Merge pull request #907 from m0nsky/readme-opencl-vulkan

Remove OpenCL & add Vulkan to readme

README.md

@@ -8,7 +8,7 @@
 [![LLamaSharp Badge](https://img.shields.io/nuget/v/LLamaSharp.Backend.Cuda12?label=LLamaSharp.Backend.Cuda12)](https://www.nuget.org/packages/LLamaSharp.Backend.Cuda12)
 [![LLamaSharp Badge](https://img.shields.io/nuget/v/LLamaSharp.semantic-kernel?label=LLamaSharp.semantic-kernel)](https://www.nuget.org/packages/LLamaSharp.semantic-kernel)
 [![LLamaSharp Badge](https://img.shields.io/nuget/v/LLamaSharp.kernel-memory?label=LLamaSharp.kernel-memory)](https://www.nuget.org/packages/LLamaSharp.kernel-memory)
-[![LLamaSharp Badge](https://img.shields.io/nuget/v/LLamaSharp.Backend.OpenCL?label=LLamaSharp.Backend.OpenCL)](https://www.nuget.org/packages/LLamaSharp.Backend.OpenCL)
+[![LLamaSharp Badge](https://img.shields.io/nuget/v/LLamaSharp.Backend.Vulkan?label=LLamaSharp.Backend.Vulkan)](https://www.nuget.org/packages/LLamaSharp.Backend.Vulkan)
 
 
 **LLamaSharp is a cross-platform library to run 🦙LLaMA/LLaVA model (and others) on your local device. Based on [llama.cpp](https://github.com/ggerganov/llama.cpp), inference with LLamaSharp is efficient on both CPU and GPU. With the higher-level APIs and RAG support, it's convenient to deploy LLMs (Large Language Models) in your application with LLamaSharp.**
@@ -83,7 +83,7 @@ The following examples show how to build APPs with LLamaSharp.
 
 ### Installation
 
-To gain high performance, LLamaSharp interacts with native libraries compiled from c++, these are called `backends`. We provide backend packages for Windows, Linux and Mac with CPU, CUDA, Metal and OpenCL. You **don't** need to compile any c++, just install the backend packages.
+To gain high performance, LLamaSharp interacts with native libraries compiled from c++, these are called `backends`. We provide backend packages for Windows, Linux and Mac with CPU, CUDA, Metal and Vulkan. You **don't** need to compile any c++, just install the backend packages.
 
 If no published backend matches your device, please open an issue to let us know. If compiling c++ code is not difficult for you, you could also follow [this guide](./docs/ContributingGuide.md) to compile a backend and run LLamaSharp with it.
 
@@ -189,7 +189,7 @@ Firstly, due to the large size of LLM models, it requires more time to generate
 
 To see if that's a LLamaSharp performance issue, please follow the two tips below.
 
-1. If you are using CUDA, Metal or OpenCL, please set `GpuLayerCount` as large as possible.
+1. If you are using CUDA, Metal or Vulkan, please set `GpuLayerCount` as large as possible.
 2. If it's still slower than you expect it to be, please try to run the same model with same setting in [llama.cpp examples](https://github.com/ggerganov/llama.cpp/tree/master/examples). If llama.cpp outperforms LLamaSharp significantly, it's likely a LLamaSharp BUG and please report that to us.