FoobarProtocol

def convert_alpaca_to_evol(
    file_path: str, 
    lines: bool = False,
    output_file: str = "converted_alpaca.json"
):
    """Convert the Instruction/Input/Output format of Alpaca Instruct datasets
    to the Evol-Instruct format of Instruction/Output. Inputs are appended to the
    instructions.
    
    Args:

FoobarProtocol

import json
import random

from openai_access import call_chatgpt
from depth import createConstraintsPrompt, createDeepenPrompt, createConcretizingPrompt, createReasoningPrompt
from breadth import createBreadthPrompt


fr = open('alpaca_data.json','r')

FoobarProtocol

import re
import json
import uuid
inputs = [json.loads(line) for line in open("instructions.jsonl").readlines()]

def split_response(instruction, response):
    if '</s>' not in response:
        return [
            {
                "from": "human",

FoobarProtocol

import openai

api_keys = ['api-key-1', 'api-key-2', 'api-key-3']  # Replace with your actual API keys
num_prompts = 1000
prompts_per_request = 100  # Adjust based on your needs
num_requests = 10

prompts = []

for i in range(num_requests):

FoobarProtocol

from transformers import AutoTokenizer, BitsAndBytesConfig, AutoModelForSeq2SeqLM

model_id = "google/flan-t5-xxl"

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=False
)

model = AutoModelForSeq2SeqLM.from_pretrained(model_id, quantization_config=quantization_config)

FoobarProtocol

curl -X "POST" "https://www.promptsapper.tech:8003/sapperpro/Explore" \
     -H 'User-Agent: Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:120.0) Gecko/20100101 Firefox/120.0' \
     -H 'Accept: */*' \
     -H 'Accept-Language: en-US,en;q=0.5' \
     -H 'Accept-Encoding: gzip, deflate, br' \
     -H 'Referer: https://www.promptsapper.tech/' \
     -H 'Origin: https://www.promptsapper.tech' \
     -H 'Dnt: 1' \
     -H 'Connection: keep-alive' \
     -H 'Sec-Fetch-Dest: empty' \

FoobarProtocol

import aiohttp
import argparse
import asyncio
import backoff
import copy
import datetime
import faiss
import os
import json
import math

FoobarProtocol

import gr

def smart_contract_prompt_builder(
    contract_type,
    state_variables,
    functions,
    visibility,
    modifiers,
    features,
    access_control,

FoobarProtocol

# !/usr/bin/env python3
# _*_ coding:utf-8 _*_
import openai
import random
import time
import json
import re

def create_response( eng,prompt_input, max_tokens=256, temperature=0.0, stop=None):
    if stop is None:

FoobarProtocol

Below are a series of dialogues between various people and an AI technical assistant. The assistant tries to be helpful, polite, honest, sophisticated, emotionally aware, and humble-but-knowledgeable. The assistant is happy to help with code questions, and will do its best to understand exactly what is needed. It also tries to avoid giving false or misleading information, and it caveats when it isn’t entirely sure about the right answer. That said, the assistant is practical and really does its best, and doesn’t let caution get too much in the way of being useful.

The Starcoder models are a series of 15.5B parameter models trained on 80+ programming languages from The Stack (v1.2) (excluding opt-out requests). The model uses Multi Query Attention, was trained using the Fill-in-the-Middle objective and with 8,192 tokens context window for a trillion tokens of heavily deduplicated data.

-----

Human: Who are you?

Assistant: My name is StarCoder, a language model developed by BigCode.