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En este notebook, aprenderás a comenzar a usar el servicio de ajuste con la biblioteca cliente de Python para la API de PaLM. Aquí, aprenderás a ajustar el modelo de texto detrás del servicio de generación de texto de la API de PaLM.
Configuración
Autenticar
La API de PaLM te permite ajustar los modelos con tus propios datos. Dado que son tus datos y Para tus modelos ajustados, se necesitan controles de acceso más estrictos que los que pueden proporcionar las claves de API.
Antes de ejecutar este instructivo, deberás Configurar OAuth para tu proyecto
Si quieres ejecutar este notebook en Colab, comienza por subir tu
client_secret*.json con la opción "Archivo > Subir" de 12 a 1 con la nueva opción de compresión.
cp client_secret*.json client_secret.jsonls client_secret.json
client_secret.json
Este comando de gcloud convierte el archivo client_secret.json en credenciales que se pueden usar para autenticarse con el servicio.
import os
if 'COLAB_RELEASE_TAG' in os.environ:
# Use `--no-browser` in colab
!gcloud auth application-default login --no-browser --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'
else:
!gcloud auth application-default login --client-id-file client_secret.json --scopes='https://www.googleapis.com/auth/cloud-platform,https://www.googleapis.com/auth/generative-language.tuning'
Instala la biblioteca cliente
pip install -q google-generativeai
Importa las bibliotecas
import google.generativeai as genai
Puedes verificar tus modelos ajustados existentes con el método genai.list_tuned_model.
for i, m in zip(range(5), genai.list_tuned_models()):
print(m.name)
tunedModels/my-model-8527 tunedModels/my-model-7092 tunedModels/my-model-2778 tunedModels/my-model-1298 tunedModels/my-model-3883
Crear modelo ajustado
Para crear un modelo ajustado, debes pasar tu conjunto de datos al modelo en el método genai.create_tuned_model. Puedes hacerlo definiendo directamente los valores de entrada y salida en la llamada o importando desde un archivo a un DataFrame para pasar al método.
En este ejemplo, ajustarás un modelo para generar el siguiente número en la secuencia. Por ejemplo, si la entrada es 1, el modelo debería generar 2. Si la entrada es one hundred, el resultado debe ser one hundred one.
base_model = [
m for m in genai.list_models()
if "createTunedTextModel" in m.supported_generation_methods][0]
base_model.name
'models/text-bison-001'
import random
name = f'generate-num-{random.randint(0,10000)}'
operation = genai.create_tuned_model(
# You can use a tuned model here too. Set `source_model="tunedModels/..."`
source_model=base_model.name,
training_data=[
{
'text_input': '1',
'output': '2',
},{
'text_input': '3',
'output': '4',
},{
'text_input': '-3',
'output': '-2',
},{
'text_input': 'twenty two',
'output': 'twenty three',
},{
'text_input': 'two hundred',
'output': 'two hundred one',
},{
'text_input': 'ninety nine',
'output': 'one hundred',
},{
'text_input': '8',
'output': '9',
},{
'text_input': '-98',
'output': '-97',
},{
'text_input': '1,000',
'output': '1,001',
},{
'text_input': '10,100,000',
'output': '10,100,001',
},{
'text_input': 'thirteen',
'output': 'fourteen',
},{
'text_input': 'eighty',
'output': 'eighty one',
},{
'text_input': 'one',
'output': 'two',
},{
'text_input': 'three',
'output': 'four',
},{
'text_input': 'seven',
'output': 'eight',
}
],
id = name,
epoch_count = 100,
batch_size=4,
learning_rate=0.001,
)
Tu modelo ajustado se agrega inmediatamente a la lista de modelos ajustados, pero su estado se establece en “Creando”. mientras se ajusta el modelo.
model = genai.get_tuned_model(f'tunedModels/{name}')
model
TunedModel(name='tunedModels/generate-num-9028',
source_model='tunedModels/generate-num-4110',
base_model='models/text-bison-001',
display_name='',
description='',
temperature=0.7,
top_p=0.95,
top_k=40,
state=<State.CREATING: 1>,
create_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc),
update_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 188028, tzinfo=datetime.timezone.utc),
tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 29, 21, 37, 32, 734118, tzinfo=datetime.timezone.utc),
complete_time=None,
snapshots=[],
hyperparameters=Hyperparameters(epoch_count=100,
batch_size=4,
learning_rate=0.001)))
model.state
<State.CREATING: 1>
Verificar el progreso del ajuste
Usa metadata para verificar el estado:
operation.metadata
tuned_model: "tunedModels/generate-num-9028" total_steps: 375
Espera a que finalice el entrenamiento con operation.result() o operation.wait_bar().
import time
for status in operation.wait_bar():
time.sleep(30)
0%| | 0/375 [00:00<?, ?it/s]
Puedes cancelar tu trabajo de ajuste en cualquier momento con el método cancel(). Quita el comentario de la siguiente línea y ejecuta la celda de código para cancelar el trabajo antes de que finalice.
