Minor Doc updates

Signed-off-by: Keval Morabia <[email protected]>

Author: kevalmorabia97

README.md

@@ -75,8 +75,10 @@ pip install -e .[dev]
 ```
 
 You can also directly use the [TensorRT-LLM docker images](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/release/tags)
-(e.g., `nvcr.io/nvidia/tensorrt-llm/release:<version>`),
-which have Model Optimizer pre-installed. Visit our [installation guide](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more fine-grained control on installed dependencies or for alternative docker images and environment variables to setup.
+(e.g., `nvcr.io/nvidia/tensorrt-llm/release:<version>`), which have Model Optimizer pre-installed.
+Make sure to upgrade Model Optimizer to the latest version using ``pip`` as described above.
+Visit our [installation guide](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for
+more fine-grained control on installed dependencies or for alternative docker images and environment variables to setup.
 
 ## Techniques
 

docs/source/getting_started/_installation_for_Linux.rst

@@ -34,7 +34,7 @@ Environment setup
     `TensorRT-LLM docker image <https://catalog.ngc.nvidia.com/orgs/nvidia/teams/tensorrt-llm/containers/release/tags>`_,
     e.g., ``nvcr.io/nvidia/tensorrt-llm/release:<version>``.
 
-    You may upgrade the Model Optimizer to the latest version if not already as described in the next section.
+    Make sure to upgrade Model Optimizer to the latest version using ``pip`` as described in the next section.
 
     You would also need to setup appropriate environment variables for the TensorRT binaries as follows:
 

examples/diffusers/README.md

@@ -27,6 +27,14 @@ Cache Diffusion is a technique that reuses cached outputs from previous diffusio
 
 ## Pre-Requisites
 
+### Docker
+
+Please use the TensorRT docker image (e.g., `nvcr.io/nvidia/tensorrt:25.08-py3`) or visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
+
+Also follow the installation steps below to upgrade to the latest version of Model Optimizer and install example-specific dependencies.
+
+### Local Installation
+
 Install Model Optimizer with `onnx` and `hf` dependencies using `pip` from [PyPI](https://pypi.org/project/nvidia-modelopt/):
 
 ```bash

examples/llm_distill/README.md

@@ -21,15 +21,23 @@ This section focuses on demonstrating how to apply Model Optimizer to perform kn
 
 ## Pre-Requisites
 
+### Docker
+
+For Hugging Face models, please use the PyTorch docker image (e.g., `nvcr.io/nvidia/pytorch:25.06-py3`).
+For NeMo models, use the NeMo container (e.g., `nvcr.io/nvidia/nemo:25.07`) which has all the dependencies installed.
+Visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
+
+Also follow the installation steps below to upgrade to the latest version of Model Optimizer and install example-specific dependencies.
+
+### Local Installation
+
 For Hugging Face models, install Model Optimizer with `hf` dependencies using `pip` from [PyPI](https://pypi.org/project/nvidia-modelopt/) and install the requirements for the example:
 
 ```bash
-pip install nvidia-modelopt[hf]
+pip install -U nvidia-modelopt[hf]
 pip install -r requirements.txt
 ```
 
-For NeMo models, use the NeMo container `nvcr.io/nvidia/nemo:25.07` or later which has all the dependencies installed.
-
 ## Getting Started
 
 ### Set up your base models

examples/llm_ptq/README.md

@@ -25,17 +25,25 @@ This section focuses on Post-training quantization, a technique that reduces mod
 
 ## Pre-Requisites
 
+### Docker
+
+For Hugging Face models, please use the TensorRT-LLM docker image (e.g., `nvcr.io/nvidia/tensorrt-llm/release:1.1.0rc2.post2`).
+For NeMo models, use the NeMo container (e.g., `nvcr.io/nvidia/nemo:25.07`).
+Visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
+
+Also follow the installation steps below to upgrade to the latest version of Model Optimizer and install example-specific dependencies.
+
+### Local Installation
+
 For Hugging Face models, install Model Optimizer with `hf` dependencies using `pip` from [PyPI](https://pypi.org/project/nvidia-modelopt/) and install the requirements for the example:
 
 ```bash
-pip install nvidia-modelopt[hf]
+pip install -U nvidia-modelopt[hf]
 pip install -r requirements.txt
 ```
 
