Update README.md

README.md

@@ -45,6 +45,31 @@ outputs = model.generate(**input_ids, max_new_tokens=100)
 print(tokenizer.decode(outputs[0]))
 ```
 
+## Model Details
+
+Zamba utilizes a unique hybrid SSM architecture. This architecture consists of a backbone of Mamba layers interspersed with a shared attention layer. 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. 
+
+
+<center>
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/IGK562oVTFSOQbpLavu7E.png" width="400" alt="Zamba architecture">
+</center>
+
+
+## Performance
+
+We find that Zamba performs significantly better than existing open models (with open datasets and training details) at this scale. However, it performs slightly worse than the leading open-weight models at the 7B scale. Most of this difference derives from MMLU and reasoning evaluations. Zamba, however, is trained on significantly fewer tokens than these models and is the most sample efficient model in terms of performance per training tokens.
+
+
+<center>
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/dQqaLzFwRKZOVSG3TfvsL.png" width="400" alt="Zamba performance">
+</center>
+
+Due to its SSM architecture, Zamba is extremely efficient in inference, substantially outperforming comparable 7B and 8B models in inference latency as well as memory cost of generation due to its substantially diminished KV cache.
+
+<center>
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/cghYPnDbdzweT1b2RyiXA.png" width="400" alt="Zamba performance">
+</center>
+
 ## Citation
 
 If you find Zamba useful in your work please cite it as: