A few small fixes.

README.md

@@ -1,7 +1,7 @@
 # <div align="left"><img src="https://rapids.ai/assets/images/rapids_logo.png" width="90px"/>&nbsp;RAFT: Reusable Accelerated Functions and Tools for Vector Search and More</div>
 
 > [!IMPORTANT]
-> The vector search and clustering algorithms in RAFT have been formally migrated to a new library dedicated to vector search called [cuVS](https://github.com/rapidsai/cuvs). The headers for the vector search and clustering algorithms in RAFT will remain for a bried period, but will no longer be tested, benchmarked, included in the pre-compiled libraft binary, or otherwise updated after the 24.12 (December 2024) release. We will be removing these headers altogether in a future release. It is strongly suggested to use cuVS for these routines, which include any headers in the `distance`, `neighbors`, `cluster` and `spatial` directories, and use the RAFT versions at your own risk. 
+> The vector search and clustering algorithms in RAFT have been formally migrated to a new library dedicated to vector search called [cuVS](https://github.com/rapidsai/cuvs). The headers for the vector search and clustering algorithms in RAFT will remain for a bried period, but will no longer be tested, benchmarked, included in the pre-compiled libraft binary, or otherwise updated after the 24.12 (December 2024) release. We will be removing these headers altogether in a future release. It is strongly suggested to use cuVS for these routines, which include any headers in the `distance`, `neighbors`, `cluster` and `spatial` directories, and use the RAFT versions at your own risk.
 
 ![RAFT tech stack](img/raft-tech-stack-vss.png)
 
@@ -119,7 +119,7 @@ auto metric = raft::distance::DistanceType::L2SqrtExpanded;
 raft::distance::pairwise_distance(handle, input.view(), input.view(), output.view(), metric);
 ```
 
-It's also possible to create `raft::device_mdspan` views to invoke the same API with raw pointers and shape information. Take this example from the [Nvidia cuVS](https://github.com/rapidsai/cuvs) library:
+It's also possible to create `raft::device_mdspan` views to invoke the same API with raw pointers and shape information. Take this example from the [NVIDIA cuVS](https://github.com/rapidsai/cuvs) library:
 
 ```c++
 #include <raft/core/device_resources.hpp>
@@ -155,7 +155,7 @@ cuvs::distance::pairwise_distance(handle, input_view, input_view, output_view, m
 
 The `pylibraft` package contains a Python API for RAFT algorithms and primitives. `pylibraft` integrates nicely into other libraries by being very lightweight with minimal dependencies and accepting any object that supports the `__cuda_array_interface__`, such as [CuPy's ndarray](https://docs.cupy.dev/en/stable/user_guide/interoperability.html#rmm). The number of RAFT algorithms exposed in this package is continuing to grow from release to release.
 
-The example below demonstrates computing the pairwise Euclidean distances between CuPy arrays using the [Nvidia cuVS](https://github.com/rapidsai/cuvs) library. Note that CuPy is not a required dependency for `pylibraft`.
+The example below demonstrates computing the pairwise Euclidean distances between CuPy arrays using the [NVIDIA cuVS](https://github.com/rapidsai/cuvs) library. Note that CuPy is not a required dependency for `pylibraft`.
 
 ```python
 import cupy as cp
@@ -276,7 +276,7 @@ When citing RAFT generally, please consider referencing this Github project.
   title={Rapidsai/raft: RAFT contains fundamental widely-used algorithms and primitives for data science, Graph and machine learning.},
   url={https://github.com/rapidsai/raft},
   journal={GitHub},
-  publisher={Nvidia RAPIDS},
+  publisher={NVIDIA RAPIDS},
   author={Rapidsai},
   year={2022}
 }

conda/recipes/libraft/meta.yaml

@@ -321,4 +321,4 @@ outputs:
     about:
       home: https://rapids.ai/
       license: Apache-2.0
-      summary: libraft tests
+      summary: libraft tests

docs/source/build.md

@@ -103,9 +103,9 @@ mamba env create --name rapids_raft -f conda/environments/all_cuda-125_arch-x86_
 mamba activate rapids_raft
 ```
 
-All of RAFT's C++ APIs can be used header-only
+All of RAFT's C++ APIs can be used header-only.
 
-The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment
+The process for building from source with CUDA 11 differs slightly in that your host system will also need to have CUDA toolkit installed which is greater than, or equal to, the version you install into you conda environment. Installing CUDA toolkit into your host system is necessary because `nvcc` is not provided with Conda's cudatoolkit dependencies for CUDA 11. The following example will install create and install dependencies for a CUDA 11.8 conda environment:
 ```bash
 mamba env create --name rapids_raft -f conda/environments/all_cuda-118_arch-x86_64.yaml
 mamba activate rapids_raft
@@ -133,7 +133,7 @@ Once installed, `libraft` headers (and dependencies which were downloaded and in
 
 ### C++ Shared Library (optional)
 
-A shared library must be built in order to build `pylibraft`. The shared library also contains a runtime API that allows Pass the `--compile-lib` flag to `build.sh` to build the library:
+A shared library must be built in order to build `pylibraft`. Pass the `--compile-lib` flag to `build.sh` to build the library:
 ```bash
 ./build.sh libraft --compile-lib
 ```

docs/source/quick_start.md

@@ -23,7 +23,7 @@ auto vector = raft::make_device_vector<float>(handle, n_cols);
 auto matrix = raft::make_device_matrix<float>(handle, n_rows, n_cols);
 ```
 
-The `mdspan` is a lightweight non-owning view that can wrap around any pointer, maintaining shape, layout, and indexing information for accessing elements. 
+The `mdspan` is a lightweight non-owning view that can wrap around any pointer, maintaining shape, layout, and indexing information for accessing elements.
 
 
 We can construct `mdspan` instances directly from the above `mdarray` instances:
@@ -95,7 +95,7 @@ auto vector = raft::make_device_vector_view(vector_ptr, raft::make_vector_stride
 Most of the primitives in RAFT accept a `raft::handle_t` object for the management of resources which are expensive to create, such CUDA streams, stream pools, and handles to other CUDA libraries like `cublas` and `cusolver`.
 
 The example below demonstrates creating a RAFT handle and using it with `device_matrix` and `device_vector` to allocate memory, generating random clusters, and computing
-pairwise Euclidean distances with [Nvidia cuVS](https://github.com/rapidsai/cuvs):
+pairwise Euclidean distances with [NVIDIA cuVS](https://github.com/rapidsai/cuvs):
 
 ```c++
 #include <raft/core/handle.hpp>
@@ -122,7 +122,7 @@ cuvs::distance::pairwise_distance(handle, input.view(), input.view(), output.vie
 
 The `pylibraft` package contains a Python API for RAFT algorithms and primitives. `pylibraft` integrates nicely into other libraries by being very lightweight with minimal dependencies and accepting any object that supports the `__cuda_array_interface__`, such as [CuPy's ndarray](https://docs.cupy.dev/en/stable/user_guide/interoperability.html#rmm). The number of RAFT algorithms exposed in this package is continuing to grow from release to release.
 
-The example below demonstrates computing the pairwise Euclidean distances between CuPy arrays with the [Nvidia cuVS](https://github.com/rapidsai/cuvs) library. Note that CuPy is not a required dependency for `pylibraft`.
+The example below demonstrates computing the pairwise Euclidean distances between CuPy arrays with the [NVIDIA cuVS](https://github.com/rapidsai/cuvs) library. Note that CuPy is not a required dependency for `pylibraft`.
 
 ```python
 import cupy as cp