the pruning API | \[[Link](#getting-started)\] | \[[docs](https://nvidia.github.io/TensorRT-Model-Optimizer/guides/3_pruning.html)\] |
 | Support Matrix | View the support matrix to see available pruning algorithms and their compatibility with different models and frameworks | \[[Link](#support-matrix)\] | |
+| Pruning Guidelines | Guidelines for choosing how and how much to prune for best results | \[[Link](#pruning-guidelines)\] | |
+| Examples | Examples of different pruning methods | \[[Link](#examples)\] | |
 | Resources | Extra links to relevant resources | \[[Link](#resources)\] | |
 
 </div>
@@ -93,6 +95,84 @@ If your model parameters are already sorted, you can skip the sorting step by se
 
 > *<sup>1.</sup>Only Pipeline Parallel models are supported. Hugging Face models can be converted to NeMo format and used subsequently.*
 
+## Pruning Guidelines
+
+### Minitron
+
+This section provides recommendations for choosing pruning strategies and distillation hyperparameters for Minitron pruning to help achieve the best latency-accuracy trade-offs.
+
+#### Depth Pruning
+
+Depth pruning reduces the number of layers (`num_layers`) in the model.
+
+**Advantages:**
+
+- Simpler to configure - only 1 parameter to tune
+- Faster inference than width-pruned models at a fixed number of parameters
+
+**Recommendations:**
+
+- Up to **1/3rd parameter reduction** can generally result in a model above the Pareto frontier with good latency-accuracy trade-off (when using a good quality dataset for distillation with ~80-100B tokens)
+- For pruning **>50%**, use iterative pruning: compress by 30%, perform distillation, then compress again
+
+**Examples:**
+
+- [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) (`num_layers=36`) → 6B (`num_layers=24`)
+- [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) (`num_layers=32`) → 4.5B (`num_layers=16`)
+
+#### Width Pruning
+
+Width pruning reduces model dimensions per layer such as `hidden_size`, `ffn_hidden_size`, `num_attention_heads`, `num_query_groups`, `mamba_num_heads`, and `mamba_head_dim`.
+
+**Advantages:**
+
+- Better accuracy than depth-pruned models at a fixed number of parameters
+
+**Recommendations:**
+
+- Start with pruning `hidden_size` and `ffn_hidden_size` as the simplest configuration
+- Up to **1/3rd parameter reduction** can generally result in a model above the Pareto frontier with good latency-accuracy trade-off (when using a good quality dataset for distillation with ~80-100B tokens)
+- **Axis sensitivity:** MLP dimensions (`ffn_hidden_size`) can typically be pruned more aggressively than embedding dimensions (`hidden_size`) and attention/Mamba dimensions (`num_attention_heads`, `num_query_groups`, `mamba_num_heads`, `mamba_head_dim`)
+- For pruning **>50%**, use iterative pruning: compress by 30%, perform distillation, then compress again
+
+**Examples:**
+
+- [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) (`ffn_hidden_size=12288`, `hidden_size=4096`) → 6B (`ffn_hidden_size=9216`, `hidden_size=3584`)
+- [Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B) (`ffn_hidden_size=14336`, `hidden_size=4096`) → 4.5B (`ffn_hidden_size=9216`, `hidden_size=3072`)
+- [Nemotron-H-8B-Base-8K](https://huggingface.co/nvidia/Nemotron-H-8B-Base-8K) (`ffn_hidden_size=21504`, `hidden_size=4096`, `mamba_num_heads=128`) → [Nemotron-H-4B-Base-8K](https://huggingface.co/nvidia/Nemotron-H-4B-Base-8K) (`ffn_hidden_size=12288`, `hidden_size=3072`, `mamba_num_heads=112`) - See [paper](https://arxiv.org/pdf/2504.11409)
+
+#### Depth and Width Pruning
+
+For optimal results, combine depth and width pruning. This will require more tuning to find the best architecture.
+
+**Examples:**
+
+- [NVIDIA-Nemotron-Nano-12B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-12B-v2) (`ffn_hidden_size=20480`, `hidden_size=5120`, `num_layers=62`) → [NVIDIA-Nemotron-Nano-9B-v2](https://huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2) (`ffn_hidden_size=15680`, `hidden_size=4480`, `num_layers=56`) - See [paper](https://arxiv.org/pdf/2508.14444)
+
+#### General Pruning Guidelines
+
+- **Pruning ratio:** Anything **>50% pruning is hard to recover**. For such aggressive pruning, iterative pruning (compress → distill → compress again) is recommended.
+- **Latency-accuracy trade-off:** The more pruning you do, the faster your model will be at the cost of lower accuracy. Choose based on your requirements.
+- **Dataset quality:** Use a high-quality dataset for distillation. If you don't have a specific dataset, [Nemotron-Pretraining-SFT-v1](https://huggingface.co/datasets/nvidia/Nemotron-Pretraining-SFT-v1) is recommended.
+- **Post-training:** Further post-training (e.g., instruction tuning, preference alignment) is needed after pruning and distillation on pre-training datasets to improve reasoning capabilities. A good dataset for post-training is [Nemotron-Post-Training-Dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2).
+
+#### Distillation Hyperparameters
+
+After pruning, distillation is required to recover model accuracy. Below are recommended starting hyperparameters for distillation:
+
+| **Hyperparameter** | **Recommendation** |
+| :--- | :--- |
+| **Sequence Length** | 8192 (or 4096 if dataset has smaller sequences) |
+| **Global Batch Size (GBS)** | 768 |
+| **Micro Batch Size (MBS)** | As large as your GPU memory can accommodate |
+| **Learning Rate (LR)** | 1e-4 → 1e-5 (linear decay) for 30-50% pruning<br>• More compression → higher LR<br>• Less compression → lower LR<br>• As model gets larger → reduce LR to avoid divergence |
+| **Warmup Steps** | 100 |
+| **Training Max Steps** | Num training tokens / (Seq len × GBS)<br>• Recommended: 80-100B tokens |
+| **Data Composition** | • Standard models: 100% pre-training data<br>• Reasoning models: 70% reasoning data + 30% pre-training data |
+
+> [!TIP]
+> If you know the maximum learning rate used during the original training, a good rule of thumb for knowledge distillation is to use **1/5th of that maximum LR** when compressing by ~50%.
+
 ## Examples
 
 ### Minitron Pruning for Megatron-LM / NeMo Framework LLMs (e.g. Qwen 3, Nemotron Nano)
@@ -108,7 +188,9 @@ Some of the models pruned using Minitron method followed by distillation and pos
 
 ### FastNAS Pruning for PyTorch Computer Vision Models
 
-Checkout the FastNAS pruning interactive notebook [cifar_resnet](./cifar_resnet.ipynb) in this directory
+Check out the FastNAS pruning example usage in the [documentation](https://nvidia.github.io/TensorRT-Model-Optimizer/guides/3_pruning.html#pruning-and-subnet-search).
+
+You can also take a look at FastNAS pruning interactive notebook [cifar_resnet](./cifar_resnet.ipynb) in this directory
 which showcases the usage of FastNAS for pruning a ResNet 20 model for the CIFAR-10 dataset. The notebook
 also how to profiling the model to understand the search space of possible pruning options and demonstrates
 the usage saving and restoring pruned models.