gurusura

import os
import sys
from typing import override

with open(sys.argv[0]) as f:
    code = f.read() # read the code of this file ASAP, for logging
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
import contextlib
import time
import uuid

gurusura

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

gurusura

gurusura

Begin by enclosing all thoughts within <thinking> tags, exploring multiple angles and approaches.
Break down the solution into clear steps within <step> tags. Start with a 20-step budget, requesting more for complex problems if needed.
Use <count> tags after each step to show the remaining budget. Stop when reaching 0.
Continuously adjust your reasoning based on intermediate results and reflections, adapting your strategy as you progress.
Regularly evaluate progress using <reflection> tags. Be critical and honest about your reasoning process.
Assign a quality score between 0.0 and 1.0 using <reward> tags after each reflection. Use this to guide your approach:

0.8+: Continue current approach
0.5-0.7: Consider minor adjustments
Below 0.5: Seriously consider backtracking and trying a different approach

gurusura

# The vast majority of this code was written by Mistral-large and 
# is therefore public domain in the United States.
# But just in case, this script is public domain as set out in the 
# Creative Commons Zero 1.0 Universal Public Domain Notice
# https://creativecommons.org/publicdomain/zero/1.0/

import argparse
import json
from datetime import datetime
import html

gurusura

"""
The 2024 Transformer (the Noam Transformer):
- RMSNorm
- GQA or some combination 
- Sliding window attention 
- Swiglu
- RoPE (Rotary Positional Embedding)

LLM Arches:
                hidden | MLP mult.   |  n_layers | rope_theta |  GQA Group Size  | GLU Act. |  ops 

gurusura

gurusura

# To run you'll need some secrets:
# 1. SERPAPI_API_KEY secret in env var - get from https://serpapi.com/
# 2. OPENAI_API_KEY secret in env var - get from https://openai.com

import streamlit as st
import json, os
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from serpapi import GoogleSearch

gurusura

# Author: Kyle Kastner
# License: BSD 3-Clause
# Implementing http://mnemstudio.org/path-finding-q-learning-tutorial.htm
# Q-learning formula from http://sarvagyavaish.github.io/FlappyBirdRL/
# Visualization based on code from Gael Varoquaux [email protected]
# http://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection

gurusura

# import the necessary packages
from scipy.spatial import distance as dist
import numpy as np
import imutils
from imutils import contours
from imutils import perspective
import cv2

# detect aruco marker
def findArucoMarkers(img, markerSize = 6, totalMarkers=100, draw=True):