This repository is intended as a minimal, hackable and readable example to load LLaMA (arXiv) models and run inference by using only CPU. Thus requires no videocard, but 64 (better 128 Gb) of RAM and modern processor is required. Make sure you have enough swap space (128Gb should be ok :).

It is better to use another repo if you have NVIDIA card: https://github.com/randaller/llama-chat

Download and install Anaconda Python https://www.anaconda.com and run Anaconda Prompt

conda create -n llama python=3.10
conda activate llama
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

In a conda env with pytorch / cuda available, run

pip install -r requirements.txt

Then in this repository

pip install -e .

magnet:?xt=urn:btih:ZXXDAUWYLRUXXBHUYEMS6Q5CE5WA3LVA&dn=LLaMA

or

magnet:xt=urn:btih:b8287ebfa04f879b048d4d4404108cf3e8014352&dn=LLaMA&tr=udp%3a%2f%2ftracker.opentrackr.org%3a1337%2fannounce

Place tokenizer.model file from torrent into repo's [/tokenizer] folder.

Place model files from torrent folder (for example, [/13B]) into repo's [/model] folder.

Run the example:

python example-cpu.py

python example-chat.py

Running model with single prompt on Windows computer equipped with 12700k, fast nvme and 128 Gb of RAM.

By default, torch uses Float32 precision while running on CPU, which leads, for example, to use 44 GB of RAM for 7B model. We may use Bfloat16 precision on CPU too, which decreases RAM consumption/2, down to 22 GB for 7B model, but inference processing much slower.

An optimized checkpoints loader breaks compatibility with Bfloat16, so I decided to add example-bfloat16.py runner.

To use Bfloat16 precision, first you need to unshard checkpoints to a single one.

python merge_weights.py --input_dir D:\Downloads\LLaMA --model_size 13B

In this example, D:\Downloads\LLaMA is a root folder of downloaded torrent with weights.

This will create merged.pth file in the root folder of this repo. Place this file and corresponding params.json of model into [/model] folder. File tokenizer.model should be in [/tokenizer] folder of this repo. Now you are ready to go.

python example-bfloat16.py

See MODEL_CARD.md

See the LICENSE file.