The problem with large language models is that you can’t run these locally on your laptop. Thanks to Georgi Gerganov and his llama.cpp project, it is now possible to run Meta’s LLaMA on a single computer without a dedicated GPU.
There are multiple steps involved in running LLaMA locally on a M1 Mac after downloading the model weights.
| # Reference: https://svds.com/jupyter-notebook-best-practices-for-data-science/ | |
| import os | |
| from subprocess import check_call | |
| def post_save(model, os_path, contents_manager): | |
| """post-save hook for converting notebooks to .py scripts""" | |
| if model['type'] != 'notebook': | |
| return # only do this for notebooks | |
| d, fname = os.path.split(os_path) | |
| check_call(['jupyter', 'nbconvert', '--to', 'script', fname], cwd=d) |
