Jon panamantis

*2vec papers

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	import re
	import torch
	from datasets import load_dataset, Dataset
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import LoraConfig
	from trl import GRPOConfig, GRPOTrainer

	# !pip install torch transformers scikit-learn umap-learn matplotlib datasets joblib pandas zstandard

	'''
	Code outline:

	1. Loading the pre-trained GPT-2 model and tokenizer.
	2. Loading or downloading the dataset and saving it to disk with compression.
	3. Collecting and normalizing activations from the middle layer of the model.
	4. Training multiple SAEs with different feature sizes and saving them to disk.
	5. Finding the feature that responds to the phrase "Golden Gate Bridge" in each SAE.

	// Created by Anderson Mancini 2023
	// React Three Fiber AutoFocus Component to be used
	// as an extension for default Depth Of Field from react-three/postprocessing

	// HOW TO USE?
	// import AutoFocusDOF from './AutoFocusDOF'
	//
	// And add this component inside the EffectsComposer...
	//...
	// <EffectComposer>

	import requests

	headers = {"user-agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/66.0.3359.181 Safari/537.36",
	}

	company_link = 'https://www.linkedin.com/voyager/api/entities/companies/2652230'

	with requests.session() as s:
	s.cookies['li_at'] = "your li_at cookie"
	s.cookies["JSESSIONID"] = "your JSESSIONID"

	# extraction pattern: ngram TAB year TAB match_count TAB volume_count NEWLINE
	# out: unique_ngram TAB sum(match_count) NEWLINE

	import re
	import os, sys, mmap
	from pathlib import Path
	from tqdm import tqdm
	from concurrent.futures import ThreadPoolExecutor

	abv = re.compile(r'^(([A-Z]\.){1,})(_\|[^\w])') # A.B.C.

	import requests
	from bs4 import BeautifulSoup
	import pandas as pd
	import time

	baseurl = 'http://publicreporting.ltchomes.net/en-ca/'
	url = baseurl + 'Search_Selection.aspx'

	response = requests.get(url)
	soup = BeautifulSoup(response.text, "html.parser")

	"""
	The script contains example of the paramiko usage for large file downloading.

	It implements :func:`download` with limited number of concurrent requests to server, whereas
	paramiko implementation of the :meth:`paramiko.SFTPClient.getfo` send read requests without
	limitations, that can cause problems if large file is being downloaded.
	"""

	import logging
	import os

	<!DOCTYPE html>
	<head>
	<meta charset="utf-8">

	<title>d3 depth chart</title>

	<script type="text/javascript" src="https://d3js.org/d3.v4.min.js"></script>
	<script type="text/javascript" src="https://cdn.jsdelivr.net/npm/[email protected]/lodash.min.js"></script>

	<style>