-If you want to deploy the quantized model on TRT-LLM, you will also need to install the TRT-LLM dependencies as per the [TRT-LLM documentation](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html#installation).
-Visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
-
-For NeMo models, use the NeMo container `nvcr.io/nvidia/nemo:25.04` or later which has all the dependencies including TRT-LLM installed.
+For TensorRT-LLM deployment, please use the TensorRT-LLM docker image or follow their [installation docs](https://nvidia.github.io/TensorRT-LLM/installation/index.html).
+Similarly, for vLLM or SGLang deployment, please use their installation docs.
 
 ## Getting Started
 

examples/llm_qat/README.md

@@ -22,17 +22,7 @@ Quantization Aware Training (QAT) helps to improve the model accuracy beyond pos
 
 ## Pre-Requisites
 
-For Hugging Face models, install Model Optimizer with `hf` dependencies using `pip` from [PyPI](https://pypi.org/project/nvidia-modelopt/) and install the requirements for the example:
-
-```bash
-pip install nvidia-modelopt[hf]
-pip install -r requirements.txt
-```
-
-If you want to deploy the quantized model on TRT-LLM, you will also need to install the TRT-LLM dependencies as per the [TRT-LLM documentation](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html#installation).
-Visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
-
-For NeMo models, use the NeMo container `nvcr.io/nvidia/nemo:25.04` or later which has all the dependencies including TRT-LLM installed.
+Please refer to the [llm_ptq/README.md](../llm_ptq/README.md#pre-requisites) for the pre-requisites.
 
 ## Getting Started
 

examples/onnx_ptq/README.md

@@ -24,14 +24,16 @@ Model Optimizer enables highly performant quantization formats including NVFP4,
 
 ### Docker
 
-Please refer to our [Installation Guide](../../README.md#installation) for recommended docker images.
+Please use the TensorRT docker image (e.g., `nvcr.io/nvidia/tensorrt:25.08-py3`) or visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
+
+Also follow the installation steps below to upgrade to the latest version of Model Optimizer and install example-specific dependencies.
 
 ### Local Installation
 
 Install Model Optimizer with `onnx` dependencies using `pip` from [PyPI](https://pypi.org/project/nvidia-modelopt/) and install the requirements for the example:
 
 ```bash
-pip install nvidia-modelopt[onnx]
+pip install -U nvidia-modelopt[onnx]
 pip install -r requirements.txt
 ```
 

examples/pruning/README.md

@@ -23,7 +23,7 @@ This section focuses on applying Model Optimizer's state-of-the-art complementar
 
 ## Pre-Requisites
 
-For Minitron pruning for Megatron-LM / NeMo models, use the NeMo container `nvcr.io/nvidia/nemo:25.07` or later which has all the dependencies installed.
+For Minitron pruning for Megatron-LM / NeMo models, use the NeMo container (e.g., `nvcr.io/nvidia/nemo:25.07`) which has all the dependencies installed.
 
 For FastNAS pruning for PyTorch Computer Vision models, no additional dependencies are required.
 

examples/speculative_decoding/README.md

@@ -25,16 +25,26 @@ This example focuses on training with Hugging Face. To train with Megatron‑LM,
 
 ## Pre-Requisites
 
+### Docker
+
+Please use the PyTorch docker image (e.g., `nvcr.io/nvidia/pytorch:25.06-py3`) or visit our [installation docs](https://nvidia.github.io/TensorRT-Model-Optimizer/getting_started/2_installation.html) for more information.
+
+Also follow the installation steps below to upgrade to the latest version of Model Optimizer and install dataset and example-specific dependencies.
+
+### Local Installation
+
 Install Modelopt with `hf` dependencies and other requirements for this example:
 
 ```bash
-pip install -e ...
+pip install -U nvidia-modelopt[hf]
 pip install -r requirements.txt
 ```
 
 We use [Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater) dataset in this example. Download by:
 
 ```bash
+apt-get update && apt-get install -y git-lfs
+git lfs install --system
 git clone https://huggingface.co/datasets/nvidia/Daring-Anteater
 ```