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 **MiniLM v1** (February 29, 2020): the pre-trained models for the paper entitled "[MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://arxiv.org/abs/2002.10957)". **Deep self-attention distillation is all you need** (for task-agnostic knowledge distillation of pre-trained Transformers). MiniLM (12-layer, 384-hidden) achieves 2.7x speedup and comparable results over BERT-Base (12-layer, 768-hidden) on **NLU** tasks as well as strong results on **NLG** tasks. The even smaller MiniLM (6-layer, 384-hidden) obtains 5.3x speedup and produces very competitive results.
 
 ## Pre-trained Models
-We release **uncased** **12**-layer and **6**-layer MiniLM models with **384** hidden size distilled from an in-house pre-trained [UniLM v2](/unilm) model in BERT-Base size. We also release **uncased** **6**-layer MiniLM model with **768** hidden size distilled from [BERT-Base](https://github.com/google-research/bert). The models use the same WordPiece vocabulary as BERT.
+We release the **uncased** **12**-layer and **6**-layer MiniLM models with **384** hidden size distilled from an in-house pre-trained [UniLM v2](/unilm) model in BERT-Base size. We also release **uncased** **6**-layer MiniLM model with **768** hidden size distilled from [BERT-Base](https://github.com/google-research/bert). The models use the same WordPiece vocabulary as BERT.
 
 The links to the pre-trained models:
 - MiniLMv1-L12-H384-uncased: 12-layer, 384-hidden, 12-heads, 33M parameters, 2.7x faster than BERT-Base