llama.cpp/examples/llava/README-minicpmv2.5.md

MiniCPM-Llama3-V 2.5

Prepare models and code

Download MiniCPM-Llama3-V-2_5 PyTorch model from huggingface to "MiniCPM-Llama3-V-2_5" folder.

Clone llama.cpp:

git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp

Usage

Convert PyTorch model to gguf files (You can also download the converted gguf by us)

python ./examples/minicpmv/minicpmv-surgery.py -m ../MiniCPM-Llama3-V-2_5
python ./examples/minicpmv/minicpmv-convert-image-encoder-to-gguf.py -m ../MiniCPM-Llama3-V-2_5 --minicpmv-projector ../MiniCPM-Llama3-V-2_5/minicpmv.projector --output-dir ../MiniCPM-Llama3-V-2_5/ --image-mean 0.5 0.5 0.5 --image-std 0.5 0.5 0.5
python ./convert-hf-to-gguf.py ../MiniCPM-Llama3-V-2_5/model

# quantize int4 version
./llama-quantize ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf Q4_K_M

Build for Linux or Mac

make
make llama-minicpmv-cli

Inference on Linux or Mac

# run f16 version
./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/model-8B-F16.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -p "What is in the image?"

# run quantized int4 version
./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"

# or run in interactive mode
./llama-minicpmv-cli -m ../MiniCPM-Llama3-V-2_5/model/ggml-model-Q4_K_M.gguf --mmproj ../MiniCPM-Llama3-V-2_5/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg -i

Android

Build on Android device using Termux

We found that build on Android device would bring better runtime performance, so we recommend to build on device.

Termux is a terminal app on Android device (no root required).

Install tools in Termux:

apt update && apt upgrade -y
apt install git make cmake

It's recommended to move your model inside the ~/ directory for best performance:

cd storage/downloads
mv model.gguf ~/

Building the Project using Android NDK

Obtain the Android NDK and then build with CMake.

Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux:

mkdir build-android
cd build-android
export NDK=/your_ndk_path
cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod ..
make

Install termux on your device and run termux-setup-storage to get access to your SD card (if Android 11+ then run the command twice).

Finally, copy these built llama binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the /data/data/com.termux/files/home/bin path, and then execute the following commands in Termux to add executable permission:

(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using adb push)

$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
$cd /data/data/com.termux/files/home/bin
$chmod +x ./*

Download models and push them to /sdcard/llama.cpp/, then move it to /data/data/com.termux/files/home/model/

$mv /sdcard/llama.cpp/ggml-model-Q4_K_M.gguf /data/data/com.termux/files/home/model/
$mv /sdcard/llama.cpp/mmproj-model-f16.gguf /data/data/com.termux/files/home/model/

Now, you can start chatting:

$cd /data/data/com.termux/files/home/bin
$./llama-minicpmv-cli -m ../model/ggml-model-Q4_K_M.gguf --mmproj ../model/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image xx.jpg  -p "What is in the image?"