morganmcg1

import base64
from openai import OpenAI
import weave
import wave


client = OpenAI()
weave.init("audio-in-weave")

@weave.op()

morganmcg1

run = wandb.init(
    settings={
        "_disable_stats": True,  # disable collecting system metrics
        "_disable_meta": True,  # disable collecting system metadata (including hardware info)
        "console": "off",  # disable capturing stdout/stderr
    }
)

morganmcg1

# From Unsloth Llama 3.1 fine-tuning notebook

from unsloth import FastLanguageModel
import torch

def print_gpu_stats():
  gpu_stats = torch.cuda.get_device_properties(0)
  start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
  max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
  print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")

morganmcg1

celebrities = [
"Tom Cruise",
"Leonardo DiCaprio",
"Brad Pitt",
"Jennifer Lawrence",
"Scarlett Johansson",
"Johnny Depp",
"Meryl Streep",
"Robert De Niro",
"Tom Hanks",

morganmcg1

#!/bin/bash

# Download the Miniforge installer
wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh

# Run the Miniforge installer
bash Miniforge3-Linux-x86_64.sh

# Install mamba in the base environment using conda
conda install mamba -n base -c conda-forge

morganmcg1

import os
import json
import tqdm

import wandb
from openai import OpenAI

from time import sleep
from pathlib import Path

morganmcg1

import openai
import wandb
openai.api_key = "sk-..."  # supply your API key however you choose

# https://platform.openai.com/docs/models

tbl = wandb.Table("my_table", columns=["input", "output", "temperature"])

query = "hello world"
systemp_prompts = "you are friendly"

morganmcg1

"Recently, this problem was solved correctly. Here is the answer which turned out to be perfectly correct. 
Note how the answer is documented step-by-step in a way that uses complex reasoning. 
The person who discovered this solution always showed how they arrived on the decision to 
execute the most efficient possible choice about what to do next, and clearly relied on
error-free code, calculators & fact-checked outside data sources to  provide perfectly accurate answers at every step."

---------------

Here's a prompt that works really well to get GPT-4 to shorten text: I often use it to make my tweets fit in 280 characters:
```

morganmcg1

#!/usr/bin/env python
# coding: utf-8

# In this notebook we will automatically generate a set of evaluation questions based on wandb docs

import random
import wandb
import re
import openai
import os

morganmcg1

import langchain
import wandb

from typing import Any, Awaitable, Callable, Dict, Optional, Union
from fastapi.responses import StreamingResponse as _StreamingResponse
from langchain.chains.base import Chain
from starlette.background import BackgroundTask
from starlette.types import Send

from dotenv import load_dotenv