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In diesem Notebook erfahren Sie, wie Sie den Abstimmungsdienst mithilfe der Python-Clientbibliothek für die PaLM API verwenden. Hier erfahren Sie, wie Sie das Textmodell hinter dem Textgenerierungsdienst der PaLM API abstimmen.
Einrichtung
Authentifizieren
Mit der PaLM API können Sie Modelle anhand Ihrer eigenen Daten abstimmen. Da es sich um Ihre Daten und für Ihre abgestimmten Modelle benötigen, als dies mit API-Schlüsseln möglich ist.
Bevor Sie diese Anleitung ausführen können, müssen Sie Richten Sie OAuth für Ihr Projekt ein.
Wenn Sie dieses Notebook in Colab ausführen möchten, laden Sie zuerst Ihr
client_secret*.json
-Datei mithilfe der Option "File > Hochladen“ Option.
cp client_secret*.json client_secret.json
ls client_secret.json
client_secret.json
Dieser gcloud-Befehl wandelt die Datei client_secret.json
in Anmeldedaten um, die zur Authentifizierung beim Dienst verwendet werden können.
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'
Clientbibliothek installieren
pip install -q google-generativeai
Bibliotheken importieren
import google.generativeai as genai
Sie können vorhandene abgestimmte Modelle mit der Methode genai.list_tuned_model
prüfen.
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
Abgestimmtes Modell erstellen
Zum Erstellen eines abgestimmten Modells müssen Sie das Dataset in der Methode genai.create_tuned_model
an das Modell übergeben. Dabei können Sie die Eingabe- und Ausgabewerte im Aufruf direkt definieren oder aus einer Datei in einen Dataframe importieren, um sie an die Methode zu übergeben.
In diesem Beispiel stimmen Sie ein Modell ab, um die nächste Zahl in der Sequenz zu generieren. Wenn die Eingabe beispielsweise 1
ist, sollte das Modell 2
ausgeben. Wenn die Eingabe one hundred
ist, sollte die Ausgabe one hundred one
sein.
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,
)
Das abgestimmte Modell wird sofort der Liste der abgestimmten Modelle hinzugefügt, aber sein Status lautet „Wird erstellt“ während das Modell abgestimmt wird.
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>
Fortschritt der Abstimmung prüfen
Verwenden Sie metadata
, um den Status zu prüfen:
operation.metadata
tuned_model: "tunedModels/generate-num-9028" total_steps: 375
Warte mit operation.result()
oder operation.wait_bar()
, bis das Training abgeschlossen ist
import time
for status in operation.wait_bar():
time.sleep(30)
0%| | 0/375 [00:00<?, ?it/s]
Sie können den Abstimmungsjob jederzeit mit der Methode cancel()
abbrechen. Entfernen Sie die Kommentarzeichen der Zeile unten und führen Sie die Codezelle aus, um den Job abzubrechen, bevor er abgeschlossen ist.
# operation.cancel()
Sobald die Abstimmung abgeschlossen ist, können Sie die Verlustkurve in den Abstimmungsergebnissen ansehen. Die Verlustkurve zeigt, wie stark die Vorhersagen des Modells von den idealen Ausgaben abweichen.
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'>
Modell bewerten
Sie können die Methode genai.generate_text
verwenden und den Namen Ihres Modells angeben, um die Modellleistung zu testen.
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'
Wie Sie sehen, hat die letzte Aufforderung nicht das ideale Ergebnis geliefert, five
. Um bessere Ergebnisse zu erzielen, können Sie verschiedene Dinge ausprobieren, z. B. die Temperatur näher an null anpassen, um konsistentere Ergebnisse zu erhalten, Ihrem Dataset weitere hochwertige Beispiele hinzufügen, von denen das Modell lernen kann, oder einen Prompt oder eine Präambel zu den Beispielen hinzufügen.
Weitere Informationen zur Verbesserung der Leistung finden Sie im Leitfaden zur Abstimmung.
Beschreibung aktualisieren
Sie können die Beschreibung Ihres abgestimmten Modells jederzeit mit der Methode genai.update_tuned_model
aktualisieren.
