Update README.md (#63)

alltogether -> altogether

README.md

@@ -8,7 +8,7 @@ MegaBlocks is built on top of [Megatron-LM](https://github.com/NVIDIA/Megatron-L
 
 ![MegaBlocks Performance](media/dropping_end_to_end.png)
 
-MegaBlocks dMoEs outperform MoEs trained with [Tutel](https://github.com/microsoft/tutel) by up to **40%** compared to Tutel's best performing `capacity_factor` configuration. MegaBlocks dMoEs use a reformulation of MoEs in terms of block-sparse operations, which allows us to avoid token dropping without sacrificing hardware efficiency. In addition to being faster, MegaBlocks simplifies MoE training by removing the `capacity_factor` hyperparameter alltogether. Compared to dense Transformers trained with [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), MegaBlocks dMoEs can accelerate training by as much as **2.4x**. Check out our [paper](https://arxiv.org/abs/2211.15841) for more details!
+MegaBlocks dMoEs outperform MoEs trained with [Tutel](https://github.com/microsoft/tutel) by up to **40%** compared to Tutel's best performing `capacity_factor` configuration. MegaBlocks dMoEs use a reformulation of MoEs in terms of block-sparse operations, which allows us to avoid token dropping without sacrificing hardware efficiency. In addition to being faster, MegaBlocks simplifies MoE training by removing the `capacity_factor` hyperparameter altogether. Compared to dense Transformers trained with [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), MegaBlocks dMoEs can accelerate training by as much as **2.4x**. Check out our [paper](https://arxiv.org/abs/2211.15841) for more details!
 
 # :building_construction: Installation