中文 ReadMe

InferLLM is a lightweight LLM model inference framework that mainly references and borrows from the llama.cpp project. llama.cpp puts almost all core code and kernels in a single file and use a large number of macros, making it difficult for developers to read and modify. InferLLM has the following features:

• Simple structure, easy to get started and learning, and decoupled the framework part from the kernel part.
• High efficiency, ported most of the kernels in llama.cpp.
• Defined a dedicated KVstorage type for easy caching and management.
• Compatible with multiple model formats (currently only supporting alpaca Chinese and English int4 models).
• Currently only supports CPU, mainly Arm and x86 platforms, and can be deployed on mobile phones, with acceptable speed.

In short, InferLLM is a simple and efficient LLM CPU inference framework that can deploy quantized models in LLM locally and has good inference speed.

Currently, InferLLM uses the same models as llama.cpp and can download models from the llama.cpp project. In addition, models can also be downloaded directly from Hugging Face kewin4933/InferLLM-Model. Currently, two alpaca models are uploaded in this project, one is the Chinese int4 model and the other is the English int4 model.

mkdir build
cd build
cmake ..
make

According to the cross compilation, you can use the pre-prepared tools/android_build.sh script. You need to install NDK in advance and configure the path of NDK to the NDK_ROOT environment variable.

export NDK_ROOT=/path/to/ndk
./tools/android_build.sh

If it is executed locally, execute ./alpaca -m chinese-alpaca-7b-q4.bin -t 4 directly. If you want to execute it on your mobile phone, you can use the adb command to copy alpaca and the model file to your mobile phone, and then execute adb shell ./alpaca -m chinese-alpaca-7b-q4.bin -t 4.

• x86 is：Intel(R) Xeon(R) CPU E5-2620 v4 @ 2.10GHz
• android is xiomi9，Qualcomm SM8150 Snapdragon 855

InferLLM is licensed under the Apache License, Version 2.0