Update docs

Signed-off-by: Keval Morabia <28916987+kevalmorabia97@users.noreply.github.com>

Author: kevalmorabia97

examples/megatron_bridge/README.md

@@ -18,7 +18,33 @@ This directory contains examples of using Model Optimizer with [NeMo Megatron-Br
 
 Running these examples requires many additional dependencies to be installed (e.g., Megatron-Bridge, Megatron-core, etc.), hence we strongly recommend directly using the NeMo container (e.g., `nvcr.io/nvidia/nemo:26.02`) which has all the dependencies installed.
 
-To get the latest ModelOpt features and examples, you can mount your latest ModelOpt cloned repository to the container at `/opt/Megatron-Bridge/3rdparty/Model-Optimizer` or pull the latest changes once inside the docker container (`cd /opt/Megatron-Bridge/3rdparty/Model-Optimizer && git checkout main && git pull`).
+To get the latest ModelOpt features and examples scripts, mount your Model-Optimizer repo to the container.
+
+```bash
+export MODELOPT_DIR=${PWD}/Model-Optimizer # or set to your local Model-Optimizer repository path if you have cloned it
+if [ ! -d "${MODELOPT_DIR}" ]; then
+  git clone https://github.com/NVIDIA/Model-Optimizer.git ${MODELOPT_DIR}
+fi
+
+export DOCKER_IMAGE=nvcr.io/nvidia/nemo:26.02
+docker run \
+  --gpus all \
+  --shm-size=16GB \
+  --net=host \
+  --ulimit memlock=-1 \
+  --rm -it \
+  -v ${MODELOPT_DIR}:/opt/Model-Optimizer \
+  -v ${MODELOPT_DIR}/modelopt:/opt/venv/lib/python3.12/site-packages/modelopt \
+  -w /opt/Model-Optimizer/examples/megatron_bridge \
+  ${DOCKER_IMAGE} bash
+```
+
+Once inside the container, you need to login with your HuggingFace token to download gated datasets / models.
+Note that the default dataset for pruning and quantization is [`nemotron-post-training-dataset-v2`](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2), which is gated.
+
+```bash
+huggingface-cli login --token <your token>
+```
 
 ## Pruning
 
@@ -30,7 +56,7 @@ Example usage to prune Qwen3-8B to 6B on 2-GPUs (Pipeline Parallelism = 2) while
     top-10 candidates are evaluated for MMLU score (5% sampled data) to select the best model.
 
 ```bash
-torchrun --nproc_per_node 2 /opt/Megatron-Bridge/3rdparty/Model-Optimizer/examples/megatron_bridge/prune_minitron.py \
+torchrun --nproc_per_node 2 prune_minitron.py \
     --hf_model_name_or_path Qwen/Qwen3-8B \
     --prune_target_params 6e9 \
     --hparams_to_skip num_attention_heads \
@@ -41,7 +67,7 @@ Example usage for manually pruning to a specific architecture using following de
     1024 samples from [`nemotron-post-training-dataset-v2`](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) for calibration.
 
 ```bash
-torchrun --nproc_per_node 2 /opt/Megatron-Bridge/3rdparty/Model-Optimizer/examples/megatron_bridge/prune_minitron.py \
+torchrun --nproc_per_node 2 prune_minitron.py \
     --hf_model_name_or_path Qwen/Qwen3-8B \
     --prune_export_config '{"hidden_size": 3584, "ffn_hidden_size": 9216}' \
     --output_hf_path /tmp/Qwen3-8B-Pruned-6B-manual
@@ -50,7 +76,7 @@ torchrun --nproc_per_node 2 /opt/Megatron-Bridge/3rdparty/Model-Optimizer/exampl
 To see the full usage for advanced configurations, run:
 
 ```bash
-torchrun --nproc_per_node 1 /opt/Megatron-Bridge/3rdparty/Model-Optimizer/examples/megatron_bridge/prune_minitron.py --help
+torchrun --nproc_per_node 1 prune_minitron.py --help
 ```
 
 > [!TIP]

examples/megatron_bridge/prune_minitron.py

@@ -27,7 +27,7 @@
         --output_hf_path /tmp/Qwen3-8B-Pruned-6B
 
 To see the full usage for advanced configurations, run:
-    python prune_minitron.py --help
+    torchrun --nproc_per_node 1 prune_minitron.py --help
 """
 
 import argparse