 ดูใน ai.google.dev
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 ลองใช้ Colab Notebook
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 ดูสมุดบันทึกใน GitHub
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 ดาวน์โหลดสมุดบันทึก
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ในสมุดบันทึกนี้ คุณจะได้ดูวิธีเริ่มต้นใช้งานบริการปรับแต่งโดยใช้ไลบรารีของไคลเอ็นต์ Python สำหรับ PaLM API ในหัวข้อนี้ คุณจะได้ดูวิธีปรับแต่งโมเดลข้อความเบื้องหลังบริการสร้างข้อความของ PaLM API
ตั้งค่า
ตรวจสอบสิทธิ์
PaLM API ช่วยให้คุณปรับแต่งโมเดลจากข้อมูลของคุณเองได้ เนื่องจากเป็นข้อมูลของคุณและ โมเดลที่คุณปรับแต่งซึ่งต้องการการควบคุมการเข้าถึงที่เข้มงวดกว่าคีย์ API
ก่อนที่คุณจะสามารถเรียกใช้บทแนะนำนี้ คุณจะต้อง ตั้งค่า OAuth สำหรับโปรเจ็กต์
หากต้องการเรียกใช้สมุดบันทึกนี้ใน Colab ให้เริ่มด้วยการอัปโหลด
client_secret*.json ไฟล์ที่ใช้ "ไฟล์ > อัปโหลด" ตัวเลือก
cp client_secret*.json client_secret.jsonls client_secret.json
client_secret.json
คำสั่ง gcloud นี้จะเปลี่ยนไฟล์ client_secret.json เป็นข้อมูลเข้าสู่ระบบที่ใช้ตรวจสอบสิทธิ์กับบริการได้
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'
ติดตั้งไลบรารีของไคลเอ็นต์
pip install -q google-generativeai
นำเข้าไลบรารี
import google.generativeai as genai
คุณตรวจสอบโมเดลที่ปรับแต่งที่มีอยู่แล้วได้โดยใช้เมธอด 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
สร้างโมเดลที่ปรับแต่ง
หากต้องการสร้างโมเดลที่ปรับแต่ง คุณต้องส่งชุดข้อมูลไปยังโมเดลในเมธอด genai.create_tuned_model โดยกำหนดค่าอินพุตและเอาต์พุตในการเรียกใช้โดยตรง หรือนำเข้าจากไฟล์ไปยัง DataFrame เพื่อส่งไปยังเมธอด
สำหรับตัวอย่างนี้ คุณจะปรับแต่งโมเดลเพื่อสร้างตัวเลขถัดไปในลำดับ ตัวอย่างเช่น หากอินพุตคือ 1 โมเดลควรแสดงผลเป็น 2 หากอินพุตคือ one hundred เอาต์พุตควรเป็น 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,
)
ระบบจะเพิ่มโมเดลที่คุณปรับแต่งลงในรายการโมเดลที่ปรับแต่งทันที แต่สถานะตั้งค่าเป็น "กำลังสร้าง" ขณะปรับแต่งโมเดล
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>
ตรวจสอบความคืบหน้าในการปรับแต่ง
ใช้ metadata เพื่อตรวจสอบสถานะ
operation.metadata
tuned_model: "tunedModels/generate-num-9028" total_steps: 375
รอให้การฝึกเสร็จสิ้นโดยใช้ operation.result() หรือ operation.wait_bar()
import time
for status in operation.wait_bar():
time.sleep(30)
0%| | 0/375 [00:00<?, ?it/s]
คุณสามารถยกเลิกงานการปรับแต่งได้ทุกเมื่อโดยใช้เมธอด cancel() ยกเลิกการเลือกบรรทัดด้านล่างแล้วเรียกใช้เซลล์โค้ดเพื่อยกเลิกงานก่อนที่งานจะเสร็จสิ้น
# operation.cancel()
เมื่อการปรับแต่งเสร็จสมบูรณ์ คุณสามารถดูเส้นโค้งการสูญเสียได้จากผลการปรับแต่ง เส้นโค้งการสูญเสียจะแสดงว่าการคาดการณ์ของโมเดลเบี่ยงเบนจากเอาต์พุตอุดมคติมากน้อยเพียงใด
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'>
ประเมินโมเดล
คุณสามารถใช้เมธอด genai.generate_text และระบุชื่อโมเดลเพื่อทดสอบประสิทธิภาพของโมเดล
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'
จะเห็นได้ว่าพรอมต์สุดท้ายไม่ได้ให้ผลลัพธ์ที่ดีที่สุด ซึ่งก็คือ five เพื่อให้ได้ผลลัพธ์ที่ดีขึ้น คุณสามารถลองใช้วิธีที่ต่างออกไป เช่น การปรับอุณหภูมิให้เข้าใกล้ 0 เพื่อให้ได้ผลลัพธ์ที่สม่ำเสมอมากขึ้น การเพิ่มตัวอย่างที่มีคุณภาพมากขึ้นให้กับชุดข้อมูลที่โมเดลสามารถเรียนรู้ได้ หรือเพิ่มพรอมต์หรือคำนำในตัวอย่าง
ดูคําแนะนําเพิ่มเติมเกี่ยวกับการปรับปรุงประสิทธิภาพได้ในคู่มือการปรับแต่ง
อัปเดตคำอธิบาย
คุณอัปเดตคำอธิบายโมเดลที่ปรับแต่งได้ทุกเมื่อโดยใช้เมธอด 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,
'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,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 29, 226304, tzinfo=<UTC>),
'epoch': 83,
'mean_loss': 9.968644e-06,
'step': 314},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 30, 493632, tzinfo=<UTC>),
'epoch': 84,
'mean_loss': 0.00027738986,
'step': 315},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 31, 735723, tzinfo=<UTC>),
'epoch': 84,
'mean_loss': 0.0004675896,
'step': 316},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 32, 954139, tzinfo=<UTC>),
'epoch': 84,
'mean_loss': 0.00014443416,
'step': 317},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 34, 196120, tzinfo=<UTC>),
'epoch': 84,
'mean_loss': 0.0006946635,
'step': 318},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 35, 443514, tzinfo=<UTC>),
'epoch': 85,
'mean_loss': 0.0007360133,
'step': 319},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 36, 687667, tzinfo=<UTC>),
'epoch': 85,
'mean_loss': 1.326669e-06,
'step': 320},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 37, 903099, tzinfo=<UTC>),
'epoch': 85,
'mean_loss': 0.0005314335,
'step': 321},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 39, 138629, tzinfo=<UTC>),
'epoch': 85,
'mean_loss': 6.947189e-05,
'step': 322},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 40, 371666, tzinfo=<UTC>),
'epoch': 86,
'mean_loss': 0.00053617253,
'step': 323},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 41, 602304, tzinfo=<UTC>),
'epoch': 86,
'mean_loss': 9.1956696e-05,
'step': 324},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 42, 836714, tzinfo=<UTC>),
'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.'
ลบโมเดล
คุณล้างข้อมูลรายการโมเดลที่ปรับแต่งได้โดยการลบโมเดลที่ไม่จำเป็นต้องใช้แล้ว ใช้เมธอด genai.delete_tuned_model เพื่อลบโมเดล หากคุณยกเลิกงานการปรับแต่ง คุณอาจต้องการลบงานเหล่านั้นเนื่องจากประสิทธิภาพอาจคาดเดาไม่ได้
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.