- Automatic mechanism to transform existing models to quantized versions, with customizable bit-width configuration for different layers. No need to re-write the model for different quantization methods.
- Post-training quantization of trained full-precision models
- Post-training quantization of trained full-precision models, dynamic and static (statistics-based)
- Support for [quantization-aware training](https://nervanasystems.github.io/distiller/quantization/index.html#quantization-aware-training) in the loop
***Knowledge distillation**
- Training with [knowledge distillation](https://nervanasystems.github.io/distiller/knowledge_distillation/index.html), in conjunction with the other available pruning / regularization / quantization methods.
