Update README.md

README.md

@@ -7,7 +7,7 @@ license: apache-2.0
 
 Zamba2-1.2B-instruct is obtained from Zamba2-1.2B by fine-tuning on instruction-following and chat datasets.
 
-Zamba2-1.2B-Instruct is a hybrid model composed of state-space and transformer blocks. It is based on the [Zamba2-1.2B](https://huggingface.co/Zyphra/Zamba2-1.2B) architecture.
+Zamba2-1.2B-Instruct is a hybrid model composed of state-space ([Mamba2](https://github.com/state-spaces/mamba)) and transformer blocks. It is based on the [Zamba2-1.2B](https://huggingface.co/Zyphra/Zamba2-1.2B) architecture.
 
 ## Quick start
 
@@ -20,7 +20,7 @@ To download Zamba2-1.2B, clone Zyphra's fork of transformers:
 4. `pip install accelerate`
 
 
-You can run the model without using the optimized Mamba kernels, but it is **not** recommended as it will result in significantly higher latency and memory usage. 
+You can run the model without using the optimized Mamba2 kernels, but it is **not** recommended as it will result in significantly higher latency and memory usage. 
 
 To run on CPU, please specify `use_mamba_kernels=False` when loading the model using ``AutoModelForCausalLM.from_pretrained``.
 
@@ -63,7 +63,7 @@ Zamba2-1.2B-Instruct achieves leading instruction-following and multi-turn chat
 | SmolLM-1.7B-Instruct | 1.7B | 43.37 | 16.53 |
 | Qwen2-1.5B-Instruct | 1.5B | N/A | 34.68 |
 
-Zamba2-1.2B's high performance and small inference compute and memory footprint renders it an ideal generalist model for on-device applications.
+Moreover, due to its unique hybrid SSM architecture, Zamba2-1.2B-Instruct achieves extremely low inference latency and rapid generation with a significantly smaller memory footprint than comparable transformer-based models. 
 
 <center>
 <img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/iu46KgopP6rDrvDpXdlNj.png" width="700" alt="Zamba performance">
@@ -85,7 +85,7 @@ And memory overhead
 
 ## Model Details
 
-Zamba2-1.2B utilizes and extends our original Zamba hybrid SSM-attention architecture. The core Zamba architecture consists of a backbone of Mamba layers interleaved with one or more shared attention layers. This attention has shared weights to minimize the parameter cost of the model. We find that concatenating the original model embeddings to the input to this attention block improves performance, likely due to better maintenance of information across depth. The Zamba2 architecture also applies LoRA projection matrices to the shared transformer blocks to gain some additional expressivity in each block and allow each shared block to specialize slightly to its own unique position while keeping the additional parameter overhead small. 
+Zamba2-1.2B utilizes and extends our original Zamba hybrid SSM-attention architecture. The core Zamba architecture consists of a backbone of Mamba2 layers interleaved with one or more shared attention layers. This attention has shared weights to minimize the parameter cost of the model. We find that concatenating the original model embeddings to the input to this attention block improves performance, likely due to better maintenance of information across depth. The Zamba2 architecture also applies LoRA projection matrices to the shared transformer blocks to gain some additional expressivity in each block and allow each shared block to specialize slightly to its own unique position while keeping the additional parameter overhead small. 
 
 <center>
 <img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/Vay6htbnBcySR3Z6NEgwj.png" width="300" alt="Zamba architecture">