 להצגה ב-ai.google.dev
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 כדאי לנסות notebook של Colab
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 הצגת notebook ב-GitHub
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 להורדת ה-notebook
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ב-notebook הזה תלמדו איך להתחיל להשתמש בשירות הכוונון באמצעות ספריית הלקוח של Python ל-PaLM API. כאן נסביר איך לכוונן את מודל הטקסט שמאחורי שירות יצירת הטקסט של PaLM API.
הגדרה
אמת
PaLM API מאפשר לכוונן מודלים לפי הנתונים שלכם. כי מדובר בנתונים שלכם, למודלים המכווננים שלך נדרשים בקרת גישה מחמירה יותר מאשר מפתחות API.
כדי להפעיל את המדריך, צריך: כך מגדירים OAuth לפרויקט שלכם.
כדי להריץ את ה-notebook הזה ב-Colab, צריך להעלות את
קובץ client_secret*.json באמצעות 'File >' העלאה" כאפשרות.
cp client_secret*.json client_secret.jsonls client_secret.json
client_secret.json
הפקודה ב-gcloud הופכת את הקובץ client_secret.json לפרטי כניסה שבהם אפשר להשתמש כדי לבצע אימות עם השירות.
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'
התקנת ספריית הלקוח
pip install -q google-generativeai
ייבוא ספריות
import google.generativeai as genai
אפשר לבדוק את המודלים המכווננים הקיימים בשיטה 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
יצירת מודל מכוונן
כדי ליצור מודל שעבר כוונון, צריך להעביר את מערך הנתונים למודל בשיטה genai.create_tuned_model. אפשר לעשות זאת באופן ישיר, להגדיר את ערכי הקלט והפלט בקריאה או לייבא מקובץ ל-dataframe שיועברו ל-method.
בדוגמה הזו, תכווננו מודל כדי ליצור את המספר הבא ברצף. לדוגמה, אם הקלט הוא 1, הפלט של המודל צריך להיות 2. אם הקלט הוא one hundred, הפלט צריך להיות 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,
)
המודל המכוונן נוסף מיד לרשימת המודלים המכווננים, אבל הסטטוס שלו מוגדר כ'יוצר' בזמן שהמודל מכוונן.
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>
בדיקת התקדמות הכוונון
יש להשתמש ב-metadata כדי לבדוק את המדינה (State):
operation.metadata
tuned_model: "tunedModels/generate-num-9028" total_steps: 375
צריך להמתין עד שהאימון יסתיים באמצעות operation.result() או operation.wait_bar()
import time
for status in operation.wait_bar():
time.sleep(30)
0%| | 0/375 [00:00<?, ?it/s]
אפשר לבטל את פעולת הכוונון בכל שלב באמצעות השיטה cancel(). מבטלים תגובה לשורה שלמטה ומריצים את תא הקוד כדי לבטל את המשימה לפני שהיא תסתיים.
# operation.cancel()
לאחר השלמת הכוונון, ניתן יהיה להציג את עקומת אובדן הנתונים מתוצאות הכוונון. עקומת ההפסד מראה עד כמה יש סטייה בתחזיות של המודל מהפלט האידיאלי.
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'>
הערכת המודל
אפשר להשתמש בשיטה genai.generate_text ולציין את שם המודל כדי לבדוק את ביצועי המודל.
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'
כמו שאפשר לראות, ההנחיה האחרונה לא הניבה את התוצאה האידיאלית, five. כדי לקבל תוצאות טובות יותר, אפשר לנסות כמה פעולות שונות, כמו שינוי הטמפרטורה קרוב לאפס, כדי לקבל תוצאות עקביות יותר, הוספת עוד דוגמאות איכותיות למערך הנתונים שהמודל יוכל ללמוד מהן או הוספת הנחיה או קידומת לדוגמאות.
תוכלו למצוא מידע נוסף במדריך הכוונון לשיפור הביצועים.
עדכון התיאור
אפשר לעדכן את תיאור המודל המכוונן בכל שלב באמצעות השיטה 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,
'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,
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{'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,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 4, 43, 118015, tzinfo=<UTC>),
'epoch': 12,
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{'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,
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'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},
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'epoch': 86,
'mean_loss': 0.00018627953,
'step': 325},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 44, 874288, tzinfo=<UTC>),
'epoch': 86,
'mean_loss': 0.0002088271,
'step': 326},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 46, 162027, tzinfo=<UTC>),
'epoch': 87,
'mean_loss': 0.00075449655,
'step': 327},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 47, 431864, tzinfo=<UTC>),
'epoch': 87,
'mean_loss': 7.8588026e-05,
'step': 328},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 48, 680277, tzinfo=<UTC>),
'epoch': 87,
'mean_loss': -1.3336539e-06,
'step': 329},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 49, 968707, tzinfo=<UTC>),
'epoch': 88,
'mean_loss': 0.00012271712,
'step': 330},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 51, 259023, tzinfo=<UTC>),
'epoch': 88,
'mean_loss': 0.0017514592,
'step': 331},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 52, 511807, tzinfo=<UTC>),
'epoch': 88,
'mean_loss': 4.1678373e-05,
'step': 332},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 53, 808914, tzinfo=<UTC>),
'epoch': 88,
'mean_loss': 0.0006764167,
'step': 333},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 55, 179417, tzinfo=<UTC>),
'epoch': 89,
'mean_loss': 0.00013730745,
'step': 334},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 56, 395678, tzinfo=<UTC>),
'epoch': 89,
'mean_loss': 0.00032095844,
'step': 335},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 57, 654428, tzinfo=<UTC>),
'epoch': 89,
'mean_loss': 0.00015303271,
'step': 336},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 58, 891717, tzinfo=<UTC>),
'epoch': 89,
'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.'
מחיקת המודל
אפשר לנקות את רשימת המודלים המכווננים על ידי מחיקת מודלים שכבר אין לך צורך בהם. אפשר להשתמש בשיטה genai.delete_tuned_model כדי למחוק מודל. אם ביטלתם משימות כוונון, כדאי למחוק אותן כי הביצועים שלהן עלולים להיות לא צפויים.
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.