root/llama.cpp
1
0
Fork 0
mirror of https://github.com/ggerganov/llama.cpp.git synced 2024-12-24 10:24:35 +00:00
Code Issues Actions 5 Packages Projects Releases Wiki Activity

Tidy Android Instructions README.md (#7016)

Browse Source 
* Tidy Android Instructions README.md

Remove CLBlast instructions(outdated), added OpenBlas.

* don't assume git is installed

Added apt install git, so that git clone works

* removed OpenBlas

Linked to Linux build instructions

* fix typo

Remove word "run"

* correct style

Co-authored-by: slaren <slarengh@gmail.com>

* correct grammar

Co-authored-by: slaren <slarengh@gmail.com>

* delete reference to Android API

* remove Fdroid reference, link directly to Termux

Fdroid is not required

Co-authored-by: slaren <slarengh@gmail.com>

* Update README.md

Co-authored-by: slaren <slarengh@gmail.com>

---------

Co-authored-by: slaren <slarengh@gmail.com>
This commit is contained in:
Jeximo 2024-05-04 13:10:15 -03:00 committed by GitHub
parent fcd84a0f5a
commit cf768b7e71
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 8 additions and 36 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
README.md
View File

@ -977,48 +977,20 @@ Here is a demo of an interactive session running on Pixel 5 phone:
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
#### Building the Project using 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.
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.
If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
#### Build on Android using Termux
[Termux](https://github.com/termux/termux-app#installation) is an alternative to execute `llama.cpp` on an Android device (no root required).
```
apt install libopenblas
apt update && apt upgrade -y
apt install git
```
Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
It's recommended to move your model inside the `~/` directory for best performance:
```
apt install ocl-icd opencl-headers opencl-clhpp clinfo
cd storage/downloads
mv model.gguf ~/
```
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* $PREFIX/lib
cp ./include/clblast.h ../llama.cpp
```
Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
```
cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
```
Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
```
GGML_OPENCL_PLATFORM=0
GGML_OPENCL_DEVICE=0
export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
```
(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/ )
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.
Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script.
[Follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`.
### Docker