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+# Model
+miniALBERT is a recursive transformer model which uses cross-layer parameter sharing, embedding factorisation, and bottleneck adapters to achieve high parameter efficiency.
+Since miniALBERT is a compact model, it is trained using a layer-to-layer distillation technique, using the bert-base model as the teacher. Currently, this model is trained for one epoch on the English subset of Wikipedia.
+In terms of architecture, this model uses an embedding dimension of 128, a hidden size of 768, an MLP expansion rate of 4, and a reduction factor of 16 for bottleneck adapters. In general, this model uses 6 recursions and has a unique parameter count of 11 million parameters.