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// 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.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.33.0
// protoc v4.25.3
// source: google/ai/generativelanguage/v1beta/tuned_model.proto
package generativelanguagepb
import (
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
timestamppb "google.golang.org/protobuf/types/known/timestamppb"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// The state of the tuned model.
type TunedModel_State int32
const (
// The default value. This value is unused.
TunedModel_STATE_UNSPECIFIED TunedModel_State = 0
// The model is being created.
TunedModel_CREATING TunedModel_State = 1
// The model is ready to be used.
TunedModel_ACTIVE TunedModel_State = 2
// The model failed to be created.
TunedModel_FAILED TunedModel_State = 3
)
// Enum value maps for TunedModel_State.
var (
TunedModel_State_name = map[int32]string{
0: "STATE_UNSPECIFIED",
1: "CREATING",
2: "ACTIVE",
3: "FAILED",
}
TunedModel_State_value = map[string]int32{
"STATE_UNSPECIFIED": 0,
"CREATING": 1,
"ACTIVE": 2,
"FAILED": 3,
}
)
func (x TunedModel_State) Enum() *TunedModel_State {
p := new(TunedModel_State)
*p = x
return p
}
func (x TunedModel_State) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (TunedModel_State) Descriptor() protoreflect.EnumDescriptor {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_enumTypes[0].Descriptor()
}
func (TunedModel_State) Type() protoreflect.EnumType {
return &file_google_ai_generativelanguage_v1beta_tuned_model_proto_enumTypes[0]
}
func (x TunedModel_State) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use TunedModel_State.Descriptor instead.
func (TunedModel_State) EnumDescriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{0, 0}
}
// A fine-tuned model created using ModelService.CreateTunedModel.
type TunedModel struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The model used as the starting point for tuning.
//
// Types that are assignable to SourceModel:
//
// *TunedModel_TunedModelSource
// *TunedModel_BaseModel
SourceModel isTunedModel_SourceModel `protobuf_oneof:"source_model"`
// Output only. The tuned model name. A unique name will be generated on
// create. Example: `tunedModels/az2mb0bpw6i` If display_name is set on
// create, the id portion of the name will be set by concatenating the words
// of the display_name with hyphens and adding a random portion for
// uniqueness. Example:
//
// display_name = "Sentence Translator"
// name = "tunedModels/sentence-translator-u3b7m"
Name string `protobuf:"bytes,1,opt,name=name,proto3" json:"name,omitempty"`
// Optional. The name to display for this model in user interfaces.
// The display name must be up to 40 characters including spaces.
DisplayName string `protobuf:"bytes,5,opt,name=display_name,json=displayName,proto3" json:"display_name,omitempty"`
// Optional. A short description of this model.
Description string `protobuf:"bytes,6,opt,name=description,proto3" json:"description,omitempty"`
// Optional. Controls the randomness of the output.
//
// Values can range over `[0.0,1.0]`, inclusive. A value closer to `1.0` will
// produce responses that are more varied, while a value closer to `0.0` will
// typically result in less surprising responses from the model.
//
// This value specifies default to be the one used by the base model while
// creating the model.
Temperature *float32 `protobuf:"fixed32,11,opt,name=temperature,proto3,oneof" json:"temperature,omitempty"`
// Optional. For Nucleus sampling.
//
// Nucleus sampling considers the smallest set of tokens whose probability
// sum is at least `top_p`.
//
// This value specifies default to be the one used by the base model while
// creating the model.
TopP *float32 `protobuf:"fixed32,12,opt,name=top_p,json=topP,proto3,oneof" json:"top_p,omitempty"`
// Optional. For Top-k sampling.
//
// Top-k sampling considers the set of `top_k` most probable tokens.
// This value specifies default to be used by the backend while making the
// call to the model.
//
// This value specifies default to be the one used by the base model while
// creating the model.
TopK *int32 `protobuf:"varint,13,opt,name=top_k,json=topK,proto3,oneof" json:"top_k,omitempty"`
// Output only. The state of the tuned model.
State TunedModel_State `protobuf:"varint,7,opt,name=state,proto3,enum=google.ai.generativelanguage.v1beta.TunedModel_State" json:"state,omitempty"`
// Output only. The timestamp when this model was created.
CreateTime *timestamppb.Timestamp `protobuf:"bytes,8,opt,name=create_time,json=createTime,proto3" json:"create_time,omitempty"`
// Output only. The timestamp when this model was updated.
