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Revise README for performance improvements and features #393

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# BitNet CPU Inference Optimization

This update provides significant performance improvements for BitNet inference on CPU through paralleled kernel implementations, native I2_S GEMM/GEMV support, configurable tiling block size and embedding quantization.
This update delivers significant performance improvements for BitNet inference on CPU through parallelized kernel implementations, native I2_S GEMM/GEMV support, configurable tiling block sizes, and embedding quantization

## Update

- **Parallel Weight & Activation Computation**
Implemented parallel processing of weights and activations in the W2A8 vet_dot kernel, achieving improved throughput on both x86 and ARM architectures.
- ** Parallel Weight & Activation Computation
Implemented parallel processing of weights and activations in the W2A8 vec_dot kernel, achieving higher throughput on both x86 and ARM architectures.
- ** Native I2_S GEMM & GEMV Support
Integrated I2_S GEMM and GEMV operations into the ggml library, ensuring full compatibility with the llama.cpp architecture. This enables seamless integration with existing inference pipelines.
- ** Configurable Tiling & Parallelism
Introduced configurable GEMM and GEMV block sizes along with adjustable parallelism levels, allowing fine‑tuned performance optimization across different CPU architectures.
- ** Embedding Quantization
Added support for embedding layer quantization using the Q6_K format, reducing memory footprint and improving inference speed while maintaining high accuracy.

- **Native I2_S GEMM & GEMV Support**
Integrated I2_S GEMM and GEMV operations into ggml library, making them fully compatible with the llama.cpp architecture. This enables seamless integration with existing inference pipelines.

- **Configurable Tiling & Parallelism**
Introduced configurable GEMM & GEMV block sizes and parallelism levels, allowing performance fine-tuning for different CPU architectures.

- **Embedding Quantization**
Added support for embedding layer quantization with Q6_K format, reducing memory footprint and improving inference speed while maintaining high accuracy.

## Usage

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