 ai.google.dev पर देखें
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 Colab notebook को आज़माएं
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 GitHub पर notebook देखें
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 नोटबुक डाउनलोड करें
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इस notebook में, PaLM API के लिए Python क्लाइंट लाइब्रेरी का इस्तेमाल करके ट्यूनिंग की सेवा इस्तेमाल करने का तरीका बताया गया है. यहां आपको PaLM API की टेक्स्ट जनरेट करने की सेवा के टेक्स्ट मॉडल को ट्यून करने का तरीका पता चलेगा.
सेटअप
प्रमाणीकृत करें
PaLM API की मदद से, मॉडल को अपने डेटा पर ट्यून किया जा सकता है. क्योंकि इसमें आपका डेटा और आपके ट्यून किए गए मॉडल, एपीआई-कुंजी की तुलना में, ज़्यादा सख्त ऐक्सेस कंट्रोल की ज़रूरत होती है.
इस ट्यूटोरियल को चलाने से पहले, आपको अपने प्रोजेक्ट के लिए OAuth सेटअप करें.
अगर आपको इस notebook को Colab में चलाना है, तो सबसे पहले अपना
client_secret*.json फ़ाइल में "फ़ाइल > अपलोड करें" का विकल्प शामिल है.
cp client_secret*.json client_secret.jsonls client_secret.json
client_secret.json
यह gcloud कमांड client_secret.json फ़ाइल को क्रेडेंशियल में बदल देता है. इसका इस्तेमाल सेवा में पुष्टि के लिए किया जा सकता है.
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'
क्लाइंट लाइब्रेरी इंस्टॉल करना
pip install -q google-generativeai
लाइब्रेरी इंपोर्ट करें
import google.generativeai as genai
genai.list_tuned_model तरीके से, अपने चैनल के मौजूदा मॉडल देखे जा सकते हैं.
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
ट्यून किया गया मॉडल बनाएं
ट्यून किया जाने वाला मॉडल बनाने के लिए, आपको genai.create_tuned_model तरीके में अपना डेटासेट पास करना होगा. इसके लिए, कॉल में इनपुट और आउटपुट वैल्यू सीधे तौर पर तय करें या फ़ाइल को किसी डेटाफ़्रेम में इंपोर्ट करके देखें.
इस उदाहरण के लिए, आपको क्रम में अगला नंबर जनरेट करने के लिए मॉडल को ट्यून करना होगा. उदाहरण के लिए, अगर इनपुट 1 है, तो मॉडल में 2 आउटपुट मिलेगा. अगर इनपुट one hundred है, तो आउटपुट one hundred one होना चाहिए.
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,
)
आपके ट्यून किए गए मॉडल को, ट्यून किए गए मॉडल की सूची में तुरंत जोड़ दिया जाता है. हालांकि, इसका स्टेटस "बनाया जा रहा है" पर सेट हो जाता है मॉडल को ट्यून किया जाता है.
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>
ट्यूनिंग की प्रोग्रेस देखना
राज्य का नाम जानने के लिए, metadata का इस्तेमाल करें:
operation.metadata
tuned_model: "tunedModels/generate-num-9028" total_steps: 375
operation.result() या operation.wait_bar() का इस्तेमाल करके ट्रेनिंग खत्म होने का इंतज़ार करें
import time
for status in operation.wait_bar():
time.sleep(30)
0%| | 0/375 [00:00<?, ?it/s]
cancel() तरीके का इस्तेमाल करके, ट्यूनिंग का जॉब कभी भी रद्द किया जा सकता है. नीचे दी गई लाइन पर की गई टिप्पणी को हटाएं और अपना काम पूरा होने से पहले उसे रद्द करने के लिए, कोड सेल चलाएं.
# operation.cancel()
ट्यूनिंग पूरी हो जाने के बाद, ट्यूनिंग के नतीजों से लॉस कर्व देखा जा सकता है. लॉस कर्व दिखाता है कि मॉडल के अनुमान, सही आउटपुट से कितना अलग हैं.
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'>
अपने मॉडल का मूल्यांकन करना
अपने मॉडल की परफ़ॉर्मेंस की जांच करने के लिए, genai.generate_text तरीके का इस्तेमाल करके अपने मॉडल का नाम तय किया जा सकता है.
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'
जैसा कि आपको दिख रहा है, पिछले प्रॉम्प्ट से सही नतीजा नहीं मिला, five. बेहतर नतीजे पाने के लिए, कुछ अलग-अलग चीज़ें आज़माई जा सकती हैं. जैसे, तापमान को शून्य के करीब अडजस्ट करना, एक जैसे नतीजे पाने के लिए अपने डेटासेट में क्वालिटी के ज़्यादा उदाहरण जोड़ना, जिससे मॉडल सीख सकता हो या उदाहरणों में प्रॉम्प्ट या प्रीएंबल जोड़ना.
परफ़ॉर्मेंस को बेहतर बनाने के बारे में ज़्यादा जानकारी के लिए, ट्यूनिंग गाइड देखें.
जानकारी अपडेट करें
genai.update_tuned_model तरीके का इस्तेमाल करके, किसी भी समय अपने ट्यून किए गए मॉडल की जानकारी को अपडेट किया जा सकता है.
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,
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{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 26, 713095, tzinfo=<UTC>),
'epoch': 83,
'mean_loss': 0.0009658756,
'step': 312},
{'compute_time': datetime.datetime(2023, 9, 19, 19, 10, 27, 973220, tzinfo=<UTC>),
'epoch': 83,
'mean_loss': 0.00014872942,
'step': 313},
{'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.'
मॉडल मिटाएं
आपको जिन मॉडल की ज़रूरत नहीं है उन्हें मिटाकर, ट्यून किए गए मॉडल की सूची को खाली किया जा सकता है. किसी मॉडल को मिटाने के लिए, genai.delete_tuned_model तरीके का इस्तेमाल करें. अगर आपने ट्यूनिंग से जुड़ी कोई भी जॉब रद्द कर दी है, तो हो सकता है कि आप उसे मिटाना चाहें, क्योंकि उसकी परफ़ॉर्मेंस उम्मीद के मुताबिक नहीं है.
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.