Update README.md

README.md

@@ -65,7 +65,7 @@ The format for TinyCoT was:
 | [MPT 7B Instruct](https://hf.co/mosaicml/mpt-7b-instruct)              | **7B** | **Human**+Anthropic | SFT           |    2.05%       | 24.12%                                  | 11.01%                          |
 | [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%                   | 29.84%                          |
 | [StableLM Zephyr 3B](https://hf.co/stabilityai/stablelm-zephyr-3b)     | 3B     | GPT                 | DPO           |    possibly contaminated (45.72%)  | **33.31%**                   | 0.91%                           |
-| [**Memphis-CoT 3B**](https://hf.co/euclaise/memphis-cot-3b)            | 3B     | **Human**           | Self-teaching |    **13.8%**       | *26.24%*                            | **38.24%**                      |
+| [**Memphis-CoT 3B**](https://hf.co/euclaise/Memphis-CoT-3B)            | 3B     | **Human**           | Self-teaching |    **13.8%**       | *26.24%*                            | **38.24%**                      |
 *5-shot, as performed automatically by LM Evaluation Harness bbh_cot_fewshot even with num_fewshot=0
 
 Memphis outperforms other primarily-human-data models that are over twice its size, along with SFT models of its size, and trades with the Zephyr DPO model. That said, Zephyr uses synthetic data, and *much* more of it.