UpdateTime *timestamppb.Timestamp `protobuf:"bytes,9,opt,name=update_time,json=updateTime,proto3" json:"update_time,omitempty"`
// Required. The tuning task that creates the tuned model.
TuningTask *TuningTask `protobuf:"bytes,10,opt,name=tuning_task,json=tuningTask,proto3" json:"tuning_task,omitempty"`
}
func (x *TunedModel) Reset() {
*x = TunedModel{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[0]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TunedModel) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TunedModel) ProtoMessage() {}
func (x *TunedModel) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[0]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TunedModel.ProtoReflect.Descriptor instead.
func (*TunedModel) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{0}
}
func (m *TunedModel) GetSourceModel() isTunedModel_SourceModel {
if m != nil {
return m.SourceModel
}
return nil
}
func (x *TunedModel) GetTunedModelSource() *TunedModelSource {
if x, ok := x.GetSourceModel().(*TunedModel_TunedModelSource); ok {
return x.TunedModelSource
}
return nil
}
func (x *TunedModel) GetBaseModel() string {
if x, ok := x.GetSourceModel().(*TunedModel_BaseModel); ok {
return x.BaseModel
}
return ""
}
func (x *TunedModel) GetName() string {
if x != nil {
return x.Name
}
return ""
}
func (x *TunedModel) GetDisplayName() string {
if x != nil {
return x.DisplayName
}
return ""
}
func (x *TunedModel) GetDescription() string {
if x != nil {
return x.Description
}
return ""
}
func (x *TunedModel) GetTemperature() float32 {
if x != nil && x.Temperature != nil {
return *x.Temperature
}
return 0
}
func (x *TunedModel) GetTopP() float32 {
if x != nil && x.TopP != nil {
return *x.TopP
}
return 0
}
func (x *TunedModel) GetTopK() int32 {
if x != nil && x.TopK != nil {
return *x.TopK
}
return 0
}
func (x *TunedModel) GetState() TunedModel_State {
if x != nil {
return x.State
}
return TunedModel_STATE_UNSPECIFIED
}
func (x *TunedModel) GetCreateTime() *timestamppb.Timestamp {
if x != nil {
return x.CreateTime
}
return nil
}
func (x *TunedModel) GetUpdateTime() *timestamppb.Timestamp {
if x != nil {
return x.UpdateTime
}
return nil
}
func (x *TunedModel) GetTuningTask() *TuningTask {
if x != nil {
return x.TuningTask
}
return nil
}
type isTunedModel_SourceModel interface {
isTunedModel_SourceModel()
}
type TunedModel_TunedModelSource struct {
// Optional. TunedModel to use as the starting point for training the new
// model.
TunedModelSource *TunedModelSource `protobuf:"bytes,3,opt,name=tuned_model_source,json=tunedModelSource,proto3,oneof"`
}
type TunedModel_BaseModel struct {
// Immutable. The name of the `Model` to tune.
// Example: `models/text-bison-001`
BaseModel string `protobuf:"bytes,4,opt,name=base_model,json=baseModel,proto3,oneof"`
}
func (*TunedModel_TunedModelSource) isTunedModel_SourceModel() {}
func (*TunedModel_BaseModel) isTunedModel_SourceModel() {}
// Tuned model as a source for training a new model.
type TunedModelSource struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Immutable. The name of the `TunedModel` to use as the starting point for
// training the new model.
// Example: `tunedModels/my-tuned-model`
TunedModel string `protobuf:"bytes,1,opt,name=tuned_model,json=tunedModel,proto3" json:"tuned_model,omitempty"`
// Output only. The name of the base `Model` this `TunedModel` was tuned from.
// Example: `models/text-bison-001`
BaseModel string `protobuf:"bytes,2,opt,name=base_model,json=baseModel,proto3" json:"base_model,omitempty"`
}
func (x *TunedModelSource) Reset() {
*x = TunedModelSource{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[1]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TunedModelSource) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TunedModelSource) ProtoMessage() {}
func (x *TunedModelSource) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[1]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TunedModelSource.ProtoReflect.Descriptor instead.
func (*TunedModelSource) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{1}
}
func (x *TunedModelSource) GetTunedModel() string {
if x != nil {
return x.TunedModel
}
return ""
}
func (x *TunedModelSource) GetBaseModel() string {
if x != nil {
return x.BaseModel
}
return ""
}
// Tuning tasks that create tuned models.
type TuningTask struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. The timestamp when tuning this model started.
