Update README.md

README.md

@@ -11,7 +11,7 @@ Zamba2-2.7B is a hybrid model composed of state-space and transformer blocks. It
 
 3.) We apply a LoRA projector to each shared MLP block, which allows the network to specialize the MLPs at each invocation of the shared layer across depth. LoRA enables us to add depth-specialization for only a minimal increase in total parameter count.
 
-Zamba was trained using next-token prediction. It uses the Mistral v0.1 tokenizer. Zamba2-2.7B was pre-trained on 3T tokens of text and code data sourced from open web-datasets, including [Zyda](https://arxiv.org/abs/2406.01981). Subsequently, in a second phase, Zamba2-2.7B was annealed on a mixture of 100B high-quality tokens.
+Zamba2-2.7B uses the Mistral v0.1 tokenizer and was pre-trained on 3T tokens of text and code data sourced from open web-datasets, including [Zyda](https://arxiv.org/abs/2406.01981). Subsequently, in a second phase, Zamba2-2.7B was annealed on a mixture of 100B high-quality tokens.
 
 Note: this is a temporary HuggingFace implementation of Zamba2-2.7B. It may not yet be fully compatible with all frameworks and tools intended to interface with HuggingFace models.