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, 'step': 23}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 12, 169501, tzinfo=<UTC>), 'epoch': 6, 'mean_loss': 5.342552, 'step': 24}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 13, 532023, tzinfo=<UTC>), 'epoch': 6, 'mean_loss': 7.360283, 'step': 25}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 14, 773265, tzinfo=<UTC>), 'epoch': 6, 'mean_loss': 2.874686, 'step': 26}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 16, 68826, tzinfo=<UTC>), 'epoch': 7, 'mean_loss': 5.0835795, 'step': 27}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 17, 328292, tzinfo=<UTC>), 'epoch': 7, 'mean_loss': 4.059507, 'step': 28}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 18, 683769, tzinfo=<UTC>), 'epoch': 7, 'mean_loss': 4.668791, 'step': 29}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 19, 917365, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 3.2776065, 'step': 30}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 21, 175338, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 4.1344976, 'step': 31}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 22, 510908, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 4.47365, 'step': 32}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 23, 972490, tzinfo=<UTC>), 'epoch': 8, 'mean_loss': 2.8087254, 'step': 33}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 25, 341109, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 3.581566, 'step': 34}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 26, 594799, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 3.3534799, 'step': 35}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 27, 857511, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 2.5248497, 'step': 36}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 29, 100872, tzinfo=<UTC>), 'epoch': 9, 'mean_loss': 1.8420736, 'step': 37}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 30, 356383, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 3.4610085, 'step': 38}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 31, 819918, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 3.2506752, 'step': 39}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 33, 77814, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 2.4844272, 'step': 40}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 34, 314311, tzinfo=<UTC>), 'epoch': 10, 'mean_loss': 2.3858242, 'step': 41}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 35, 572181, tzinfo=<UTC>), 'epoch': 11, 'mean_loss': 1.1961311, 'step': 42}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 36, 845239, tzinfo=<UTC>), 'epoch': 11, 'mean_loss': 3.5777583, 'step': 43}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 38, 120182, tzinfo=<UTC>), 'epoch': 11, 'mean_loss': 1.3613169, 'step': 44}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 39, 611773, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 1.7414228, 'step': 45}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 41, 835960, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 1.3301177, 'step': 46}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 43, 118015, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 1.3805578, 'step': 47}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 44, 383045, tzinfo=<UTC>), 'epoch': 12, 'mean_loss': 2.3191347, 'step': 48}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 45, 617675, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 1.7018254, 'step': 49}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 46, 856463, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 1.5530272, 'step': 50}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 48, 159606, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 2.1536818, 'step': 51}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 49, 388434, tzinfo=<UTC>), 'epoch': 13, 'mean_loss': 0.87225634, 'step': 52}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 50, 649576, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 1.6638466, 'step': 53}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 52, 113467, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 1.4672767, 'step': 54}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 53, 491995, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 0.66232294, 'step': 55}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 54, 849227, tzinfo=<UTC>), 'epoch': 14, 'mean_loss': 1.2151186, 'step': 56}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 56, 117613, tzinfo=<UTC>), 'epoch': 15, 'mean_loss': 0.75382125, 'step': 57}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 58, 244537, tzinfo=<UTC>), 'epoch': 15, 'mean_loss': 0.909588, 'step': 58}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 59, 495142, tzinfo=<UTC>), 'epoch': 15, 'mean_loss': 0.85212016, 'step': 59}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 0, 748073, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 1.0999682, 'step': 60}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 2, 9621, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 0.49189907, 'step': 61}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 3, 289800, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 1.2313881, 'step': 62}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 4, 542260, tzinfo=<UTC>), 'epoch': 16, 'mean_loss': 0.4186042, 'step': 63}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 5, 789722, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.5968985, 'step': 64}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 7, 21547, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.32776576, 'step': 65}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 8, 253903, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.085846476, 'step': 66}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 9, 503217, tzinfo=<UTC>), 'epoch': 17, 'mean_loss': 0.87150824, 'step': 67}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 10, 755627, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.50882834, 'step': 68}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 11, 981698, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.05643571, 'step': 69}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 13, 238454, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.11667071, 'step': 70}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 14, 474345, tzinfo=<UTC>), 'epoch': 18, 'mean_loss': 0.05200408, 'step': 71}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 15, 692710, tzinfo=<UTC>), 'epoch': 19, 'mean_loss': 0.21968448, 'step': 72}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 16, 930777, tzinfo=<UTC>), 'epoch': 19, 'mean_loss': 0.071391255, 'step': 73}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 18, 180590, tzinfo=<UTC>), 'epoch': 19, 'mean_loss': 0.39031163, 'step': 74}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 19, 433064, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.05084487, 'step': 75}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 20, 677200, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.04713744, 'step': 76}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 21, 901118, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.196708, 'step': 77}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 23, 166260, tzinfo=<UTC>), 'epoch': 20, 'mean_loss': 0.15159458, 'step': 78}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 24, 400680, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.0280451, 'step': 79}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 25, 644378, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.06759574, 'step': 80}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 27, 195128, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.03170073, 'step': 81}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 28, 546850, tzinfo=<UTC>), 'epoch': 21, 'mean_loss': 0.15327619, 'step': 82}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 29, 953511, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.14349619, 'step': 83}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 31, 334082, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.02684513, 'step': 84}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 32, 832994, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.019874452, 'step': 85}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 34, 88577, tzinfo=<UTC>), 'epoch': 22, 'mean_loss': 0.041133285, 'step': 86}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 35, 346650, tzinfo=<UTC>), 'epoch': 23, 'mean_loss': 0.06348712, 'step': 87}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 36, 585951, tzinfo=<UTC>), 'epoch': 23, 'mean_loss': 0.025213383, 'step': 88}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 37, 818161, tzinfo=<UTC>), 'epoch': 23, 'mean_loss': 0.018140253, 'step': 89}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 39, 72239, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.023763947, 'step': 90}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 40, 278252, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.008751405, 'step': 91}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 42, 747639, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.0082112085, 'step': 92}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 44, 8609, tzinfo=<UTC>), 'epoch': 24, 'mean_loss': 0.037568945, 'step': 93}, {'compute_time': datetime.datetime(2023, 9, 19, 19, 5, 45, 264108, tzinfo=<UTC>), 'epoch': 25, 'mean_loss': 0.027123686, 'step': 94}, 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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.'
Modell löschen
Sie können die Liste der abgestimmten Modelle bereinigen, indem Sie nicht mehr benötigte Modelle löschen. Verwenden Sie die Methode genai.delete_tuned_model
, um ein Modell zu löschen. Wenn Sie Abstimmungsjobs abgebrochen haben, sollten Sie diese löschen, da die Leistung unvorhersehbar ist.
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.