Update README.md

Revised M1/M2 installation instructions.

README.md

@@ -207,7 +207,7 @@ Follow this [page](https://linuxconfig.org/how-to-install-the-nvidia-drivers-on-
 
 1- Follow this [page](https://developer.apple.com/metal/pytorch/) to build up PyTorch with Metal Performance Shaders (MPS) support. PyTorch uses the new MPS backend for GPU training acceleration. It is good practice to verify mps support using a simple Python script as mentioned in the provided link.
 
-2- By following the page, here is an example of you may initiate in your terminal
+2- By following the page, here is an example of what you may initiate in your terminal
 
 ```shell
 xcode-select --install
@@ -220,45 +220,9 @@ pip install pdfminer.six
 pip install xformers
 ```
 
-3- Create a new `verifymps.py` in the same directory (localGPT) where you have all files and environment.
+3- Please keep in mind that the quantized models are not yet supported by Apple Silicon (M1/M2) by auto-gptq library that is being used for loading quantized models, [see here](https://github.com/PanQiWei/AutoGPTQ/issues/133#issuecomment-1575002893). Therefore, you will not be able to run quantized models on M1/M2.
 
- import torch
- if torch.backends.mps.is_available():
-  mps_device = torch.device("mps")
-  x = torch.ones(1, device=mps_device)
-  print (x)
- else:
-  print ("MPS device not found.")
 
-4- Find `instructor.py` and open it in VS Code to edit.
-
-The `instructor.py` is probably embeded similar to this:
-
- file_path = "/System/Volumes/Data/Users/USERNAME/anaconda3/envs/LocalGPT/lib/python3.10/site-packages/InstructorEmbedding/instructor.py"
-
-You can open the `instructor.py` and then edit it using this code:
-
-#### Open the file in VSCode
-
- subprocess.run(["open", "-a", "Visual Studio Code", file_path])
-
-Once you open `instructor.py` with VS Code, replace the code snippet that has `device_type` with the following codes:
-
-  if device is None:
-  device = self._target_device
-
-  # Replace the line: self.to(device)
-
-  if device in ['cpu', 'CPU']:
-  device = torch.device('cpu')
-
-  elif device in ['mps', 'MPS']:
-  device = torch.device('mps')
-
-  else:
-  device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-
-  self.to(device)
 
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