Update README.md

README.md

@@ -17,17 +17,17 @@ inference: false
 
 ## Model Details
 
-Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pre-trained model. Links to other models can be found in the index at the bottom.
+Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pre-trained model.
 
 
 ## About GPTQ (from HF Blog)
 
-Quantization methods usually belong into one of two categories: 
+Quantization methods usually belong to one of two categories: 
 
 1. Post-Training Quantization (PTQ): We quantize a pre-trained model using moderate resources, such as a calibration dataset and a few hours of computation.
 2. Quantization-Aware Training (QAT): Quantization is performed before training or further fine-tuning. 
 
-GPTQ falls into the PTQ category and this is particularly interesting for massive models, for which full model training or even fine-tuning can be very expensive.
+GPTQ falls into the PTQ category, and this is particularly interesting for massive models, for which full model training or even fine-tuning can be very expensive.
 
 Specifically, GPTQ adopts a mixed int4/fp16 quantization scheme where weights are quantized as int4 while activations remain in float16. During inference, weights are dequantized on the fly and the actual compute is performed in float16.