# operation.cancel()
Una vez que se complete el ajuste, podrás ver la curva de pérdida en los resultados del ajuste. La curva de pérdida muestra cuánto se desvían las predicciones del modelo de los resultados ideales.
import pandas as pd
import seaborn as sns
model = operation.result()
snapshots = pd.DataFrame(model.tuning_task.snapshots)
sns.lineplot(data=snapshots, x = 'epoch', y='mean_loss')
<Axes: xlabel='epoch', ylabel='mean_loss'>
Evalúa tu modelo
Puedes usar el método genai.generate_text y especificar el nombre de tu modelo para probar su rendimiento.
completion = genai.generate_text(model=f'tunedModels/{name}',
prompt='5')
completion.result
'6'
completion = genai.generate_text(model=f'tunedModels/{name}',
prompt='-9')
completion.result
'-8'
completion = genai.generate_text(model=f'tunedModels/{name}',
prompt='four')
completion.result
'four'
Como puedes ver, la última instrucción no produjo el resultado ideal, five. Para producir mejores resultados, puedes probar algunas cosas diferentes, como ajustar la temperatura más cerca de cero para obtener resultados más coherentes, agregar más ejemplos de calidad a tu conjunto de datos de los que el modelo pueda aprender o agregar una instrucción o un preámbulo a los ejemplos.
Consulta la guía de ajustes para obtener más indicaciones sobre cómo mejorar el rendimiento.
Actualiza la descripción
Puedes actualizar la descripción de tu modelo ajustado en cualquier momento con el método genai.update_tuned_model.
genai.update_tuned_model(f'tunedModels/{name}', {"description":"This is my model."})
TunedModel(name='', source_model=None, base_model=None, display_name='', description='This is my model.', temperature=None, top_p=None, top_k=None, state=<State.STATE_UNSPECIFIED: 0>, create_time=None, update_time=None, tuning_task=None)
model = genai.get_tuned_model(f'tunedModels/{name}')
model
TunedModel(name='tunedModels/generate-num-4668',
source_model=None,
base_model='models/text-bison-001',
display_name='',
description='This is my model.',
temperature=0.7,
top_p=0.95,
top_k=40,
state=<State.ACTIVE: 2>,
create_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 22249, tzinfo=<UTC>),
update_time=datetime.datetime(2023, 9, 19, 19, 11, 48, 101024, tzinfo=<UTC>),
tuning_task=TuningTask(start_time=datetime.datetime(2023, 9, 19, 19, 3, 38, 562798, tzinfo=<UTC>),
complete_time=datetime.datetime(2023, 9, 19, 19, 11, 48, 101024, tzinfo=<UTC>),
snapshots=[{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 41, 221503, tzinfo=<UTC>),
'epoch': 0,
'mean_loss': 7.2774773,
'step': 1},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 42, 611142, tzinfo=<UTC>),
'epoch': 0,
'mean_loss': 6.178241,
'step': 2},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 43, 886844, tzinfo=<UTC>),
'epoch': 0,
'mean_loss': 5.505934,
'step': 3},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 45, 213316, tzinfo=<UTC>),
'epoch': 1,
'mean_loss': 7.9365344,
'step': 4},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 46, 719674, tzinfo=<UTC>),
'epoch': 1,
'mean_loss': 7.656596,
'step': 5},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 48, 51559, tzinfo=<UTC>),
'epoch': 1,
'mean_loss': 7.3750257,
'step': 6},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 49, 419247, tzinfo=<UTC>),
'epoch': 1,
'mean_loss': 4.579882,
'step': 7},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 50, 902477, tzinfo=<UTC>),
'epoch': 2,
'mean_loss': 6.776862,
'step': 8},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 52, 213448, tzinfo=<UTC>),
'epoch': 2,
'mean_loss': 6.3564157,
'step': 9},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 53, 679693, tzinfo=<UTC>),
'epoch': 2,
'mean_loss': 8.558726,
'step': 10},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 55, 2348, tzinfo=<UTC>),
'epoch': 2,
'mean_loss': 4.783774,
'step': 11},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 56, 322485, tzinfo=<UTC>),
'epoch': 3,
'mean_loss': 7.0234137,
'step': 12},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 58, 145081, tzinfo=<UTC>),
'epoch': 3,
'mean_loss': 7.317513,
'step': 13},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 3, 59, 399317, tzinfo=<UTC>),
'epoch': 3,
'mean_loss': 5.85363,
'step': 14},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 0, 646995, tzinfo=<UTC>),
'epoch': 4,
'mean_loss': 4.21408,
'step': 15},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 1, 899798, tzinfo=<UTC>),
'epoch': 4,
