Update README.md

README.md

@@ -22,14 +22,14 @@ The models that were implemented are as follows:
 
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
-| [Q2_K Tiny](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q2_k.gguf) | Q2_K | 2 | 7.87 GB| 9.87 GB | smallest, significant quality loss - not recommended for most purposes |
-| [Q3_K_M](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q3_k_m.gguf) | Q3_K_M | 3 | 10.28 GB| 12.28 GB | very small, high quality loss |
-| [Q4_0](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q4_0.gguf) | Q4_0 | 4 | 13.3 GB| 15.3 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [Q4_K_M](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q4_k_m.gguf) | Q4_K_M | 4 | 13.32 GB| 15.32 GB | medium, balanced quality - recommended |
-| [Q5_0](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q5_0.gguf) | Q5_0 | 5 | 16.24 GB| 18.24 GB | legacy; large, balanced quality |
-| [Q5_K_M](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q5_k_m.gguf) | Q5_K_M | 5 | ~16.24 GB| ~18.24 GB | large, balanced quality - recommended |
-| [Q6 XL](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q6_k.gguf) | Q6_K | 6 | 19.35 GB| 21.35 GB | very large, extremely minor degradation |
-| [Q8 XXL](https://huggingface.co/Kquant03/FrankenDPO-4x7B-GGUF/blob/main/ggml-model-q8_0.gguf) | Q8_0 | 8 | 25.1 GB| 27.1 GB | very large, extremely minor degradation - not recommended |
+| [Q2_K Tiny](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q2_k.gguf) | Q2_K | 2 | 7.87 GB| 9.87 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 | 10.28 GB| 12.28 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 | 13.3 GB| 15.3 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 | 13.32 GB| 15.32 GB | medium, balanced quality - recommended |
+| [Q5_0](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q5_0.gguf) | Q5_0 | 5 | 16.24 GB| 18.24 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 | ~16.24 GB| ~18.24 GB | large, balanced quality - recommended |
+| [Q6 XL](https://huggingface.co/Kquant03/Buttercup-4x7B-GGUF/blob/main/ggml-model-q6_k.gguf) | Q6_K | 6 | 19.35 GB| 21.35 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 | 25.1 GB| 27.1 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.)