Tensor library for machine learning
Note that this project is under development and not ready for production use.
Some of the development is currently happening in the whisper.cpp repo
- Written in C
- 16-bit float support
- Automatic differentiation (WIP in progress)
- ADAM and L-BFGS optimizers
- Optimized for Apple silicon via NEON intrinsics and Accelerate framework
- On x86 architectures utilzes AVX intrinsics
- No third-party dependencies
- Zero memory allocations during runtime
- Example of GPT-2 inference examples/gpt-2
- Example of GPT-J inference examples/gpt-j
- Example of Whisper inference examples/whisper
- Support 4-bit integer quantization ggerganov#27
- Example of FLAN-T5 inference ggerganov#12
- Example of LLaMA inference
- Example of RWKV inference
With ggml you can efficiently run Whisper inference on the CPU.
Memory requirements:
| Model | Disk | Mem |
|---|---|---|
| tiny | 75 MB | ~280 MB |
| base | 142 MB | ~430 MB |
| small | 466 MB | ~1.0 GB |
| medium | 1.5 GB | ~2.6 GB |
| large | 2.9 GB | ~4.7 GB |
With ggml you can efficiently run GPT-2 and GPT-J inference on the CPU.
Here is how to run the example programs:
# 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"The inference speeds that I get for the different models on my 32GB MacBook M1 Pro are as follows:
| 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.