ggml

Tensor library in C for machine learning

Features

• Automatic differentiation (WIP)
• 16-bit float support
• ADAM and L-BFGS optimizers
• Optimized for Arm64 architectures (i.e. MacBook M1) via NEON intrinsics
• On x86 architectures utilzes AVX intrinsics
• No third-party dependencies
• Zero memory allocations during runtime

Local GPT inference

Using ggml you can run GPT-2 and GPT-J inference locally on your computer without any additional software or hardware. You don't even need to install python or any other third-party library.

The example programs are implemented in C++. They run entirely on the CPU.

Here is how to use them:

# Build ggml + examples
git clone https://github.com/ggerganov/ggml
cd ggml
mkdir build && cd build
cmake ..
make -j4 gpt-2 gpt-j

# Run the GPT-2 small 117M model
../examples/gpt-2/download-ggml-model.sh 117M
./bin/gpt-2 -m models/gpt-2-117M/ggml-model.bin -p "This is an example"

# Run the GPT-J 6B model (requires 12GB disk space and 16GB CPU RAM)
../examples/gpt-j/download-ggml-model.sh 6B
./bin/gpt-j -m models/gpt-j-6B/ggml-model.bin -p "This is an example"

This is the inference speed for the different models on my MacBook M1 Pro:

Model	Size	Time / Token
GPT-2	117M	5 ms
GPT-2	345M	12 ms
GPT-2	774M	23 ms
GPT-2	1558M	42 ms
---	---	---
GPT-J	6B	125 ms

For more information, checkout the corresponding programs in the examples folder.