| import torch | |
| from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM | |
| # Cargar el modelo de lenguaje preentrenado | |
| model_name = "gpt-neo-2.7B" # Puedes cambiarlo a GPT-J o cualquier otro | |
| 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 = [] | |
| 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>" | |
| # 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) | |