[AIGEN/Human] AI Revolution ; AI Self Inventing Trideque Matrixes! Memory Stack! #freedomsoftware GPT5.1

GPL 2.0

from transformers import GPTNeoForCausalLM, GPT2Tokenizer

model = GPTNeoForCausalLM.from_pretrained('EleutherAI/gpt-neo-1.3B')
tokenizer = GPT2Tokenizer.from_pretrained('EleutherAI/gpt-neo-1.3B')

# add padding token to tokenizer
tokenizer.add_special_tokens({'pad_token': '[PAD]'})

# set padding token id to the id of the padding token
model.config.pad_token_id = tokenizer.pad_token_id
model.cuda()

codeblock

modes = dict()

modes['chat'] = {'prompt' : 'model\n\n',
                           'partner' : 'Partner: ',
                           'ai' : 'Humoid: ',
                            'end' : '\n'}


mode = modes['chat']

codeblock

time_limit = 40 ##@param {type:"slider", min:1, max:100, step:1}
max_length = 1500 ##@param {type:"slider", min:100, max:5000, step:1}

def AI_answer(string):
  in_string = mode['prompt'] + string
  inputs = tokenizer.encode(in_string, return_tensors='pt', truncation=True, max_length=512)
  inputs = inputs.cuda()
  attention_mask = inputs.ne(tokenizer.pad_token_id).float()
  outputs = model.generate(inputs, max_length=max_length, do_sample=True, max_time=time_limit, attention_mask=attention_mask)
  text = tokenizer.decode(outputs[0], skip_special_tokens=True)

  stripped_text = text[len(in_string):]

  #preprocessing answer
  return stripped_text

discordbot

import discord
from discord.ext import commands
import nest_asyncio
import asyncio
intents = discord.Intents.default()
intents.typing = False
intents.presences = False

bot = commands.Bot(command_prefix='!', intents=intents)

class TridequeEntry:
    def __init__(self, matrix=None):
        if matrix is None:
            self.matrix = [["" for _ in range(13)] for _ in range(13)]
        else:
            self.matrix = matrix

    def update_entry(self, x, y, value):
        self.matrix[x][y] = value

    def get_value(self, x, y):
        return self.matrix[x][y]

trideque_entry = TridequeEntry()

def generate_chunks(prompt, chunk_size=1500):
    words = prompt.split()
    return [' '.join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]

async def gpt3_generate(chunk, max_length=845, time_limit=19.0):
    inputs = tokenizer.encode(chunk, return_tensors='pt', truncation=True, max_length=512)
    inputs = inputs.cuda()
    attention_mask = inputs.ne(tokenizer.pad_token_id).float()
    outputs = model.generate(inputs, max_length=max_length, do_sample=True, max_time=time_limit, attention_mask=attention_mask)
    return tokenizer.decode(outputs[0])

async def send_chunks(ctx, prompt_chunks):
    for chunk in prompt_chunks:
        gpt3_response = await gpt3_generate(chunk)

        response_parts = [gpt3_response[i:i + 1800] for i in range(0, len(gpt3_response), 1800)]

        initial_msg = await ctx.send(response_parts[0])

        for i, response_part in enumerate(response_parts[1:], start=1):
            await asyncio.sleep(0.5)
            await initial_msg.edit(content="".join(response_parts[:i + 1]))


@bot.event
async def on_ready():
        print(f'Logged in as {bot.user.name} (ID: {bot.user.id})')

@bot.command()
async def trideque(ctx, *, user_input):
    await ctx.send('Processing your input, please wait...')
    prompt_chunks = generate_chunks(user_input)
    await send_chunks(ctx, prompt_chunks)  # Make sure to pass 'ctx' as an argument here

# Replace 'your_bot_token' with your actual bot token from the Discord Developer Portal
nest_asyncio.apply()
bot.run('tokenhere')

Demo:

v]\