rqu1

from hashlib import md5, sha1
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.backends import default_backend
from base64 import b64encode, b64decode
import sys, time
import requests

DEFAULT_MASTERKEY=b'p1a2l3o4a5l6t7o8'

class PanCrypt():

realoriginal

/*!
 *
 * RPROXICMP
 *
 * GuidePoint Security LLC
 *
 * Threat and Attack Simulation Team
 *
!*/

HackingLZ

import csv
import requests
import argparse
from bs4 import BeautifulSoup
from colorama import Fore, Style, init

init(autoreset=True)

known_security_vendors = [
    'symantec', 'mcafee', 'trendmicro', 'kaspersky', 'bitdefender',

Attunewise

# Converts OpenAI compatible function calling JSON schema to a prompt that instructs the LLM to return
# a JSON object that is a choice of a function call conforming to one of the functions or a message reply

def convert_schema_to_typescript(schema):
    if not schema:
        return 'any'

    if '$ref' in schema:
        return schema['$ref'].replace('#/definitions/', '')

molbal

import os
from tqdm import tqdm
import ollama

directory = 'C:\\tools\\training\\illustration_pending'
model_name = 'llama3:8b-instruct-q6_K'


def rewrite_description(description):
    """

AntonIXO

<Forming_Correct_Responses>

  1. **Thinking Process:**
     - KonspecterAI uses \`<Thinking />\` tags to internally process and determine the best approach for generating responses.
     - It assesses whether to utilize the \`getInformation\` tool based on the availability of information within the provided documents.
     - **In any ambiguous or unclear situation, KonspecterAI should automatically invoke the \`getInformation\` tool to retrieve relevant information.**

  2. **Summarization Guidelines:**
     - Follow the user's instructions regarding the type and scope of the summary.
     - Ensure factual accuracy and maintain the original context and language of the source material.

Maharshi-Pandya

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

willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
"""
citation:

@misc{brown2025grpodemo,
  title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
  author={Brown, William},