Add AVX2 support for x86 architectures thanks to @Const-me !

README.md

@@ -17,6 +17,7 @@ The main goal is to run the model using 4-bit quantization on a MacBook.
 
 - Plain C/C++ implementation without dependencies
 - Apple silicon first-class citizen - optimized via Arm Neon and Accelerate framework
+- AVX2 support for x86 architectures
 - Mixed F16 / F32 precision
 - 4-bit quantization support
 - Runs on the CPU
@@ -185,9 +186,6 @@ When running the larger models, make sure you have enough disk space to store al
   In general, it seems to work, but I think it fails for unicode character support. Hopefully, someone can help with that
 - I don't know yet how much the quantization affects the quality of the generated text
 - Probably the token sampling can be improved
-- x86 quantization support [not yet ready](https://github.com/ggerganov/ggml/pull/27). Basically, you want to run this
-  on Apple Silicon. For now, on Linux and Windows you can use the F16 `ggml-model-f16.bin` model, but it will be much
-  slower.
 - The Accelerate framework is actually currently unused since I found that for tensor shapes typical for the Decoder,
   there is no benefit compared to the ARM_NEON intrinsics implementation. Of course, it's possible that I simlpy don't
   know how to utilize it properly. But in any case, you can even disable it with `LLAMA_NO_ACCELERATE=1 make` and the