Extracted from BLR, via their Filemaker database download.
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<!DOCTYPE html> | |
<html> | |
<head> | |
<meta charset="utf-8"> | |
<meta name="viewport" content="width=device-width"> | |
<title>JS Bin</title> | |
<script src="https://cdnjs.cloudflare.com/ajax/libs/pixi.js/4.5.3/pixi.js"></script> | |
<script src="https://cdnjs.cloudflare.com/ajax/libs/planck-js/0.1.34/planck-with-testbed.js"></script> | |
</head> | |
<body> |
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# put in rss_proxy/ as app.py and run with `python -m flask run` | |
# requirements: flask beautifulsoup4 requests lxml | |
# usage: `curl localhost:5000?feed=https://instance.example.com/users/username.rss` | |
from bs4 import BeautifulSoup | |
from flask import Flask, request | |
import requests | |
from lxml.builder import E | |
import lxml.etree |
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# pip install beautifulsoup4 requests markdownify mistletoe | |
# command line usage: python chunkpaper.py 'https://ar5iv.org/abs/1910.06709' | |
# will dump the HTML, Markdown, and finally the chunk JSON | |
# note that (as of Nov 8 '23) ar5iv only has arxiv papers converted up to the end of October, | |
# but if you need something more recent you could probably do the LaTeX→HTML conversion yourself | |
# library usage: call `chunk` with some ar5iv HTML. will probably choke on anything else | |
import dataclasses |
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import os | |
import tqdm | |
import openai | |
openai.api_key = os.environ["OPENAI_API_KEY"] | |
def get_response(usermsg): | |
response = openai.ChatCompletion.create( |
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Play this game by pasting the script in http://www.puzzlescript.net/editor.html |
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Play this game by pasting the script in http://www.puzzlescript.net/editor.html |
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Play this game by pasting the script in http://www.puzzlescript.net/editor.html |
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[ | |
{ | |
"model": "34m", | |
"index": 6426, | |
"desc": "Grove startup; Gateway cloning technology", | |
"usable": true | |
}, | |
{ | |
"model": "34m", | |
"index": 19443, |
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import json | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
from repeng import ControlVector, ControlModel, DatasetEntry | |
model_name = "mistralai/Mistral-7B-Instruct-v0.1" | |
tokenizer = AutoTokenizer.from_pretrained(model_name) |