perryism

import json
import sys
import os
import urllib.request

match = sys.argv[1]
filepath = "https://googlechromelabs.github.io/chrome-for-testing/known-good-versions-with-downloads.json"#sys.argv[2]

def get_versions(filepath):
    if os.path.exists(filepath):

perryism

from langchain.callbacks.manager import AsyncCallbackManager
import os, logging

logger = logging.getLogger(__name__)

stream_manager = AsyncCallbackManager([])

from langchain_google_vertexai import VertexAIModelGarden
from typing import Any, List, Optional

perryism

"""
I found myself waiting for LLM model to give me result.  My result is always the same since I am working on the lower level library. 

Example

@memoize("test.pkl")
def sum(x, y):
    return x + y
"""

perryism

FROM nvidia/cuda:11.2.2-runtime-ubuntu20.04 

RUN apt-get update --yes --quiet
RUN apt install software-properties-common -y && add-apt-repository ppa:deadsnakes/ppa -y && \
     apt-get install -y python3.10 \
                        pip \
                        python3.10-distutils \
                        curl

RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.10

perryism

import google.auth
from google.auth.transport.requests import Request
import requests

def get_credentials() -> str:
    credentials, _project_id = google.auth.default(scopes=['https://www.googleapis.com/auth/cloud-platform'])
    request = Request()
    credentials.refresh(request)
    return credentials

perryism

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, pipeline, logging, TextStreamer
from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model
import os,torch, platform, warnings
from datasets import load_dataset     
from trl import SFTTrainer

class Lora:
    @classmethod
    def prepare(cls, model_name):
        """

perryism

import unittest
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logger = logging.getLogger(__name__)

class Agent:
    def __init__(self, role, objective):

perryism

import argparse
import logging
from enum import EnumMeta

logger = logging.getLogger(__name__)

class Blackmore:
    def __init__(self, name, functions):
        self.name = name
        self.functions = dict([[f.__name__, f] for f in functions])

perryism

from google.oauth2.credentials import Credentials
from google.cloud import bigquery
import google.oauth2.credentials

import os

PROJECT = os.environ.get("PROJECT")

# gcloud auth print-access-token
ACCESS_TOKEN = os.environ.get("ACCESS_TOKEN")

perryism

from langchain.chains import SequentialChain
from langchain.prompts import ChatPromptTemplate
from langchain.chains import LLMChain
from langchain.chat_models import ChatOpenAI

llm = ChatOpenAI(temperature=0.9)

class MyChain:
    def __init__(self, llm=None, template_engine=None):
        self.llm = llm