Update README.md

README.md

@@ -65,7 +65,7 @@ The format for TinyCoT was:
 | [MPT 7B Instruct](mosaicml/mpt-7b-instruct)                            | **7B** | **Human**+Anthropic | SFT           |    2.05%       | 24.12%                                  |
 | [OpenLLaMA 7B v2 open-instruct](http://hf.co/VMware/open-llama-7b-v2-open-instruct) | **7B** | **Human** (nearly: ecqa is an exception) | SFT | 8.64% | 23.21%                   |
 | [StableLM Zephyr 3B](https://hf.co/stabilityai/stablelm-zephyr-3b)     | 3B     | GPT                 | DPO           |    45.72% (contaminated)  | **33.31%**                              |
-| [**Memphis-CoT 3B**](https://hf.co/euclaise/memphis-cot-3b)            | 3B     | **Human**           | Self-teaching |    *13.8%*       | *26.24%*                                |
+| [**Memphis-CoT 3B**](https://hf.co/euclaise/memphis-cot-3b)            | 3B     | **Human**           | Self-teaching |    **13.8%**       | *26.24%*                                |
 
 
 Memphis outperforms human-data models that are over twice its size, along with SFT models of its size, but doesn't quite reach the performance of the Zephyr DPO model. That said, Zephyr uses synthetic data, and *much* more of it.