StartTime *timestamppb.Timestamp `protobuf:"bytes,1,opt,name=start_time,json=startTime,proto3" json:"start_time,omitempty"`
// Output only. The timestamp when tuning this model completed.
CompleteTime *timestamppb.Timestamp `protobuf:"bytes,2,opt,name=complete_time,json=completeTime,proto3" json:"complete_time,omitempty"`
// Output only. Metrics collected during tuning.
Snapshots []*TuningSnapshot `protobuf:"bytes,3,rep,name=snapshots,proto3" json:"snapshots,omitempty"`
// Required. Input only. Immutable. The model training data.
TrainingData *Dataset `protobuf:"bytes,4,opt,name=training_data,json=trainingData,proto3" json:"training_data,omitempty"`
// Immutable. Hyperparameters controlling the tuning process. If not provided,
// default values will be used.
Hyperparameters *Hyperparameters `protobuf:"bytes,5,opt,name=hyperparameters,proto3" json:"hyperparameters,omitempty"`
}
func (x *TuningTask) Reset() {
*x = TuningTask{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[2]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TuningTask) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TuningTask) ProtoMessage() {}
func (x *TuningTask) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[2]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TuningTask.ProtoReflect.Descriptor instead.
func (*TuningTask) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{2}
}
func (x *TuningTask) GetStartTime() *timestamppb.Timestamp {
if x != nil {
return x.StartTime
}
return nil
}
func (x *TuningTask) GetCompleteTime() *timestamppb.Timestamp {
if x != nil {
return x.CompleteTime
}
return nil
}
func (x *TuningTask) GetSnapshots() []*TuningSnapshot {
if x != nil {
return x.Snapshots
}
return nil
}
func (x *TuningTask) GetTrainingData() *Dataset {
if x != nil {
return x.TrainingData
}
return nil
}
func (x *TuningTask) GetHyperparameters() *Hyperparameters {
if x != nil {
return x.Hyperparameters
}
return nil
}
// Hyperparameters controlling the tuning process. Read more at
// https://ai.google.dev/docs/model_tuning_guidance
type Hyperparameters struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Options for specifying learning rate during tuning.
//
// Types that are assignable to LearningRateOption:
//
// *Hyperparameters_LearningRate
// *Hyperparameters_LearningRateMultiplier
LearningRateOption isHyperparameters_LearningRateOption `protobuf_oneof:"learning_rate_option"`
// Immutable. The number of training epochs. An epoch is one pass through the
// training data. If not set, a default of 5 will be used.
EpochCount *int32 `protobuf:"varint,14,opt,name=epoch_count,json=epochCount,proto3,oneof" json:"epoch_count,omitempty"`
// Immutable. The batch size hyperparameter for tuning.
// If not set, a default of 4 or 16 will be used based on the number of
// training examples.
BatchSize *int32 `protobuf:"varint,15,opt,name=batch_size,json=batchSize,proto3,oneof" json:"batch_size,omitempty"`
}
func (x *Hyperparameters) Reset() {
*x = Hyperparameters{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[3]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *Hyperparameters) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*Hyperparameters) ProtoMessage() {}
func (x *Hyperparameters) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[3]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use Hyperparameters.ProtoReflect.Descriptor instead.
func (*Hyperparameters) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{3}
}
func (m *Hyperparameters) GetLearningRateOption() isHyperparameters_LearningRateOption {
if m != nil {
return m.LearningRateOption
}
return nil
}
func (x *Hyperparameters) GetLearningRate() float32 {
if x, ok := x.GetLearningRateOption().(*Hyperparameters_LearningRate); ok {
return x.LearningRate
}
return 0
}
func (x *Hyperparameters) GetLearningRateMultiplier() float32 {
if x, ok := x.GetLearningRateOption().(*Hyperparameters_LearningRateMultiplier); ok {
return x.LearningRateMultiplier
}
return 0
}
func (x *Hyperparameters) GetEpochCount() int32 {
if x != nil && x.EpochCount != nil {
return *x.EpochCount
}
return 0
}
func (x *Hyperparameters) GetBatchSize() int32 {
if x != nil && x.BatchSize != nil {
return *x.BatchSize
}
return 0
}
type isHyperparameters_LearningRateOption interface {
isHyperparameters_LearningRateOption()
}
type Hyperparameters_LearningRate struct {
// Optional. Immutable. The learning rate hyperparameter for tuning.
