OhadRubin

This is a KL controller: """ class AdaptiveKLController: """Adaptive KL Controller as described in Ziegler et al. "Fine-Tuning Language Models from Human Preferences" Reference: Section 2.2 https://arxiv.org/pdf/1909.08593.pdf#page=2 Source: https://github.com/openai/lm-human-preferences/blob/master/lm_human_preferences/train_policy.py """

def init(self, init_kl_coef: float, target: float, horizon: int):

	import cv2
	import numpy as np

	# Load the input image
	image = cv2.imread('input.jpg')

	# Convert the image to grayscale
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

	# Apply gaussian blur to the image

	import os
	counts = {}
	for hash in os.popen('git log --all --format=%H'):
	hash = hash.strip()
	os.system('git checkout {}'.format(hash))
	for fname in os.popen('git diff --name-only HEAD^ HEAD'):
	fname = fname.strip()
	counts[fname] = counts.get(fname, 0) + 1
	print('Most changed files:')
	print('-------------------')

	from colorama import Fore, Back, Style
	import os
	import openai

	# Set up OpenAI API key
	openai.api_key = os.getenv("OPENAI_API_KEY")
	prompt = """Your prompts here"""
	# Set up initial message
	messages = [{"role": "system", "content": }]

	import inspect
	from contextlib import ContextDecorator

	class VariableCapture(ContextDecorator):
	def __enter__(self):
	frame = inspect.currentframe().f_back
	self.keys = {k for k,v in frame.f_locals.items() if k != 'self'}
	return self

	def __exit__(self, exc_type, exc_value, traceback):

	import multiprocessing
	import tensorflow as tf
	import seqio
	import numpy as np
	import tqdm
	from transformers import AutoTokenizer
	import jax

	tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
	tokenizer.pad_token = tokenizer.eos_token

	#The following code is the result of taking the code for gpt-j-6b and changing the model name and config name to gpt-neox along with concating the gpt-neox code from pytorch
	# our job is to modify it such that it will be correct for gpt-neox, since right now it is just a copy of gpt-j-6b (with names replaced)
	from functools import partial
	from typing import Optional, Tuple

	import flax.linen as nn
	import jax
	import jax.numpy as jnp
	import numpy as np
	from flax.core.frozen_dict import FrozenDict, freeze, unfreeze

	import networkx as nx
	from itertools import product
	"""
	When we compare this code with Airflow, the strengths of your code lie in its simplicity, lightweight nature, and the ability to easily integrate with existing Python code:

	Simplicity: This code provides a simple and straightforward way to model and work with DAGs without needing to go through the process of setting up and configuring a comprehensive system like Airflow. For smaller teams or projects with less complexity, this can be an advantage.

	Lightweight and easy to incorporate: Your code is a compact, single-file solution that can be easily integrated into an existing Python project without having to set up an entire Airflow environment. When your primary focus is on creating task dependencies with parameter combinations, rather than scheduling and monitoring, your code is easier to incorporate.

	Focused on task generation: Your code emphasizes creating a Cartesian product of tasks associated with nodes' parameters. It is geared towards tackling