Update README.md

README.md

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 [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).
 
 The best sidekick of [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) └(^o^ )Ｘ( ^o^)┘
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+Boosting Performance with KrylovPreconditioners.jl
+
+Voici les corrections :
+
+To further enhance the performance of [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl), especially on GPUs, we recommend using `KrylovPreconditioners.jl`.
+This package provides a variety of preconditioning strategies that can significantly improve convergence rates for Krylov solvers, making them even more efficient for large-scale problems.
+It also contains operators to perform multiple sparse matrix-dense vector products, as well as triangular solves, more efficiently on various GPUs.