[ZENTORCH CORE] Update DESCRIPTION.md for 5.0

-- Update DESCRIPTION.md to support 5.0

Signed-off-by: durga <[email protected]>
Change-Id: Iad00d24c5e81c5c751bf50d599c2d0da77353390
Signed-off-by: durga <[email protected]>

Author: Venkata-Durga-Rao

DESCRIPTION.md

@@ -1,14 +1,16 @@
-**EARLY ACCESS:** The ZenDNN PyTorch* Plugin (zentorch) extends PyTorch* with an innovative upgrade that's set to revolutionize performance on AMD hardware.
+The latest ZenDNN Plugin for PyTorch* (zentorch) 5.0 is here!  
 
-As of version 5.0, AMD is unveiling a game-changing upgrade to ZenDNN, introducing a cutting-edge plug-in mechanism and an enhanced architecture under the hood. This isn't just about extensions; ZenDNN's aggressive AMD-specific optimizations operate at every level. It delves into comprehensive graph optimizations, including pattern identification, graph reordering, and seeking opportunities for graph fusions. At the operator level, ZenDNN boasts enhancements with microkernels, mempool optimizations, and efficient multi-threading on the large number of AMD EPYC cores. Microkernel optimizations further exploit all possible low-level math libraries, including AOCL BLIS.
+This powerful upgrade continues to redefine deep learning performance on AMD EPYC™ CPUs, combining relentless optimization, innovative features, and industry-leading support for modern workloads.
 
-The result? Enhanced performance with respect to baseline PyTorch*. zentorch leverages torch.compile, the latest PyTorch enhancement for accelerated performance. torch.compile makes PyTorch code run faster by JIT-compiling PyTorch code into optimized kernels, all while requiring minimal code changes and unlocking unprecedented speed and efficiency.
+zentorch 5.0 takes deep learning to new heights with significant enhancements for bfloat16 performance, expanded support for cutting-edge models like Llama 3.1 and 3.2, Microsoft Phi, and more as well as support for INT4 quantized datatype. This includes the advanced Activation-Aware Weight Quantization (AWQ) algorithm, driving remarkable accuracy in low-precision computations. 
 
-The ZenDNN PyTorch plugin is compatible with PyTorch version 2.1.2.
+Combined with PyTorch's torch.compile, zentorch transforms deep learning pipelines into finely-tuned, AMD-specific engines, delivering unparalleled efficiency and speed for large-scale inference workloads.  
 
+The zentorch 5.0 plugs seamlessly with PyTorch version 2.4.0, offering a high-performance experience for deep learning on AMD EPYC™ platforms.
+ 
 ## Support
 
-Please note that zentorch is currently in “Early Access” mode. We welcome feedback, suggestions, and bug reports. Should you have any of the these, please contact us on zendnn.maintainers@amd.com
+We welcome feedback, suggestions, and bug reports. Should you have any of the these, please kindly file an issue on the ZenDNN Plugin for PyTorch Github page [here](https://github.com/amd/ZenDNN-pytorch-plugin/issues) 
 
 ## License