This part from 449-451 needs to be replaced with actual code:

# Step 1 
model.train() 
model.fuse_model() 
# Step 2 
model_ft = create_combined_model(model)
model_ft[0].qconfig = torch.quantization.default_qat_qconfig 
# Use default QAT configuration 
# Step 3 
model_ft = torch.quantization.prepare_qat(model_ft, inplace=True)

Minor comment, suggest replacing with:
Once steps 1-3 are done, we can start fine tuning the model (step 4), followed by converting the model into a quantized. (step 5).

Suggest merging this section (1.4) with 1.2 as we are explaining what we are doing in 1.2 here, suggested change is shown above.

The helper functions are built-in in transformers library. We mainly use
the following helper functions: one for converting the text examples
into the feature vectors; The other one for measuring the F1 score of
the predicted result.

The glue_convert_examples_to_features <https://github.com/huggingface/transformers/blob/master/transformers/data/processors/glue.py>_ function converts the texts into input features:

• Tokenize the input sequences;
• Insert [CLS] in the beginning;
• Insert [SEP] between the first sentence and the second sentence, and
  in the end;
• Generate token type ids to indicate whether a token belongs to the
  first sequence or the second sequence.

The glue_compute_metrics function computes the F1 score <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html>_
can be interpreted as a weighted average of the precision and recall,
where an F1 score reaches its best value at 1 and worst score at 0. The
relative contribution of precision and recall to the F1 score are equal.

• The equation for the F1 score is:
  .. math:: F1 = 2 * (\text{precision} * \text{recall}) / (\text{precision} + \text{recall})

@jianyuh : Does this look ok?