// If not set, a default of 0.001 or 0.0002 will be calculated based on the
// number of training examples.
LearningRate float32 `protobuf:"fixed32,16,opt,name=learning_rate,json=learningRate,proto3,oneof"`
}
type Hyperparameters_LearningRateMultiplier struct {
// Optional. Immutable. The learning rate multiplier is used to calculate a
// final learning_rate based on the default (recommended) value. Actual
// learning rate := learning_rate_multiplier * default learning rate Default
// learning rate is dependent on base model and dataset size. If not set, a
// default of 1.0 will be used.
LearningRateMultiplier float32 `protobuf:"fixed32,17,opt,name=learning_rate_multiplier,json=learningRateMultiplier,proto3,oneof"`
}
func (*Hyperparameters_LearningRate) isHyperparameters_LearningRateOption() {}
func (*Hyperparameters_LearningRateMultiplier) isHyperparameters_LearningRateOption() {}
// Dataset for training or validation.
type Dataset struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Inline data or a reference to the data.
//
// Types that are assignable to Dataset:
//
// *Dataset_Examples
Dataset isDataset_Dataset `protobuf_oneof:"dataset"`
}
func (x *Dataset) Reset() {
*x = Dataset{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[4]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *Dataset) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*Dataset) ProtoMessage() {}
func (x *Dataset) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[4]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use Dataset.ProtoReflect.Descriptor instead.
func (*Dataset) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{4}
}
func (m *Dataset) GetDataset() isDataset_Dataset {
if m != nil {
return m.Dataset
}
return nil
}
func (x *Dataset) GetExamples() *TuningExamples {
if x, ok := x.GetDataset().(*Dataset_Examples); ok {
return x.Examples
}
return nil
}
type isDataset_Dataset interface {
isDataset_Dataset()
}
type Dataset_Examples struct {
// Optional. Inline examples.
Examples *TuningExamples `protobuf:"bytes,1,opt,name=examples,proto3,oneof"`
}
func (*Dataset_Examples) isDataset_Dataset() {}
// A set of tuning examples. Can be training or validation data.
type TuningExamples struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Required. The examples. Example input can be for text or discuss, but all
// examples in a set must be of the same type.
Examples []*TuningExample `protobuf:"bytes,1,rep,name=examples,proto3" json:"examples,omitempty"`
}
func (x *TuningExamples) Reset() {
*x = TuningExamples{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[5]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TuningExamples) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TuningExamples) ProtoMessage() {}
func (x *TuningExamples) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[5]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TuningExamples.ProtoReflect.Descriptor instead.
func (*TuningExamples) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{5}
}
func (x *TuningExamples) GetExamples() []*TuningExample {
if x != nil {
return x.Examples
}
return nil
}
// A single example for tuning.
type TuningExample struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// The input to the model for this example.
//
// Types that are assignable to ModelInput:
//
// *TuningExample_TextInput
ModelInput isTuningExample_ModelInput `protobuf_oneof:"model_input"`
// Required. The expected model output.
Output string `protobuf:"bytes,3,opt,name=output,proto3" json:"output,omitempty"`
}
func (x *TuningExample) Reset() {
*x = TuningExample{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[6]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TuningExample) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TuningExample) ProtoMessage() {}
func (x *TuningExample) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[6]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TuningExample.ProtoReflect.Descriptor instead.
func (*TuningExample) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{6}
}
func (m *TuningExample) GetModelInput() isTuningExample_ModelInput {
if m != nil {
return m.ModelInput
}
return nil
}
func (x *TuningExample) GetTextInput() string {
if x, ok := x.GetModelInput().(*TuningExample_TextInput); ok {
return x.TextInput
}
return ""
}
func (x *TuningExample) GetOutput() string {
if x != nil {
return x.Output
}
return ""
}
type isTuningExample_ModelInput interface {
isTuningExample_ModelInput()
}
type TuningExample_TextInput struct {
// Optional. Text model input.
