Update README.md

README.md

@@ -106,8 +106,36 @@ Refer to the Provided Files table below to see what files use which methods, and
 | [stablebeluga2.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/StableBeluga2-GGML/blob/main/stablebeluga2.ggmlv3.q5_0.bin) | q5_0 | 5 | 47.46 GB| 49.96 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
 | [stablebeluga2.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/StableBeluga2-GGML/blob/main/stablebeluga2.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 48.75 GB| 51.25 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 |
 | [stablebeluga2.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/StableBeluga2-GGML/blob/main/stablebeluga2.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 47.46 GB| 49.96 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
+| stablebeluga2.ggmlv3.q5_1.bin | q5_1 | 5 | 51.76 GB | 54.26 GB | Original quant method, 5-bit. Higher accuracy, slower inference than q5_0. |
+| stablebeluga2.ggmlv3.q6_K.bin | q6_K | 6 | 56.59 GB | 59.09 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
+| stablebeluga2.ggmlv3.q8_0.bin | q8_0 | 8 | 73.23 GB | 75.73 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
+
+### q5_1, q6_K and q8_0 files require expansion from archive
+
+**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the q6_K and q8_0 files as multi-part ZIP files. They are not compressed, they are just for storing a .bin file in two parts.
+
+### q5_1
+Please download:
+* `stablebeluga2.ggmlv3.q5_1.zip`
+* `stablebeluga2.ggmlv3.q5_1.z01`
+ 
+### q6_K 
+Please download:
+* `stablebeluga2.ggmlv3.q6_K.zip`
+* `stablebeluga2.ggmlv3.q6_K.z01`
+
+### q8_0
+Please download:
+* `stablebeluga2.ggmlv3.q8_0.zip`
+* `stablebeluga2.ggmlv3.q8_0.z01`
+
+Then extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use `7zip` - the basic `unzip` tool did not work. Example:
+```
+sudo apt update -y && sudo apt install 7zip
+7zz x stablebeluga2.ggmlv3.q6_K.zip
+```
 
-**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.
+Once the `.bin` is extracted you can delete the `.zip` and `.z01` files.
 
 ## How to run in `llama.cpp`