# Post-Training Quantization of a Language Model using Distiller

A detailed, Jupyter Notebook based tutorial on this topic is located at `<distiller_repo_root>/examples/word_language_model/quantize_lstm.ipynb`.
You can view a "read-only" version of it in the Distiller GitHub repository here.

The tutorial covers the following:

- Converting the model to use Distiller's modular LSTM implementation, which allows flexible quantization of internal LSTM operations.
- Collecting activation statistics prior to quantization
- Creating a `PostTrainLinearQuantizer` and preparing the model for quantization
- "Net-aware quantization" capability of `PostTrainLinearQuantizer`
- Progressively tweaking the quantization settings in order to improve accuracy

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