Update README.md

README.md

@@ -88,7 +88,7 @@ Refer to the Provided Files table below to see what files use which methods, and
 | llama-65b.ggmlv3.q5_1.bin | q5_1 | 5 | 48.97 GB| 51.47 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
 | llama-65b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 46.20 GB| 48.70 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
 | llama-65b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 44.89 GB| 47.39 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
-| llama-65b.ggmlv3.q6_K.bin	| q6_K	|6	| |53.56 GB	| 56.06 GB	| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
+| llama-65b.ggmlv3.q6_K.bin	  | q6_K   |6  | 53.56 GB| 56.06 GB	| New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
 | llama-65b.ggmlv3.q8_0.bin	| q8_0	| 8	| 69.370 GB	| 71.87 GB	| Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
 
 **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.