Update README.md

README.md

@@ -34,6 +34,14 @@ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/gger
 * [4-bit, 5-bit, and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGML)
 * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b)
 
+## Prompt template
+
+```
+Optional instruction ("You are a helpful assistant" etc)
+USER: prompt
+ASSISTANT:
+```
+
 ## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!
 
 llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508
@@ -49,7 +57,6 @@ I have quantised the GGML files in this repo with the latest version. Therefore
 | WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_1.bin | q5_1 | 5 | 24.40 GB | 26.90 GB | 5-bit. Even higher accuracy, resource usage and slower inference. |
 | WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q8_0.bin | q8_0 | 8 | 34.56 GB | 37.06 GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for normal use. |
 
-
 **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.
 
 ## How to run in `llama.cpp`
@@ -57,7 +64,7 @@ I have quantised the GGML files in this repo with the latest version. Therefore
 I use the following command line; adjust for your tastes and needs:
 
 ```
-./main -t 10 -ngl 32 -m WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
+./main -t 10 -ngl 32 -m WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "USER: Write a story about llamas\nASSISTANT:"
 ```
 Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.