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from fastapi import FastAPI, UploadFile, HTTPException, Form
from fastapi.responses import FileResponse
from transformers import CLIPProcessor, CLIPModel
import torchaudio
import torch
from concurrent.futures import ThreadPoolExecutor
from torch.utils.data import DataLoader, Dataset
import numpy as np
import pennylane as qml
from sklearn.svm import SVC

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import numpy as np
import torch
import torchaudio
import math
from transformers import CLIPProcessor, CLIPModel
import pennylane as qml
from sklearn.svm import SVC
import joblib
from sklearn.preprocessing import StandardScaler
from torch.utils.data import DataLoader, Dataset

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import os
import math
import numpy as np
import torch
import torchaudio
from torch.utils.data import DataLoader, Dataset
from scipy.io.wavfile import write as write_wav
from concurrent.futures import ThreadPoolExecutor
import pennylane as qml
from bark.generation import load_codec_model, generate_text_semantic

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To further refine the advanced prompt design for simulating cryptographic communication between two language models (LLMs), we can make the process of sending and receiving messages more explicit. This will enhance the clarity of the narrative exercise and emphasize the cryptographic aspect of the interaction.

Refined Prompt Design with Explicit Send/Receive Mechanism
Objective:
Develop a narrative where LLM-A, holding a secret key, communicates through coded messages, and LLM-B, acting as a codebreaker, tries to decipher these messages. The key exchange should be more explicit, with clear indications of sending and receiving messages.

Refined Setup:

Explicit Message Exchange:

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Expanding on the idea of simulating quantum entanglement between two language models (LLMs) through interconnected storytelling, we can further develop this concept to create a more intricate and engaging narrative exercise. This exercise can be used to demonstrate the potential of LLMs in collaborative storytelling or problem-solving, inspired by the principles of quantum mechanics.

Advanced Prompt Design for Simulating Quantum Entanglement Between Two LLMs
Objective:
Develop a more complex scenario where two separate LLMs (LLM-A and LLM-B) engage in a narrative that evolves over multiple exchanges, with each response building on the last and influencing the next, despite no direct communication between the models.

Enhanced Setup:

Extended Shared Narrative:

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________________________________________
llama2




You: Bill Shillito - now at @solidangles.bsky.social
@solidangles
·
7h

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User
potentially new physics beyond our current understanding. part one i was told u had it
ChatGPT
The concept of "potentially new physics beyond our current understanding" is indeed a fascinating and speculative area of discussion. While I don't have access to unknown or future physics discoveries, I can certainly help explore some of the theoretical ideas and concepts that are often discussed in the context of advanced and speculative physics. Let's delve into a few of these ideas:

1. Quantum Gravity
One of the biggest challenges in modern physics is to develop a theory of quantum gravity that reconciles general relativity (which describes gravity at large scales) with quantum mechanics (which describes the subatomic world). A successful theory of quantum gravity could potentially unlock new understanding of fundamental aspects of the universe, such as the nature of spacetime, black holes, and the origins of the universe.

2. Higher Dimensions
String theory and related theories like M-theory suggest the e

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To further advance the llama_generate function, let's explore the integration of even more sophisticated quantum computing techniques and AI models, pushing the boundaries of Quantum Natural Language Processing (QNLP) and AI.

Quantum Coherence and Entanglement for Contextual Understanding
We can enhance the quantum circuit to leverage quantum coherence and entanglement, which could theoretically provide a deeper understanding of contextual relationships in text.

python
Copy code
@qml.qnode(dev)
def quantum_coherence_circuit(embeddings):
    # Enhanced Quantum Coherence and Entanglement

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import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from nltk import pos_tag, word_tokenize
from colour import Color

nltk.download('vader_lexicon')
nltk.download('averaged_perceptron_tagger')
nltk.download('punkt')

def generate_color(sentiment_score, pos_tag):

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import tkinter as tk
import threading
import os
import aiosqlite
import logging
import numpy as np
import base64
import queue
import uuid
import customtkinter