Update README.md

README.md

@@ -46,7 +46,6 @@ 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 FP8 and floating point representations for each output channel dimension.
 Activations are quantized with a symmetric per-tensor scheme, where a fixed linear scaling factor is applied between FP8 and floating point representations for the entire activation tensor.
-Linear scaling factors are computed via by minimizing the mean squarred error (MSE).
 Weights are quantized by rounding to nearest FP8 representation.
 The [llm-compressor](https://github.com/vllm-project/llm-compressor) library was applied to quantize the model, usin 512 sequences sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration).
 
@@ -112,7 +111,6 @@ recipe = QuantizationModifier(
     targets="Linear",
     scheme="FP8",
     ignore=["lm_head"],
-    observer="mse",
   )
 ]