spelling fixes to readme

README.md

@@ -41,7 +41,7 @@ This code is written for modern PyTorch (version 2.7 or newer) using DTensor-bas
 
 ## Quick Start
 
-Install dependences:
+Install dependencies:
 ```bash
 pip install -r requirements.txt
 ```
@@ -139,7 +139,7 @@ We summarize the above in this table. Let `d_in` be the input dimension of the u
 | Embedding     | `nn.Embedding.weight`                       | `"lion"` / `"adamw"`  | `lr`                   |
 | Unembedding   | `nn.Linear.weight` (must identify manually) | `"lion"` / `"adamw"`  | `lr / math.sqrt(d_in)` |
 
-We emphasize again that **particular care** needs to be taken with **embedding and unembedding layers**. They must be isolated from ordinary matrix parameters, and the unembedding layer futhermore should use a scaled learning rate. Merely checking the dimensions of a parameter (such as `if p.ndim == 2`) or the type of the module (such as `if isinstance(module, nn.Linear)`) **is not sufficient** to identify these special parameters. This is why we require manual parameter group creation.
+We emphasize again that **particular care** needs to be taken with **embedding and unembedding layers**. They must be isolated from ordinary matrix parameters, and the unembedding layer furthermore should use a scaled learning rate. Merely checking the dimensions of a parameter (such as `if p.ndim == 2`) or the type of the module (such as `if isinstance(module, nn.Linear)`) **is not sufficient** to identify these special parameters. This is why we require manual parameter group creation.
 
 The optimizer cannot tell if a given parameter is a weight matrix, embedding, or unembedding, because they are all two-dimensional tensors. You will not receive any errors if these parameters are incorrectly grouped with matrix weights!
 
@@ -242,7 +242,7 @@ optimizer = Dion(
 
 Muon uses different device mesh arguments from Dion.
 
-Our implementation of Muon takes a single 1D device mesh as a generic `distributed_mesh` argument. If this mesh is used for sharding parameters, Muon will efficiently perform unsharding using all-to-all. If this mesh is not used for sharding, Muon will distribue work across this mesh and all-gather the final results.
+Our implementation of Muon takes a single 1D device mesh as a generic `distributed_mesh` argument. If this mesh is used for sharding parameters, Muon will efficiently perform unsharding using all-to-all. If this mesh is not used for sharding, Muon will distribute work across this mesh and all-gather the final results.
 
 2D sharding is not supported by Muon---use Dion instead. For hybrid-sharded data parallel, with a replicated mesh dimension and a sharded dimension, pass only the sharded sub-mesh to Muon.
 
@@ -457,3 +457,5 @@ If you use Dion in your research, please cite:
   year={2025}
 }
 ``` 
+
+LocalWords:  Orthonormal