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En este notebook, aprenderás a comenzar a usar la API de PaLM, que te da acceso a los modelos grandes de lenguaje más recientes de Google. Aquí, aprenderás a usar las funciones de generación de texto de la API de PaLM.
Configuración
Primero, descarga e instala la biblioteca de Python de la API de PaLM.
pip install -q google-generativeai
import pprint
import google.generativeai as palm
Obtén una clave de API
Para comenzar, deberás crear una clave de API.
palm.configure(api_key='YOUR_API_KEY')
Generación de texto
Usa la función palm.list_models para encontrar los modelos disponibles:
models = [m for m in palm.list_models() if 'generateText' in m.supported_generation_methods]
model = models[0].name
print(model)
models/text-bison-001
Usa el método palm.generate_text para generar texto:
prompt = """
You are an expert at solving word problems.
Solve the following problem:
I have three houses, each with three cats.
each cat owns 4 mittens, and a hat. Each mitten was
knit from 7m of yarn, each hat from 4m.
How much yarn was needed to make all the items?
Think about it step by step, and show your work.
"""
completion = palm.generate_text(
model=model,
prompt=prompt,
temperature=0,
# The maximum length of the response
max_output_tokens=800,
)
print(completion.result)
There are 3 houses * 3 cats / house = 9 cats. So, 9 cats * 4 mittens / cat = 36 mittens were made. Also, 9 cats * 1 hat / cat = 9 hats were made. So, 36 mittens * 7m / mitten = 252m of yarn was used for the mittens. Also, 9 hats * 4m / hat = 36m of yarn was used for the hats. In total, 252m + 36m = 288m of yarn was used. Thus, the answer is 288.
Más opciones
La función palm.generate_text tiene otros argumentos que vale la pena mencionar.
Secuencias de detención
Usa el argumento stop_sequences para detener la generación antes.
Por ejemplo, los LLM suelen cometer errores aritméticos. Podrías pedirle al modelo que use una calculadora colocando ecuaciones en una etiqueta <calc>
Haz que el modelo se detenga en la etiqueta de cierre para que puedas editar la instrucción:
calc_prompt = f"""
Please solve the following problem.
{prompt}
----------------
Important: Use the calculator for each step.
Don't do the arithmetic in your head.
To use the calculator wrap an equation in <calc> tags like this:
<calc> 3 cats * 2 hats/cat </calc> = 6
----------------
"""
equation=None
while equation is None:
completion = palm.generate_text(
model=model,
prompt=calc_prompt,
stop_sequences=['</calc>'],
# The maximum length of the response
max_output_tokens=800,
)
try:
response, equation = completion.result.split('<calc>', maxsplit=1)
except Exception:
continue
print(response)
Chain-of-thought: There are three houses, and each house has three cats, so there are 3 houses * 3 cats / house = 9 cats. Each cat has 4 mittens, so the cats need 9 cats * 4 mittens / cat = 36 mittens. Each mitten takes 7m of yarn, so 36 mittens * 7m / mitten = 252m of yarn. Each cat has a hat, and each hat takes 4m of yarn, so 9 cats * 4m / cat = 36m of yarn. So, in total, 36m + 252m = 288m of yarn were needed. The answer should be
print(equation)
9 cats * 4 mittens / cat
A partir de ahí, puedes calcular el resultado y armar una nueva instrucción para que el modelo continúe. Para obtener una implementación funcional completa, consulta el ejemplo de calculadora de texto.
Candidatos
Por lo general, existe algún grado de aleatorización en el texto producido por los LLM. (Obtén más información sobre las razones de esto en el primer manual de LLM). Esto significa que cuando llamas a la API más de una vez con la misma entrada, puedes obtener respuestas diferentes. Puedes aprovechar esta función para obtener respuestas alternativas del modelo.
El argumento temperature controla la varianza de las respuestas. El objeto palm.Model proporciona el valor predeterminado para temperature y otros parámetros.
models[0]
Model(name='models/text-bison-001', base_model_id='', version='001', display_name='Text Bison', description='Model targeted for text generation.', input_token_limit=8196, output_token_limit=1024, supported_generation_methods=['generateText'], temperature=0.7, top_p=0.95, top_k=40)
El argumento candidate_count controla la cantidad de respuestas que se muestran:
completion = palm.generate_text(
model=model,
prompt=prompt,
# The number of candidates to return
candidate_count=8,
# Set the temperature to 1.0 for more variety of responses.
temperature=1.0,
max_output_tokens=800,
)
print(completion.result)
In each house there are 3 cats * 4 mittens / cat = 12 mittens. In total there are 3 houses * 12 mittens / house = 36 mittens. In total there are 36 mittens * 7m / mitten = 252m of yarn for the mittens. In total there are 3 houses * 3 cats / house * 1 hat / cat = 9 hats. In total there are 9 hats * 4m / hat = 36m of yarn for the hats. In total there are 36m yarn for the hats + 252m yarn for the mittens = 288m of yarn. The answer: 288.
