Cannot run quantization example
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**Describe the bug**
I tried to run the `basic_usage.py` file under `/examples/quantization` and reported an error.

**Steps/Code to reproduce bug**
`python basic_usage.py`

```shell
# error report info
  return self.fget.__get__(instance, owner)()
2024-03-26 12:05:10 INFO [auto_gptq.modeling._base] Start quantizing layer 1/12
2024-03-26 12:05:11 INFO [auto_gptq.modeling._base] Quantizing self_attn.k_proj in layer 1/12...
Traceback (most recent call last):
  File "/root/autoGPTQ/AutoGPTQ/examples/quantization/basic_usage_local.py", line 74, in <module>
    main()
  File "/root/autoGPTQ/AutoGPTQ/examples/quantization/basic_usage_local.py", line 29, in main
    model.quantize(examples)
  File "/usr/local/corex-3.2.1.20231222.40/lib64/python3/dist-packages/torch/utils/_contextlib.py", line 115, in decorate_context
    return func(*args, **kwargs)
  File "/root/anaconda3/envs/auto_gptq/lib/python3.10/site-packages/auto_gptq/modeling/_base.py", line 368, in quantize
    scale, zero, g_idx = gptq[name].fasterquant(
  File "/root/anaconda3/envs/auto_gptq/lib/python3.10/site-packages/auto_gptq/quantization/gptq.py", line 110, in fasterquant
    H = torch.linalg.cholesky(H)
RuntimeError: cusolver error: Unknown cusolver error number, when calling `cusolverDnCreate(handle)`. If you keep seeing this error, you may use `torch.backends.cuda.preferred_linalg_library()` to try linear algebra operators with other supported backends. See https://pytorch.org/docs/stable/backends.html#torch.backends.cuda.preferred_linalg_library
```


**Expected behavior**

A clear and concise description of what you expected to happen.

**Environment overview (please complete the following information)**

 - Environment location: `Docker`
 - Method of NeMo install: `BUILD_CUDA_EXT=0 pip install auto-gptq==0.5.1`
 - If method of install is [Docker], provide `docker pull` & `docker run` commands used

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: `Linux aarch64`
- PyTorch version: `2.1.0`
- Python version: `3.10`

**Additional context**

Add any other context about the problem here.
Example: GPU model
