fix doc link (#2344)

* .rst link fix

* bug fix in docstring

---------

Co-authored-by: Jing Xu <jing.xu@intel.com>

Author: ZailiWang

docs/index.rst

@@ -10,7 +10,7 @@ Optimizations take advantage of Intel® Advanced Vector Extensions 512 (Intel®
 Moreover, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs through the PyTorch* ``xpu`` device.
 
 In the current technological landscape, Generative AI (GenAI) workloads and models have gained widespread attention and popularity. Large Language Models (LLMs) have emerged as the dominant models driving these GenAI applications. Starting from 2.1.0, specific optimizations for certain 
-LLM models are introduced in the Intel® Extension for PyTorch*. For more information on LLM optimizations, refer to the `Large Language Models (LLM) <llm.html>`_ section.
+LLM models are introduced in the Intel® Extension for PyTorch*. For more information on LLM optimizations, refer to the `Large Language Models (LLM) <tutorials/llm.rst>`_ section.
 
 The extension can be loaded as a Python module for Python programs or linked as a C++ library for C++ programs. In Python scripts, users can enable it dynamically by importing ``intel_extension_for_pytorch``.
 

intel_extension_for_pytorch/quantization/_qconfig.py

@@ -72,9 +72,8 @@ def get_smooth_quant_qconfig_mapping(
             For nn.Linear with SmoothQuant enabled, it calculates q-params
             after applying scaling factors. PerChannelMinMaxObserver by
             default.
-            Example: ``torch.ao.quantization.PerChannelMinMaxObserver.with_args(
-                dtype=torch.qint8, qscheme=torch.per_channel_symmetric
-            )``
+            Example: ``torch.ao.quantization.PerChannelMinMaxObserver.with_args(\
+dtype=torch.qint8, qscheme=torch.per_channel_symmetric)``
         wei_ic_observer: Per-input-channel Observer for weight.
             For nn.Linear with SmoothQuant enabled only.
             PerChannelMinMaxObserver by default.