Update README.md

README.md

@@ -18,7 +18,7 @@ https://www.kaggle.com/code/reginliu/perplexity
 | [causallm_7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/CausalLM-7B-GGUF/blob/main/causallm_7b.Q5_K_M.gguf) | 5.53 | 16.5278 +/- 0.18005 | 152064 | 16.5278 |
 | [Qwen1.5-22B-Chat-Merge-Q4_0.gguf](https://huggingface.co/DisOOM/Qwen1.5-22B-Chat-Merge-GGUF/blob/main/Qwen1.5-22B-Chat-Merge-Q4_0.gguf) | 12.6 | 21.9669 +/- 0.28980 | 152064 | 21.9669 |
 | [Kunoichi-DPO-v2-7B-Q4_K_M-imatrix.gguf](https://hf-mirror.com/Lewdiculous/Kunoichi-DPO-v2-7B-GGUF-Imatrix/blob/main/Kunoichi-DPO-v2-7B-Q4_K_M-imatrix.gguf) | 4.37 | 6.7096 +/- 0.04519 | 32000 | 31.8840 |
-
+| [WizardLM-2-7B-IQ4_XS-imat.gguf](https://huggingface.co/ABX-AI/WizardLM-2-7B-GGUF-IQ-Imatrix/blob/main/WizardLM-2-7B-IQ4_XS-imat.gguf) | 3.91 | 9.8891 +/- 0.08106 | 32000 | 46.9930 |
 For a model that returns tokens completely at random, we have
 $$ P(token|context) = \frac{1}{n_{vocab}}, \quad PPL = \sqrt[N]{\left(\frac{1}{P}\right)^N} = n_{vocab} $$
 therefore