Update README.md

README.md

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-# `KrylovPreconditioners`.jl
+# KrylovPreconditioners.jl
 
 | **Documentation** | **CI** | **Coverage** | **Downloads** |
 |:-----------------:|:------:|:------------:|:-------------:|
@@ -14,11 +14,33 @@
 [build-cirrus-url]: https://cirrus-ci.com/github/JuliaSmoothOptimizers/KrylovPreconditioners.jl
 [codecov-img]: https://codecov.io/gh/JuliaSmoothOptimizers/KrylovPreconditioners.jl/branch/main/graph/badge.svg
 [codecov-url]: https://app.codecov.io/gh/JuliaSmoothOptimizers/KrylovPreconditioners.jl
-[downloads-img]: https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/KrylovPreconditioners
-[downloads-url]: https://pkgs.genieframework.com?packages=KrylovPreconditioners
+[downloads-img]: https://img.shields.io/badge/dynamic/json?url=http%3A%2F%2Fjuliapkgstats.com%2Fapi%2Fv1%2Fmonthly_downloads%2FKrylovPreconditioners&query=total_requests&suffix=%2Fmonth&label=Downloads
+[downloads-url]: https://juliapkgstats.com/pkg/KrylovPreconditioners
 
 ## How to Cite
 
-If you use KrylovPreconditioners.jl in your work, please cite using the format given in [`CITATION.cff`](https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl/blob/main/CITATION.cff).
+If you use KrylovPreconditioners.jl in your work, please cite it using the format provided in [`CITATION.cff`](https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl/blob/main/CITATION.cff).
+
+## How to Install
+
+To get started with `KrylovPreconditioners.jl`, you can install it using Julia's package manager:
+
+```julia
+julia> ]
+pkg> add KrylovPreconditioners
+```
+
+To use the package alongside `Krylov.jl`, simply import both packages:
+
+```julia
+using Krylov, KrylovPreconditioners
+```
+
+## Content
+
+To enhance the performance of [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl), especially on GPUs, we recommend `KrylovPreconditioners.jl`.
+This package provides a variety of preconditioning strategies that significantly improve convergence rates for Krylov solvers, making them more efficient for large-scale problems.
+It also contains operators that improve the efficiency of sparse matrix-dense vector products and sparse triangular solves on different GPUs, ensuring better performance on modern hardware.
+
+[KrylovPreconditioners.jl](https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl) is the best sidekick of [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl). └(^o^ )Ｘ( ^o^)┘
 
-The best sidekick of [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) └(^o^ )Ｘ( ^o^)┘