Add TMs in other tutorials

docs/how-to/llm-fine-tuning-optimization/fine-tuning-and-inference.rst

@@ -6,7 +6,7 @@
 Fine-tuning and inference
 *************************
 
-Fine-tuning using ROCm involves leveraging AMD's GPU-accelerated :doc:`libraries <rocm:reference/api-libraries>` and
+Fine-tuning using ROCm™ involves leveraging AMD's GPU-accelerated :doc:`libraries <rocm:reference/api-libraries>` and
 :doc:`tools <rocm:reference/rocm-tools>` to optimize and train deep learning models. ROCm provides a comprehensive
 ecosystem for deep learning development, including open-source libraries for optimized deep learning operations and
 ROCm-aware versions of :doc:`deep learning frameworks <../deep-learning-rocm>` such as PyTorch, TensorFlow, and JAX.

docs/how-to/llm-fine-tuning-optimization/index.rst

@@ -6,11 +6,11 @@
 Fine-tuning LLMs and inference optimization
 *******************************************
 
-ROCm empowers the fine-tuning and optimization of large language models, making them accessible and efficient for
+ROCm™ empowers the fine-tuning and optimization of large language models, making them accessible and efficient for
 specialized tasks. ROCm supports the broader AI ecosystem to ensure seamless integration with open frameworks,
 models, and tools.
 
-For more information, see `What is ROCm? <https://rocm.docs.amd.com/en/latest/what-is-rocm.html>`_
+For more information, see :doc:`What is ROCm? <../../what-is-rocm>`.
 
 Throughout the following topics, this guide discusses the goals and :ref:`challenges of fine-tuning a large language
 model <fine-tuning-llms-concept-challenge>` like Llama 2. Then, it introduces :ref:`common methods of optimizing your

docs/how-to/llm-fine-tuning-optimization/model-quantization.rst

@@ -161,6 +161,7 @@ kernels by configuring the ``exllama_config`` parameter as the following.
                            base_model_name,
                            device_map="auto",
                            quantization_config=gptq_config)
+
 bitsandbytes
 ============
 

docs/how-to/rocm-for-ai/index.rst

@@ -6,11 +6,11 @@
 Using ROCm for AI
 *****************
 
-ROCm offers a suite of optimizations for AI workloads from large language models (LLMs) to image and video detection and
+ROCm™ offers a suite of optimizations for AI workloads from large language models (LLMs) to image and video detection and
 recognition, life sciences and drug discovery, autonomous driving, robotics, and more. ROCm proudly supports the broader
 AI software ecosystem, including open frameworks, models, and tools.
 
-For more information, see `What is ROCm? <https://rocm.docs.amd.com/en/latest/what-is-rocm.html>`_
+For more information, see :doc:`What is ROCm? <../../what-is-rocm>`.
 
 In this guide, you'll learn about:
 

docs/how-to/rocm-for-ai/install.rst

@@ -8,7 +8,7 @@
 Installing ROCm and machine learning frameworks
 ***********************************************
 
-Before getting started, install ROCm and supported machine learning frameworks.
+Before getting started, install ROCm™ and supported machine learning frameworks.
 
 .. grid:: 1
 
@@ -21,7 +21,7 @@ Before getting started, install ROCm and supported machine learning frameworks.
 If you’re new to ROCm, refer to the :doc:`ROCm quick start install guide for Linux
 <rocm-install-on-linux:tutorial/quick-start>`.
 
-If you’re using a Radeon GPU for graphics-accelerated applications, refer to the
+If you’re using a Radeon™ GPU for graphics-accelerated applications, refer to the
 :doc:`Radeon installation instructions <radeon:docs/install/install-radeon>`.
 
 ROCm supports two methods for installation. There is no difference in the final ROCm installation between these two

docs/how-to/rocm-for-hpc/index.rst

@@ -13,12 +13,76 @@ while maintaining compatibility with industry software frameworks.
 For more information, see :doc:`What is ROCm? <../../what-is-rocm>`.
 
 Some of the most popular HPC frameworks are part of the ROCm platform, including
-those to help parallelize operations across multiple GPUs and servers, handle
-memory hierarchies, and solve linear systems.
+those to help parallelize operations across multiple acclerators and servers,
+handle memory hierarchies, and solve linear systems.
 
-Our GPU Accelerated Applications Catalog includes a vast set of
+Our catalog of GPU-accelerated applications includes a vast set of
 platform-compatible HPC applications, including those in astrophysics, climate 
 and weather, computational chemistry, computational fluid dynamics, earth
-science, genomics, geophysics, molecular dynamics, and physics. Many of these
-are available through the AMD Infinity Hub, build instructions ready for users
-to install and run on servers with AMD Instinct accelerators. 
+science, genomics, geophysics, molecular dynamics, and physics. Refer to the
+resources in the following table for ready-to-install build instructions and
+deployment suggestions for AMD Instinct accelerators.
+
+.. raw:: html
+
+   <style>
+     ul {
+       padding: 0;
+       list-style: none;
+     }
+   </style>
+
+.. list-table::
+   :header-rows: 1
+
+   * - Application domain
+     - HPC applications
+
+   * - Physics
+     - 
+       * `Chroma <https://github.com/amd/InfinityHub-CI/tree/main/chroma/`_
+       * `Grid <https://github.com/amd/InfinityHub-CI/tree/main/grid/`_
+       * `MILC <https://github.com/amd/InfinityHub-CI/tree/main/milc/`_
+       * `PIConGPU <https://github.com/amd/InfinityHub-CI/tree/main/picongpu`_
+
+   * - Astrophysics
+     - `Cholla <https://github.com/amd/InfinityHub-CI/tree/main/cholla/`_
+
+   * - Geophysics
+     - `Specfrem3D-Cartesian <https://github.com/amd/InfinityHub-CI/tree/main/specfem3d>`_
+
+   * - Molecular dynamics
+     - 
+       * `Gromacs with HIP (AMD implementation) <https://github.com/amd/InfinityHub-CI/tree/main/gromacs>`_
+       * `LAMMPS <https://github.com/amd/InfinityHub-CI/tree/main/lammps>`_
+
+   * - Computational fluid dynamics
+     -
+       * `NEKO <https://github.com/amd/InfinityHub-CI/tree/main/neko>`_
+       * `nekRS <https://github.com/amd/InfinityHub-CI/tree/main/nekrs>`_
+
+   * - Computational chemistry
+     - `QUDA <https://github.com/amd/InfinityHub-CI/tree/main/quda>`_
+   
+   * - Quantum Monte Carlo Simulation
+     - `QMCPACK <https://github.com/amd/InfinityHub-CI/tree/main/qmcpack>`_
+
+   * - Electronic structure
+     - `CP2K <https://github.com/amd/InfinityHub-CI/tree/main/cp2k>`_
+
+   * - Climate and weather
+     - `MPAS <https://github.com/amd/InfinityHub-CI/tree/main/mpas>`_
+
+   * - Benchmarking
+     -
+       * HPCG
+       * rocHPL
+       * rocHPL-MxP
+
+   * - Tools and libraries
+     -
+       * ROCm with GPU-aware MPI container
+       * Kokkos
+       * PyFR
+       * RAJA
+       * Trilinos