Merge 759dcdcfdb into ea2c85d5d2

README.md

@@ -804,48 +804,59 @@ Finally, copy the `llama` binary and the model files to your device storage. Her
 
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
 
-#### Building the Project using Termux (F-Droid)
+#### Building the Project in Termux (F-Droid)
-Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.
+[Termux](https://termux.dev/) is a way to run `llama.cpp` on Android devices.
 
-Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.
+Ensure Termux is up to date and clone the repo:
-
-If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
 ```
-apt install libopenblas
+apt update && apt upgrade
+cd
+git clone https://github.com/ggerganov/llama.cpp
 ```
 
-Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
+Build `llama.cpp`:
 ```
-apt install ocl-icd opencl-headers opencl-clhpp clinfo
+cd llama.cpp
-```
-
-In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:
-```
-cmake .
 make
-cp libclblast.so* $PREFIX/lib
-cp ./include/clblast.h ../llama.cpp
 ```
 
-Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
+It's possible to enable `OpenBlas` while building:
 ```
-cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
+pkg install libopenblas
-cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
+cd llama.cpp
-make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
+make LLAMA_OPENBLAS=1
 ```
 
-Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
+Move your model to the home directory (`~/`), for example:
 ```
-GGML_OPENCL_PLATFORM=0
+cd
-GGML_OPENCL_DEVICE=0
+cd storage/downloads
-export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
+mv 7b-model.gguf.q4_0.bin ~/
 ```
 
-(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )
+Usage example:
+```
+./main -m ~/7b-model.gguf.q4_0.bin --color -c 2048 --keep -1 -n -2 -b 7 -ins -p 'Below is an instruction that describes a task. Write a response that appropriately completes the request.'\n\n'### Instruction:'\n'Hi!'\n\n'### Response:Hi! How may I assist you?'
+```
 
-For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
+For building with `OpenCL` then install the requisite packages:
+```
+pkg install ocl-icd opencl-headers clblast
+cd llama.cpp
+make LLAMA_CLBLAST=1
+```
 
-Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script.
+Use one of the following to enable GPU:
+```
+export LD_LIBRARY_PATH=/vendor/lib64
+```
+or
+```
+export LD_LIBRARY_PATH=/system/vendor/lib64
+```
+then `./main ... --gpu-layers 1`
+
+(Note: Use `unset LD_LIBRARY_PATH` to re-link executables.)
 
 ### Docker