Clarify hidden size and split model details

README.md

@@ -14,7 +14,9 @@ An experimental 1,301,120-parameter language model trained with DeepJ. On 100 de
 
 ## Model details
 
-This compact language model was created with [DeepJ](https://github.com/KirstenAli/DeepJ) and uses a DeepSeek-style Transformer architecture with 4 layers, a width of 128, 4 attention heads, Q rank 64, KV rank 32 and a 2,048-token vocabulary. It is not an exact implementation of DeepSeek V2, V3 or R1 and does not currently use an incremental KV cache.
+This compact language model was created with [DeepJ](https://github.com/KirstenAli/DeepJ) and uses a DeepSeek-style Transformer architecture with 4 layers, a hidden size of 128, 4 attention heads, Q rank 64, KV rank 32 and a 2,048-token vocabulary.
+
+It is not an exact implementation of DeepSeek V2, V3 or R1 and does not currently use an incremental KV cache.
 The `model.dj` checkpoint and `tokenizer.bpe` files use DeepJ's versioned binary formats.
 They are not directly loadable by the Python Transformers library.