Update README.md

README.md

@@ -35,10 +35,8 @@ Weight quantization also reduces disk size requirements by approximately 50%.
 Only weights and activations of the linear operators within transformers blocks are quantized.
 Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension.
 Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations.
-Linear scaling factors are computed via by minimizong the mean squarred error (MSE).
-The [SmoothQuant](https://arxiv.org/abs/2211.10438) algorithm is used to alleviate outliers in the activations, whereas rhe [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization.
-Both algorithms are implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.
-GPTQ used a 1% damping factor and 256 sequences sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
+Linear scaling factors are computed via by minimizing the mean squarred error (MSE).
+The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library.GPTQ used a 1% damping factor and 256 sequences sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
 
 ## Deployment