Update README.md

README.md

@@ -25,19 +25,21 @@ The config looks like this...(detailed version is in the files and versions):
 - [ConvexAI/Metabird-7B](https://huggingface.co/ConvexAI/Metabird-7B) - expert #3
 - [alnrg2arg/test3_sft_16bit](https://huggingface.co/alnrg2arg/test3_sft_16bit) - expert #4
 
-# I just now uploaded it to Open LLM Evaluations, just to see how it will do.
+# It manages to beat Buttercup-4x7B in MMLU, and I personally think it's on-par to it, if not better.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6589d7e6586088fd2784a12c/hQ44cGgs0cSf-sIv8Xk01.png)
 ## Provided files
 
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [Q2_K Tiny](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q2_k.gguf) | Q2_K | 2 | 8.84 GB| 10.84 GB | smallest, significant quality loss - not recommended for most purposes |
-| [Q3_K_M](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q3_k_m.gguf) | Q3_K_M | 3 | 11.6 GB| 13.6 GB | very small, high quality loss |
-| [Q4_0](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q4_0.gguf) | Q4_0 | 4 | 13.6 GB| 15.6 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [Q4_K_M](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q4_k_m.gguf) | Q4_K_M | 4 | 14.6 GB| 16.6 GB | medium, balanced quality - recommended |
-| [Q5_0](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q5_0.gguf) | Q5_0 | 5 | 16.6 GB| 18.6 GB | legacy; large, balanced quality |
-| [Q5_K_M](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q5_k_m.gguf) | Q5_K_M | 5 | 17.1 GB| 19.1 GB | large, balanced quality - recommended |
-| [Q6 XL](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q6_k.gguf) | Q6_K | 6 | 19.8 GB| 21.8 GB | very large, extremely minor degradation |
-| [Q8 XXL](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q8_0.gguf) | Q8_0 | 8 | 25.7 GB| 27.7 GB | very large, extremely minor degradation - not recommended |
+| [Q3_K_M](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q3_k_m.gguf) | Q3_K_M | 3 | 11.6 GB| 13.6 GB | very small, high quality loss |
+| [Q4_0](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q4_0.gguf) | Q4_0 | 4 | 13.6 GB| 15.6 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [Q4_K_M](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q4_k_m.gguf) | Q4_K_M | 4 | 14.6 GB| 16.6 GB | medium, balanced quality - recommended |
+| [Q5_0](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q5_0.gguf) | Q5_0 | 5 | 16.6 GB| 18.6 GB | legacy; large, balanced quality |
+| [Q5_K_M](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q5_k_m.gguf) | Q5_K_M | 5 | 17.1 GB| 19.1 GB | large, balanced quality - recommended |
+| [Q6 XL](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q6_k.gguf) | Q6_K | 6 | 19.8 GB| 21.8 GB | very large, extremely minor degradation |
+| [Q8 XXL](https://huggingface.co/ConvexAI/Harmony-4x7B-GGUF/blob/main/ggml-model-q8_0.gguf) | Q8_0 | 8 | 25.7 GB| 27.7 GB | very large, extremely minor degradation - not recommended |
 # "[What is a Mixture of Experts (MoE)?](https://huggingface.co/blog/moe)"
 ### (from the MistralAI papers...click the quoted question above to navigate to it directly.)