Cuando solicitas varios candidatos, el atributo Completion.result solo contiene el primero. El atributo Completion.candidates contiene todos ellos:
import pprint
pprint.pprint(completion.candidates)
[{'output': 'In each house there are 3 cats * 4 mittens / cat = 12 mittens. In '
'total there are 3 houses * 12 mittens / house = 36 mittens. In '
'total there are 36 mittens * 7m / mitten = 252m of yarn for the '
'mittens. In total there are 3 houses * 3 cats / house * 1 hat / '
'cat = 9 hats. In total there are 9 hats * 4m / hat = 36m of yarn '
'for the hats. In total there are 36m yarn for the hats + 252m '
'yarn for the mittens = 288m of yarn.\n'
'The answer: 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'Each house has 3 cats, so each house needs 3 * 4 = 12 mittens. '
"With three houses, that's 3 * 12 = 36 mittens. And each house "
'needs 3 * 1 = 3 hats. So in total, we need 3 hats + 36 mittens = '
'39 items. Each mitten needs 7 meters of yarn, so 39 mittens need '
'39 * 7 = 273 meters of yarn. Each hat needs 4 meters of yarn, and '
"we need 3 hats, so that's 4 * 3 = 12 meters of yarn. So in total, "
'we needed 12 + 273 = 285 meters of yarn.\n'
'Thus, the answer is 285.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'There are 3 houses * 3 cats / house = 9 cats. There are 9 cats * '
'4 mittens / cat = 36 mittens. There are 9 cats * 1 hat / cat = 9 '
'hats. The total amount of yarn for the mittens is 36 mittens * 7m '
'/ mitten = 252m. The total amount of yarn for the hats is 9 hats '
'* 4m / hat = 36m. The total amount of yarn is 252m + 36m = 288m.\n'
'Thus, the answer is 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'There are 3 houses * 3 cats / house = 9 cats. Each cat has 4 '
'mittens + 1 hat = 5 items. So the total number of items is 9 cats '
'* 5 items / cat = 45 items. Thus, 45 items * 7m / item = 315m of '
'yarn was needed.\n'
'Thus, the answer is 315.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'Chain-of-thought:\n'
'There are 3 houses * 3 cats / house = 9 cats.\n'
'The cats need 9 cats * 4 mittens / cat = 36 mittens.\n'
'The cats need 9 cats * 1 hat / cat = 9 hats.\n'
'The mittens need 36 mittens * 7m / mitten = 252m of yarn.\n'
'The hats need 9 hats * 4m / hat = 36m of yarn.\n'
'Therefore, the total amount of yarn needed is 252m + 36m = 288m.\n'
'\n'
'The answer should be 288',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'First find the total number of cats: 3 houses * 3 cats / house = '
'9 cats. Then multiply that number by the number of mittens per '
'cat to find the total number of mittens: 9 cats * 4 mittens / cat '
'= 36 mittens. Then multiply that number by the number of meters '
'of yarn per mitten to find the total amount of yarn used for '
'mittens: 36 mittens * 7 meters / mitten = 252 meters. Then do the '
'same thing for hats: 9 cats * 1 hat / cat = 9 hats. Then multiply '
'that number by the number of meters of yarn per hat to find the '
'total amount of yarn used for hats: 9 hats * 4 meters / hat = 36 '
'meters. Then add the amount of yarn used for mittens and hats to '
'find the total amount of yarn used: 36 meters + 252 meters = 288 '
'meters.\n'
'Thus, the answer is 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'The total number of cats is 3 houses * 3 cats / house = 9 cats. '
'So, the total number of mittens is 9 cats * 4 mittens / cat = 36 '
'mittens. The total number of hats is 9 cats * 1 hat / cat = 9 '
'hats. The total length of yarn needed to make the mittens is 36 '
'mittens * 7 m / mitten = 252 m. The total length of yarn needed '
'to make the hats is 9 hats * 4 m / hat = 36 m. So, the total '
'length of yarn needed is 252 m + 36 m = 288 m.\n'
'\n'
'The answer: 288',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]},
{'output': 'There are 3 houses with 3 cats each, so 3 * 3 = 9 cats. Each cat '
'has 4 mittens and a hat, so 9 cats * 4 mittens / cat + 9 cats * 1 '
'hat / cat = 36 mittens and 9 hats. Each mitten takes 7m of yarn '
'and each hat takes 4m of yarn, so the total yarn needed is 36 '
'mittens * 7m / mitten + 9 hats * 4m / hat = 252m + 36m = 288m.\n'
'The answer: 288.',
'safety_ratings': [{'category': <HarmCategory.HARM_CATEGORY_DEROGATORY: 1>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_TOXICITY: 2>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_VIOLENCE: 3>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_SEXUAL: 4>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_MEDICAL: 5>,
'probability': <HarmProbability.NEGLIGIBLE: 1>},
{'category': <HarmCategory.HARM_CATEGORY_DANGEROUS: 6>,
'probability': <HarmProbability.NEGLIGIBLE: 1>}]}]
Entonces, como conoces la respuesta a este problema, es fácil comprobar la tasa de resolución:
import numpy as np
np.mean(['288' in c['output'] for c in completion.candidates])
0.75