'mean_loss': 6.6232214,
'step': 16},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 3, 167955, tzinfo=<UTC>),
'epoch': 4,
'mean_loss': 5.61497,
'step': 17},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 4, 407849, tzinfo=<UTC>),
'epoch': 4,
'mean_loss': 6.821261,
'step': 18},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 5, 649503, tzinfo=<UTC>),
'epoch': 5,
'mean_loss': 3.8338904,
'step': 19},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 7, 80497, tzinfo=<UTC>),
'epoch': 5,
'mean_loss': 5.0643735,
'step': 20},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 8, 401424, tzinfo=<UTC>),
'epoch': 5,
'mean_loss': 6.976447,
'step': 21},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 9, 688226, tzinfo=<UTC>),
'epoch': 5,
'mean_loss': 5.045044,
'step': 22},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 10, 942147, tzinfo=<UTC>),
'epoch': 6,
'mean_loss': 5.1944356,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 12, 169501, tzinfo=<UTC>),
'epoch': 6,
'mean_loss': 5.342552,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 13, 532023, tzinfo=<UTC>),
'epoch': 6,
'mean_loss': 7.360283,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 14, 773265, tzinfo=<UTC>),
'epoch': 6,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 16, 68826, tzinfo=<UTC>),
'epoch': 7,
'mean_loss': 5.0835795,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 17, 328292, tzinfo=<UTC>),
'epoch': 7,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 18, 683769, tzinfo=<UTC>),
'epoch': 7,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 19, 917365, tzinfo=<UTC>),
'epoch': 8,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 21, 175338, tzinfo=<UTC>),
'epoch': 8,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 25, 341109, tzinfo=<UTC>),
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 50, 649576, tzinfo=<UTC>),
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 10, 755627, tzinfo=<UTC>),
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 11, 981698, tzinfo=<UTC>),
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 15, 692710, tzinfo=<UTC>),
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 19, 433064, tzinfo=<UTC>),
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 24, 400680, tzinfo=<UTC>),
'epoch': 21,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 25, 644378, tzinfo=<UTC>),
'epoch': 21,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 27, 195128, tzinfo=<UTC>),
'epoch': 21,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 28, 546850, tzinfo=<UTC>),
'epoch': 21,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 29, 953511, tzinfo=<UTC>),
'epoch': 22,
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'step': 83},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 31, 334082, tzinfo=<UTC>),
'epoch': 22,
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'step': 84},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 32, 832994, tzinfo=<UTC>),
'epoch': 22,
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'mean_loss': 0.00012956047,
'step': 337},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 0, 151925, tzinfo=<UTC>),
'epoch': 90,
'mean_loss': 7.675003e-05,
'step': 338},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 1, 398761, tzinfo=<UTC>),
'epoch': 90,
'mean_loss': 0.00044489285,
'step': 339},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 2, 641274, tzinfo=<UTC>),
'epoch': 90,
'mean_loss': 1.4036312e-05,
'step': 340},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 3, 883546, tzinfo=<UTC>),
'epoch': 90,
'mean_loss': 0.00015219976,
'step': 341},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 5, 141535, tzinfo=<UTC>),
'epoch': 91,
'mean_loss': 0.00018677826,
'step': 342},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 6, 373784, tzinfo=<UTC>),
'epoch': 91,
'mean_loss': 0.000115128816,
'step': 343},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 7, 645535, tzinfo=<UTC>),
'epoch': 91,
'mean_loss': 0.00039966288,
'step': 344},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 8, 884670, tzinfo=<UTC>),
'epoch': 92,
'mean_loss': 0.0001351597,
'step': 345},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 10, 149429, tzinfo=<UTC>),
'epoch': 92,
'mean_loss': 6.1459374e-05,
'step': 346},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 11, 448963, tzinfo=<UTC>),
'epoch': 92,
'mean_loss': 0.00023051281,
