Inference of Facebook's LLaMA model in Golang with embedded C/C++.

This project embeds the work of llama.cpp in a Golang binary. The main goal is to run the model using 4-bit quantization using CPU on Consumer-Grade hardware.

At startup, the model is loaded and a prompt is offered to enter a prompt, after the results have been printed another prompt can be entered. The program can be quit using ctrl+c.

This project was tested on Linux but should be able to get to work on macOS as well.

The memory requirements for the models are approximately:

7B  -> 4 GB (1 file)
13B -> 8 GB (2 files)
30B -> 16 GB (4 files)
65B -> 32 GB (8 files)

# build this repo
git clone https://github.com/cornelk/llama-go
cd llama-go
make

# install Python dependencies
python3 -m pip install torch numpy sentencepiece

Obtain the original LLaMA model weights and place them in ./models - for example by using the https://github.com/shawwn/llama-dl script to download them.

Use the following steps to convert the LLaMA-7B model to a format that is compatible:

ls ./models
65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model

# convert the 7B model to ggml FP16 format
python3 convert-pth-to-ggml.py models/7B/ 1

# quantize the model to 4-bits
./quantize.sh 7B

When running the larger models, make sure you have enough disk space to store all the intermediate files.

./llama-go -m ./models/13B/ggml-model-q4_0.bin -t 4 -n 128

Loading model ./models/13B/ggml-model-q4_0.bin...
Model loaded successfully.

>>> Some good pun names for a pet groomer:

Some good pun names for a pet groomer:
Rub-a-Dub, Scooby Doo
Hair Force One
Duck and Cover, Two Fleas, One Duck
...

>>>

The settings can be changed at runtime, multiple values are possible:

>>> seed=1234 threads=8
Settings: repeat_penalty=1.3 seed=1234 temp=0.8 threads=8 tokens=128 top_k=40 top_p=0.95