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FoobarProtocol / Answer_LtM.py
Created October 7, 2023 15:32
This is an extraction of the 'least to most' method from the researcher's GitHub; there are still some alterations that need to be made but this extracts the necessary code, makes some fixes to it, ensures that all calls are made to defined functions etc.; pairing this with the correct dataset from the repo will allow us to provide solutions to …
# !/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:
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FoobarProtocol / ta_prompt_2.txt
Created August 15, 2023 23:06
This is the second ta_prompt provided by the organization, 'BigCode' for StarCoder
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.
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FoobarProtocol / ta_prompt_1.txt
Created August 15, 2023 23:03
This is the first ta_prompt provided by the organization, 'BigCode' for StarCoder
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.
-----
Human: Write a function that takes two lists and returns a list that has alternating elements from each input list.
Assistant: Sure. Here is a function that does that.
```
def alternating(list1, list2):
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FoobarProtocol / smart_contract_examples.asciidoc
Last active August 10, 2023 06:21
This is extracted from the 'EthereumBook'. Pretty old, yet still solid and reliable guide on many things Ethereum-related

Smart Contract Security

Security is one of the most important considerations when writing smart contracts. In the field of smart contract programming, mistakes are costly and easily exploited. In this chapter we will look at security best practices and design patterns, as well as "security antipatterns," which are practices and patterns that can introduce vulnerabilities in our smart contracts.

As with other programs, a smart contract will execute exactly what is written, which is not always what the programmer intended. Furthermore, all smart contracts are public, and any user can interact with them simply by creating a transaction. Any vulnerability can be exploited, and losses are almost always impossible to recover. It is therefore critical to follow best practices and use

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.0;
import "@openzeppelin/contracts/token/ERC20/ERC20.sol";
contract MyToken is ERC20 {
uint256 public immutable precision;
uint256 public immutable vestingPeriod;
uint256 public vestingPeriodFinish;