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261# -*- coding: utf-8 -*-
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from absl.testing import absltest
import google
import pathlib
samples = pathlib.Path(__file__).parent
class UnitTests(absltest.TestCase):
@classmethod
def setUpClass(cls):
# Code to run once before all tests in the class
# [START tuned_models_create]
import google.generativeai as genai
import time
base_model = "models/gemini-1.5-flash-001-tuning"
training_data = [
{"text_input": "1", "output": "2"},
# ... more examples ...
# [START_EXCLUDE]
{"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"},
# [END_EXCLUDE]
{"text_input": "seven", "output": "eight"},
]
operation = genai.create_tuned_model(
# You can use a tuned model here too. Set `source_model="tunedModels/..."`
display_name="increment",
source_model=base_model,
epoch_count=5,
batch_size=4,
learning_rate=0.001,
training_data=training_data,
)
for status in operation.wait_bar():
time.sleep(10)
tuned_model = operation.result()
print(tuned_model)
# # You can plot the loss curve with:
# snapshots = pd.DataFrame(result.tuning_task.snapshots)
# sns.lineplot(data=snapshots, x='epoch', y='mean_loss')
model = genai.GenerativeModel(model_name=tuned_model.name)
result = model.generate_content("III")
print(result.text) # IV
# [END tuned_models_create]
cls.tuned_model_name = tuned_model_name = tuned_model.name
def test_tuned_models_generate_content(self):
# [START tuned_models_generate_content]
import google.generativeai as genai
model = genai.GenerativeModel(model_name=self.tuned_model_name)
result = model.generate_content("III")
print(result.text) # "IV"
# [END tuned_models_generate_content]
def test_tuned_models_get(self):
# [START tuned_models_get]
import google.generativeai as genai
model_info = genai.get_model(self.tuned_model_name)
print(model_info)
# [END tuned_models_get]
def test_tuned_models_list(self):
# [START tuned_models_list]
import google.generativeai as genai
for model_info in genai.list_tuned_models():
print(model_info.name)
# [END tuned_models_list]
def test_tuned_models_delete(self):
import time
import google.generativeai as genai
base_model = "models/gemini-1.5-flash-001-tuning"
training_data = samples / "increment_tuning_data.json"
try:
operation = genai.create_tuned_model(
id="delete-this-model",
# You can use a tuned model here too. Set `source_model="tunedModels/..."`
display_name="increment",
source_model=base_model,
epoch_count=5,
batch_size=4,
learning_rate=0.001,
training_data=training_data,
)
except google.api_core.exceptions.AlreadyExists:
pass
else:
for status in operation.wait_bar():
time.sleep(10)
# [START tuned_models_delete]
import google.generativeai as genai
model_name = "tunedModels/delete-this-model"
model_info = genai.get_model(model_name)
print(model_info)
# You can pass the model_info or name here.
genai.delete_tuned_model(model_name)
# [END tuned_models_delete]
def test_tuned_models_permissions_create(self):
# [START tuned_models_permissions_create]
import google.generativeai as genai
model_info = genai.get_model(self.tuned_model_name)
# [START_EXCLUDE]
for p in model_info.permissions.list():
if p.role.name != "OWNER":
p.delete()
# [END_EXCLUDE]
public_permission = model_info.permissions.create(
role="READER",
grantee_type="EVERYONE",
)
group_permission = model_info.permissions.create(
role="READER",
# Use "user" for an individual email address.
grantee_type="group",
email_address="genai-samples-test-group@googlegroups.com",
)
# [END tuned_models_permissions_create]
public_permission.delete()
group_permission.delete()
def test_tuned_models_permissions_list(self):
# [START tuned_models_permissions_list]
import google.generativeai as genai
model_info = genai.get_model(self.tuned_model_name)
# [START_EXCLUDE]
for p in model_info.permissions.list():
if p.role.name != "OWNER":
p.delete()
public_permission = model_info.permissions.create(
role="READER",
grantee_type="EVERYONE",
)
group_permission = model_info.permissions.create(
role="READER",
grantee_type="group",
email_address="genai-samples-test-group@googlegroups.com",
)
# [END_EXCLUDE]
for p in model_info.permissions.list():
print(p)
# [END tuned_models_permissions_list]
public_permission.delete()
group_permission.delete()
def test_tuned_models_permissions_get(self):
# [START tuned_models_permissions_get]
import google.generativeai as genai
model_info = genai.get_model(self.tuned_model_name)
# [START_EXCLUDE]
for p in model_info.permissions.list():
if p.role.name != "OWNER":
p.delete()
# [END_EXCLUDE]
public = model_info.permissions.create(
role="READER",
grantee_type="EVERYONE",
)
print(public)
name = public.name
print(name) # tunedModels/{tunedModel}/permissions/{permission}
from_name = genai.types.Permissions.get(name)
print(from_name)
# [END tuned_models_permissions_get]
def test_tuned_models_permissions_update(self):
# [START tuned_models_permissions_update]
import google.generativeai as genai
model_info = genai.get_model(self.tuned_model_name)
# [START_EXCLUDE]
for p in model_info.permissions.list():
if p.role.name != "OWNER":
p.delete()
# [END_EXCLUDE]
test_group = model_info.permissions.create(
role="writer",
grantee_type="group",
email_address="genai-samples-test-group@googlegroups.com",
)
test_group.update({"role": "READER"})
# [END tuned_models_permissions_get]
def test_tuned_models_permission_delete(self):
# [START tuned_models_permissions_delete]
import google.generativeai as genai
model_info = genai.get_model(self.tuned_model_name)
# [START_EXCLUDE]
for p in model_info.permissions.list():
if p.role.name != "OWNER":
p.delete()
# [END_EXCLUDE]
public_permission = model_info.permissions.create(
role="READER",
grantee_type="EVERYONE",
)
public_permission.delete()
# [END tuned_models_permissions_delete]
if __name__ == "__main__":
absltest.main()