mlabonne

# 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():

brockmanmatt

brockmanmatt

chad-m

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

import streamlit as st
import pandas as pd

korakot

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

ines

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

choongng

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

alexeygrigorev

pip install grpcio-tools

wget https://github.com/tensorflow/tensorflow/archive/v1.9.0.zip -O tf-190.zip
unzip tf-190.zip && rm tf-190.zip

wget https://github.com/tensorflow/serving/archive/1.9.0.zip -O tf-serving-190.zip
unzip tf-serving-190.zip && rm tf-serving-190.zip

mv serving-1.9.0/tensorflow_serving tensorflow-1.9.0

pbrumblay

from google.cloud import storage
from airflow.hooks.base_hook import BaseHook
from airflow.utils.log.logging_mixin import LoggingMixin
import random
import string


class GCSCustomHook(BaseHook, LoggingMixin):

    def __init__(self, storage_conn_id='google_cloud_storage_default'):

carlthome