Fabrizio Milo Mistobaan

See rune2e.sh for info on how to run the experiment.

A good way to get a taste of Swift for Tensorflow language and tools is to set it up with Jupyter with the fastai Swift notebooks. I wanted a quick setup, which the Mac install experience currently not, so instead I installed the release binaries in a Ubuntu container via Docker. The setup process for this scenario is not well documented, so here it is for you / future me.

What we're about to do is install the S4TF 0.4 release and the fastai v3 Swift notebooks on Ubuntu 18.04. Generally we follow the swift-jupyter docker file, but install cpu-only release versions of the packages.

Below are some of the references I looked at:

Rationale for S4TF and background reading

https://github.com/tensorflow/swift/blob/master/docs/WhySwiftForTensorFlow.md https://github.com/tensorflow/swift/blob/master/docs/DifferentiableFunctions.md

	#!/usr/bin/env bash

	# Default values for percentages
	DEFAULT_WIRED_LIMIT_PERCENT=85
	DEFAULT_WIRED_LWM_PERCENT=75

	# Read input parameters or use default values
	WIRED_LIMIT_PERCENT=${1:-$DEFAULT_WIRED_LIMIT_PERCENT}
	WIRED_LWM_PERCENT=${2:-$DEFAULT_WIRED_LWM_PERCENT}

	# Train GPT-2 in five minutes -- for free
	#
	# ```bash
	# pip install modal
	# modal setup
	# modal run wrapper.py
	# ```
	#
	# Note that the end-to-end latency the first time is more like 25 minutes:
	# - five minutes to install Torch (rip)

	# Example usage:
	# python merge_peft.py --base_model=meta-llama/Llama-2-7b-hf --peft_model=./qlora-out --hub_id=alpaca-qlora

	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel
	import torch

	import argparse

	def get_args():


	import base64
	import os
	import json
	import pickle
	import uuid
	import re

	import streamlit as st
	import pandas as pd

	import json
	from subprocess import check_output
	from time import sleep
	!pip -q install youtube-dl

	from google.colab import auth
	auth.authenticate_user()
	!gcloud config set project kora-id
	gcs = 'gs://co-lab/dhamma' # use your own project, gcs

	pip install streamlit
	pip install spacy
	python -m spacy download en_core_web_sm
	python -m spacy download en_core_web_md
	python -m spacy download de_core_news_sm