Update README.md

README.md

@@ -38,6 +38,7 @@ This repo contains GGML format model files for [Mikael110's Llama2 70b Guanaco Q
 CUDA GPU acceleration is now available for Llama 2 70B GGML files. Metal acceleration (macOS) is not yet available. I haven't tested AMD acceleration - let me know if it owrks. The following clients/libraries are known to work with these files, including with CUDA GPU acceleration:
 * [llama.cpp](https://github.com/ggerganov/llama.cpp), commit `e76d630` and later.
 * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI.
+* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration on both Windows and macOS (note that there is currently no macOS GPU acceleration for Llama 70B models.)
 * [KoboldCpp](https://github.com/LostRuins/koboldcpp), version 1.37 and later. A powerful GGML web UI, especially good for story telling.
 * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), version 0.1.77 and later. A Python library with LangChain support, and OpenAI-compatible API server.
 
@@ -97,8 +98,37 @@ Refer to the Provided Files table below to see what files use which methods, and
 | [llama-2-70b-guanaco-qlora.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGML/blob/main/llama-2-70b-guanaco-qlora.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. |
 | [llama-2-70b-guanaco-qlora.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGML/blob/main/llama-2-70b-guanaco-qlora.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 |
 | [llama-2-70b-guanaco-qlora.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/llama-2-70b-Guanaco-QLoRA-GGML/blob/main/llama-2-70b-guanaco-qlora.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 |
+| llama-2-70b-guanaco-qlora.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. |
+| llama-2-70b-guanaco-qlora.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 |
+| llama-2-70b-guanaco-qlora.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. |
 
-**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.
+### 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.
+
+<details>
+  <summary>Click for instructions regarding q5_1, q6_K and q8_0 files</summary>
+  
+### q5_1
+Please download:
+* `llama-2-70b-guanaco-qlora.ggmlv3.q5_1.zip`
+* `llama-2-70b-guanaco-qlora.ggmlv3.q5_1.z01`
+ 
+### q6_K 
+Please download:
+* `llama-2-70b-guanaco-qlora.ggmlv3.q6_K.zip`
+* `llama-2-70b-guanaco-qlora.ggmlv3.q6_K.z01`
+
+### q8_0
+Please download:
+* `llama-2-70b-guanaco-qlora.ggmlv3.q8_0.zip`
+* `llama-2-70b-guanaco-qlora.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 llama-2-70b-guanaco-qlora.ggmlv3.q6_K.zip
+</details>
 
 ## How to run in `llama.cpp`