TextInput string `protobuf:"bytes,1,opt,name=text_input,json=textInput,proto3,oneof"`
}
func (*TuningExample_TextInput) isTuningExample_ModelInput() {}
// Record for a single tuning step.
type TuningSnapshot struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. The tuning step.
Step int32 `protobuf:"varint,1,opt,name=step,proto3" json:"step,omitempty"`
// Output only. The epoch this step was part of.
Epoch int32 `protobuf:"varint,2,opt,name=epoch,proto3" json:"epoch,omitempty"`
// Output only. The mean loss of the training examples for this step.
MeanLoss float32 `protobuf:"fixed32,3,opt,name=mean_loss,json=meanLoss,proto3" json:"mean_loss,omitempty"`
// Output only. The timestamp when this metric was computed.
ComputeTime *timestamppb.Timestamp `protobuf:"bytes,4,opt,name=compute_time,json=computeTime,proto3" json:"compute_time,omitempty"`
}
func (x *TuningSnapshot) Reset() {
*x = TuningSnapshot{}
if protoimpl.UnsafeEnabled {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[7]
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
ms.StoreMessageInfo(mi)
}
}
func (x *TuningSnapshot) String() string {
return protoimpl.X.MessageStringOf(x)
}
func (*TuningSnapshot) ProtoMessage() {}
func (x *TuningSnapshot) ProtoReflect() protoreflect.Message {
mi := &file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[7]
if protoimpl.UnsafeEnabled && x != nil {
ms := protoimpl.X.MessageStateOf(protoimpl.Pointer(x))
if ms.LoadMessageInfo() == nil {
ms.StoreMessageInfo(mi)
}
return ms
}
return mi.MessageOf(x)
}
// Deprecated: Use TuningSnapshot.ProtoReflect.Descriptor instead.
func (*TuningSnapshot) Descriptor() ([]byte, []int) {
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP(), []int{7}
}
func (x *TuningSnapshot) GetStep() int32 {
if x != nil {
return x.Step
}
return 0
}
func (x *TuningSnapshot) GetEpoch() int32 {
if x != nil {
return x.Epoch
}
return 0
}
func (x *TuningSnapshot) GetMeanLoss() float32 {
if x != nil {
return x.MeanLoss
}
return 0
}
func (x *TuningSnapshot) GetComputeTime() *timestamppb.Timestamp {
if x != nil {
return x.ComputeTime
}
return nil
}
var File_google_ai_generativelanguage_v1beta_tuned_model_proto protoreflect.FileDescriptor
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var (
file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescOnce sync.Once
file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescData = file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDesc
)
func file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescGZIP() []byte {
file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescOnce.Do(func() {
file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescData = protoimpl.X.CompressGZIP(file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescData)
})
return file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDescData
}
var file_google_ai_generativelanguage_v1beta_tuned_model_proto_enumTypes = make([]protoimpl.EnumInfo, 1)
var file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes = make([]protoimpl.MessageInfo, 8)
var file_google_ai_generativelanguage_v1beta_tuned_model_proto_goTypes = []interface{}{
(TunedModel_State)(0), // 0: google.ai.generativelanguage.v1beta.TunedModel.State
(*TunedModel)(nil), // 1: google.ai.generativelanguage.v1beta.TunedModel
(*TunedModelSource)(nil), // 2: google.ai.generativelanguage.v1beta.TunedModelSource
(*TuningTask)(nil), // 3: google.ai.generativelanguage.v1beta.TuningTask
(*Hyperparameters)(nil), // 4: google.ai.generativelanguage.v1beta.Hyperparameters
(*Dataset)(nil), // 5: google.ai.generativelanguage.v1beta.Dataset
(*TuningExamples)(nil), // 6: google.ai.generativelanguage.v1beta.TuningExamples
(*TuningExample)(nil), // 7: google.ai.generativelanguage.v1beta.TuningExample
(*TuningSnapshot)(nil), // 8: google.ai.generativelanguage.v1beta.TuningSnapshot
(*timestamppb.Timestamp)(nil), // 9: google.protobuf.Timestamp
}
var file_google_ai_generativelanguage_v1beta_tuned_model_proto_depIdxs = []int32{
2, // 0: google.ai.generativelanguage.v1beta.TunedModel.tuned_model_source:type_name -> google.ai.generativelanguage.v1beta.TunedModelSource
0, // 1: google.ai.generativelanguage.v1beta.TunedModel.state:type_name -> google.ai.generativelanguage.v1beta.TunedModel.State
9, // 2: google.ai.generativelanguage.v1beta.TunedModel.create_time:type_name -> google.protobuf.Timestamp
