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evo-ai/.venv/lib/python3.10/site-packages/vertexai/tuning/_distillation.py
2025-04-25 15:30:54 -03:00

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Python

# Copyright 2024 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.
#
# pylint: disable=protected-access
"""Classes for model tuning based on distillation."""
from typing import Optional
from google.cloud.aiplatform.utils import gcs_utils
from google.cloud.aiplatform_v1beta1.types import tuning_job as gca_tuning_job_types
from vertexai import generative_models
from vertexai.tuning import _tuning
def distill_model(
*,
student_model: str,
teacher_model: str,
training_dataset: str,
validation_dataset: Optional[str] = None,
epoch_count: Optional[int] = None,
learning_rate_multiplier: Optional[float] = None,
tuned_model_display_name: Optional[str] = None,
) -> "DistillationJob":
"""Tunes a model using distillation.
Args:
student_model:
Student model name for distillation, e.g., "gemma-1.1-2b-it".
teacher_model:
Teacher model name for distillation, e.g., "gemini-1.5-flash-001".
training_dataset: Cloud Storage path to file containing training dataset for distillation.
The dataset should be in JSONL format.
validation_dataset: Cloud Storage path to file containing validation dataset for distillation.
The dataset should be in JSONL format.
epoch_count: Number of training epoches for this tuning job.
learning_rate_multiplier: Learning rate multiplier for tuning.
tuned_model_display_name: The display name of the
[TunedModel][google.cloud.aiplatform.v1.Model]. The name can
be up to 128 characters long and can consist of any UTF-8 characters.
Returns:
A `TuningJob` object.
"""
if isinstance(student_model, generative_models.GenerativeModel):
student_model = student_model._prediction_resource_name
student_model = student_model.rpartition("/")[-1]
teacher_model = teacher_model.rpartition("/")[-1]
pipeline_root = (
gcs_utils.create_gcs_bucket_for_pipeline_artifacts_if_it_does_not_exist()
)
distillation_spec = gca_tuning_job_types.DistillationSpec(
student_model=student_model,
base_teacher_model=teacher_model,
training_dataset_uri=training_dataset,
validation_dataset_uri=validation_dataset,
hyper_parameters=gca_tuning_job_types.DistillationHyperParameters(
epoch_count=epoch_count,
learning_rate_multiplier=learning_rate_multiplier,
),
pipeline_root_directory=pipeline_root,
)
return DistillationJob._create( # pylint: disable=protected-access
base_model=None,
tuning_spec=distillation_spec,
tuned_model_display_name=tuned_model_display_name,
)
class DistillationJob(_tuning.TuningJob):
pass