'step': 347},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 12, 693703, tzinfo=<UTC>),
'epoch': 92,
'mean_loss': 0.00078510307,
'step': 348},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 13, 938911, tzinfo=<UTC>),
'epoch': 93,
'mean_loss': 8.103554e-06,
'step': 349},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 15, 460723, tzinfo=<UTC>),
'epoch': 93,
'mean_loss': 0.0019005266,
'step': 350},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 16, 766511, tzinfo=<UTC>),
'epoch': 93,
'mean_loss': 6.863149e-06,
'step': 351},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 18, 27508, tzinfo=<UTC>),
'epoch': 93,
'mean_loss': 0.0002926389,
'step': 352},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 19, 286497, tzinfo=<UTC>),
'epoch': 94,
'mean_loss': 0.00013998023,
'step': 353},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 20, 556785, tzinfo=<UTC>),
'epoch': 94,
'mean_loss': 2.2997847e-05,
'step': 354},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 21, 804735, tzinfo=<UTC>),
'epoch': 94,
'mean_loss': 0.0005936278,
'step': 355},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 23, 22367, tzinfo=<UTC>),
'epoch': 94,
'mean_loss': 3.43258e-05,
'step': 356},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 24, 284890, tzinfo=<UTC>),
'epoch': 95,
'mean_loss': 0.00010312116,
'step': 357},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 25, 525734, tzinfo=<UTC>),
'epoch': 95,
'mean_loss': 0.00015714776,
'step': 358},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 26, 780375, tzinfo=<UTC>),
'epoch': 95,
'mean_loss': 5.73016e-05,
'step': 359},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 28, 23466, tzinfo=<UTC>),
'epoch': 96,
'mean_loss': 0.00012817327,
'step': 360},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 29, 268204, tzinfo=<UTC>),
'epoch': 96,
'mean_loss': 3.9030332e-05,
'step': 361},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 30, 507390, tzinfo=<UTC>),
'epoch': 96,
'mean_loss': 0.0005360425,
'step': 362},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 31, 727121, tzinfo=<UTC>),
'epoch': 96,
'mean_loss': 0.00017444952,
'step': 363},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 33, 34563, tzinfo=<UTC>),
'epoch': 97,
'mean_loss': 0.0010171408,
'step': 364},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 34, 447584, tzinfo=<UTC>),
'epoch': 97,
'mean_loss': 0.0004899306,
'step': 365},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 35, 699821, tzinfo=<UTC>),
'epoch': 97,
'mean_loss': 0.00017226115,
'step': 366},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 36, 936549, tzinfo=<UTC>),
'epoch': 97,
'mean_loss': 4.2724423e-07,
'step': 367},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 38, 203783, tzinfo=<UTC>),
'epoch': 98,
'mean_loss': 1.9560219e-05,
'step': 368},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 39, 464518, tzinfo=<UTC>),
'epoch': 98,
'mean_loss': 0.00011098804,
'step': 369},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 40, 721560, tzinfo=<UTC>),
'epoch': 98,
'mean_loss': 0.0005288075,
'step': 370},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 41, 968386, tzinfo=<UTC>),
'epoch': 98,
'mean_loss': 4.2606727e-05,
'step': 371},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 43, 207703, tzinfo=<UTC>),
'epoch': 99,
'mean_loss': 1.1964934e-05,
'step': 372},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 45, 297458, tzinfo=<UTC>),
'epoch': 99,
'mean_loss': 0.00035788305,
'step': 373},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 46, 714760, tzinfo=<UTC>),
'epoch': 99,
'mean_loss': 7.525133e-05,
'step': 374},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 11, 48, 17671, tzinfo=<UTC>),
'epoch': 100,
'mean_loss': 5.5355486e-06,
'step': 375}],
hyperparameters=Hyperparameters(epoch_count=100,
batch_size=4,
learning_rate=0.001)))
model.description
'This is my model.'
Borra el modelo
Para limpiar tu lista de modelos ajustados, puedes borrar los modelos que ya no necesitas. Usa el método genai.delete_tuned_model para borrar un modelo. Si cancelaste algún trabajo de ajuste, te recomendamos que lo borres, ya que su rendimiento puede ser impredecible.
genai.delete_tuned_model(f'tunedModels/{name}')
try:
m = genai.get_tuned_model(f'tunedModels/{name}')
print(m)
except Exception as e:
print(f"{type(e)}: {e}")
<class 'google.api_core.exceptions.NotFound'>: 404 Tuned model tunedModels/generate-num-4668 does not exist.