Fix wording and style

docs/advanced/hgq.rst

@@ -1,6 +1,6 @@
-==============
+===================================
 High Granularity Quantization (HGQ)
-==============
+===================================
 
 .. image:: https://github.com/calad0i/HGQ/actions/workflows/sphinx-build.yml/badge.svg
    :target: https://calad0i.github.io/HGQ/
@@ -9,13 +9,13 @@ High Granularity Quantization (HGQ)
 .. image:: https://img.shields.io/badge/arXiv-2405.00645-b31b1b.svg
    :target: https://arxiv.org/abs/2405.00645
 
-High Granularity Quantization (HGQ) is a gradient-based automatic bitwidth optimization and quantization-aware training algorithm for neural networks to be deployed on FPGAs, By laveraging gradients, it allows for bitwidth optimization at arbitrary granularity, up to per-weight and per-activation level.
+`High Granularity Quantization (HGQ) <https://github.com/calad0i/HGQ/>`_ is a library that performs gradient-based automatic bitwidth optimization and quantization-aware training algorithm for neural networks to be deployed on FPGAs. By laveraging gradients, it allows for bitwidth optimization at arbitrary granularity, up to per-weight and per-activation level.
 
 .. image:: https://calad0i.github.io/HGQ/_images/overview.svg
    :alt: Overview of HGQ
    :align: center
 
-Conversion of models from High Granularity Quantization (HGQ) is fully supported. The HGQ models are first converted to proxy model format, which can then be parsed by hls4ml bit-accurately. Below is an example of how to create a HGQ model and converting it to hls4ml.
+Conversion of models made with HGQ library is fully supported. The HGQ models are first converted to proxy model format, which can then be parsed by hls4ml bit-accurately. Below is an example of how to create a model with HGQ and convert it to hls4ml model.
 
 .. code-block:: Python