9, // 3: google.ai.generativelanguage.v1beta.TunedModel.update_time:type_name -> google.protobuf.Timestamp
3, // 4: google.ai.generativelanguage.v1beta.TunedModel.tuning_task:type_name -> google.ai.generativelanguage.v1beta.TuningTask
9, // 5: google.ai.generativelanguage.v1beta.TuningTask.start_time:type_name -> google.protobuf.Timestamp
9, // 6: google.ai.generativelanguage.v1beta.TuningTask.complete_time:type_name -> google.protobuf.Timestamp
8, // 7: google.ai.generativelanguage.v1beta.TuningTask.snapshots:type_name -> google.ai.generativelanguage.v1beta.TuningSnapshot
5, // 8: google.ai.generativelanguage.v1beta.TuningTask.training_data:type_name -> google.ai.generativelanguage.v1beta.Dataset
4, // 9: google.ai.generativelanguage.v1beta.TuningTask.hyperparameters:type_name -> google.ai.generativelanguage.v1beta.Hyperparameters
6, // 10: google.ai.generativelanguage.v1beta.Dataset.examples:type_name -> google.ai.generativelanguage.v1beta.TuningExamples
7, // 11: google.ai.generativelanguage.v1beta.TuningExamples.examples:type_name -> google.ai.generativelanguage.v1beta.TuningExample
9, // 12: google.ai.generativelanguage.v1beta.TuningSnapshot.compute_time:type_name -> google.protobuf.Timestamp
13, // [13:13] is the sub-list for method output_type
13, // [13:13] is the sub-list for method input_type
13, // [13:13] is the sub-list for extension type_name
13, // [13:13] is the sub-list for extension extendee
0, // [0:13] is the sub-list for field type_name
}
func init() { file_google_ai_generativelanguage_v1beta_tuned_model_proto_init() }
func file_google_ai_generativelanguage_v1beta_tuned_model_proto_init() {
if File_google_ai_generativelanguage_v1beta_tuned_model_proto != nil {
return
}
if !protoimpl.UnsafeEnabled {
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[0].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TunedModel); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[1].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TunedModelSource); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[2].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TuningTask); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[3].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*Hyperparameters); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[4].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*Dataset); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[5].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TuningExamples); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[6].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TuningExample); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[7].Exporter = func(v interface{}, i int) interface{} {
switch v := v.(*TuningSnapshot); i {
case 0:
return &v.state
case 1:
return &v.sizeCache
case 2:
return &v.unknownFields
default:
return nil
}
}
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[0].OneofWrappers = []interface{}{
(*TunedModel_TunedModelSource)(nil),
(*TunedModel_BaseModel)(nil),
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[3].OneofWrappers = []interface{}{
(*Hyperparameters_LearningRate)(nil),
(*Hyperparameters_LearningRateMultiplier)(nil),
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[4].OneofWrappers = []interface{}{
(*Dataset_Examples)(nil),
}
file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes[6].OneofWrappers = []interface{}{
(*TuningExample_TextInput)(nil),
}
type x struct{}
out := protoimpl.TypeBuilder{
File: protoimpl.DescBuilder{
GoPackagePath: reflect.TypeOf(x{}).PkgPath(),
RawDescriptor: file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDesc,
NumEnums: 1,
NumMessages: 8,
NumExtensions: 0,
NumServices: 0,
},
GoTypes: file_google_ai_generativelanguage_v1beta_tuned_model_proto_goTypes,
DependencyIndexes: file_google_ai_generativelanguage_v1beta_tuned_model_proto_depIdxs,
EnumInfos: file_google_ai_generativelanguage_v1beta_tuned_model_proto_enumTypes,
MessageInfos: file_google_ai_generativelanguage_v1beta_tuned_model_proto_msgTypes,
}.Build()
File_google_ai_generativelanguage_v1beta_tuned_model_proto = out.File
file_google_ai_generativelanguage_v1beta_tuned_model_proto_rawDesc = nil
file_google_ai_generativelanguage_v1beta_tuned_model_proto_goTypes = nil
file_google_ai_generativelanguage_v1beta_tuned_model_proto_depIdxs = nil
}