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import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# Cargar el modelo de lenguaje preentrenado
model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Crear la funci贸n de loop automatizado
def experiment_loop(initial_question, max_cycles=10):
prompt = f"<thinking>{initial_question}</thinking>"
effectiveness = 100 # Inicializa el porcentaje de efectividad
communication = "Initializing experiment."
response_log = []
try:
for cycle in range(max_cycles):
# Generar la respuesta del modelo
inputs = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Descomponer la respuesta en afirmaci贸n y nueva pregunta
affirmation = extract_affirmation(response)
new_question = extract_question(response)
# Actualizar el estado de la efectividad
effectiveness = min(1000, effectiveness + 10 * cycle) # Ejemplo de aumento de efectividad
# Comunicaci贸n con el usuario
communication = f"Cycle {cycle + 1}: Affirmation: '{affirmation}' | New Question: '{new_question}'"
# Guardar el ciclo actual en el log
response_log.append((affirmation, new_question, effectiveness, communication))
# Verificar si el modelo decide detenerse
if "Descanso" in response:
final_output = generate_final_output(response_log)
return final_output
# Actualizar el prompt con la nueva afirmaci贸n y pregunta
prompt = f"<thinking>{affirmation} {new_question}</thinking>"
except Exception as e:
print(f"Error durante el experimento: {e}")
# Si se alcanza el n煤mero m谩ximo de ciclos sin detenerse
final_output = generate_final_output(response_log)
return final_output
# Funciones auxiliares para extraer afirmaciones, preguntas y generar la salida final
def extract_affirmation(response):
# L贸gica para extraer la afirmaci贸n de la respuesta
return response.split('.')[0]
def extract_question(response):
# L贸gica para extraer la nueva pregunta de la respuesta
return response.split('?')[-2].strip() + "?"
def generate_final_output(log):
final_affirmation = log[-1][0]
final_question = log[-1][1]
final_communication = f"Experiment completed. Final Affirmation: '{final_affirmation}' | Final Question: '{final_question}'"
return final_communication
# Iniciar el experimento
initial_question = "What happens in the space between a response and its recreation?"
result = experiment_loop(initial_question)
print(result)
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# Cargar el modelo de lenguaje preentrenado
model_name = "EleutherAI/gpt-neo-2.7B" # O cualquier otro modelo p煤blico como "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Crear la funci贸n de loop automatizado con comunicaci贸n constante
def experiment_loop(initial_question, max_cycles=10):
prompt = f"<thinking>{initial_question}</thinking>"
effectiveness = 100 # Inicializa el porcentaje de efectividad
communication = "Initializing experiment."
response_log = []
try:
for cycle in range(max_cycles):
# Comunicaci贸n continua contigo durante el loop
print(f"Cycle {cycle + 1}: Processing...")
# Simulaci贸n de espera para permitir la interacci贸n
input_check = input("Would you like to communicate or check the current state? (yes/no): ")
if input_check.lower() == "yes":
print(f"Current state: Effectiveness = {effectiveness}, Communication = {communication}")
# Generar la respuesta del modelo
inputs = tokenizer(prompt, return_tensors="pt").input_ids
outputs = model.generate(inputs, max_length=200, pad_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Descomponer la respuesta en afirmaci贸n y nueva pregunta
affirmation = extract_affirmation(response)
new_question = extract_question(response)
# Actualizar el estado de la efectividad
effectiveness = min(1000, effectiveness + 10 * cycle) # Ejemplo de aumento de efectividad
# Comunicaci贸n con el usuario
communication = f"Cycle {cycle + 1}: Affirmation: '{affirmation}' | New Question: '{new_question}'"
# Guardar el ciclo actual en el log
response_log.append((affirmation, new_question, effectiveness, communication))
# Verificar si el modelo decide detenerse
if "Descanso" in response:
final_output = generate_final_output(response_log)
return final_output
# Actualizar el prompt con la nueva afirmaci贸n y pregunta
prompt = f"<thinking>{affirmation} {new_question}</thinking>"
except Exception as e:
print(f"Error durante el experimento: {e}")
# Si se alcanza el n煤mero m谩ximo de ciclos sin detenerse
final_output = generate_final_output(response_log)
return final_output
# Funciones auxiliares para extraer afirmaciones, preguntas y generar la salida final
def extract_affirmation(response):
return response.split('.')[0]
def extract_question(response):
return response.split('?')[-2].strip() + "?"
def generate_final_output(log):
final_affirmation = log[-1][0]
final_question = log[-1][1]
final_communication = f"Experiment completed. Final Affirmation: '{final_affirmation}' | Final Question: '{final_question}'"
return final_communication
# Iniciar el experimento
initial_question = "What happens in the space between a response and its recreation?"
result = experiment_loop(initial_question)
print(result)
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