Update README.md

README.md

@@ -50,10 +50,18 @@ print((tokenizer.decode(outputs[0])))
 
 ## Performance
 
-Zamba2-1.2B achieves leading and state-of-the-art performance among models of <2B parameters and is competitive with some models of significantly greater size. Moreover, due to its unique hybrid SSM architecture, Zamba2-1.2B achieves extremely low inference latency and rapid generation with a significantly smaller memory footprint than comparable transformer based models. 
+Zamba2-1.2B-Instruct achieves leading instruction-following and multi-turn chat performance for a model of its size and matches strong models significantly larger. For instance, Zamba2-1.2B-Instruct outperforms Gemma2-2B-Instruct, a very strong model over 2x its size. 
 
 <img src="https://cdn-uploads.huggingface.co/production/uploads/64e40335c0edca443ef8af3e/t7et3jazHNvxKSkeorZuo.png" width="600"/>
 
+| Model | Size | MT-Bench | IFEval |
+|-------------|----|----|----|
+| **Zamba2-1.2B-Instruct** | 1.2B | **59.53** | **41.45** |
+| Gemma2-2B-Instruct | 2.7B | 51.69 | 42.20 |
+| H2O-Danube-1.6B-Chat | 1.6B | 49.78 | 27.95 |
+| StableLM-1.6B-Chat | 1.6B | 49.87 | 33.77 |
+| 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.