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This project contains the SDK used for Oracle Cloud Infrastructure Generative Ai
/**
* Copyright (c) 2016, 2024, Oracle and/or its affiliates. All rights reserved.
* This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
*/
package com.oracle.bmc.generativeai.model;
/**
* The Lora training method hyperparameters.
* Note: Objects should always be created or deserialized using the {@link Builder}. This model
* distinguishes fields that are {@code null} because they are unset from fields that are explicitly
* set to {@code null}. This is done in the setter methods of the {@link Builder}, which maintain a
* set of all explicitly set fields called {@link Builder#__explicitlySet__}. The {@link
* #hashCode()} and {@link #equals(Object)} methods are implemented to take the explicitly set
* fields into account. The constructor, on the other hand, does not take the explicitly set fields
* into account (since the constructor cannot distinguish explicit {@code null} from unset {@code
* null}).
*/
@jakarta.annotation.Generated(value = "OracleSDKGenerator", comments = "API Version: 20231130")
@com.fasterxml.jackson.databind.annotation.JsonDeserialize(
builder = LoraTrainingConfig.Builder.class)
@com.fasterxml.jackson.annotation.JsonTypeInfo(
use = com.fasterxml.jackson.annotation.JsonTypeInfo.Id.NAME,
include = com.fasterxml.jackson.annotation.JsonTypeInfo.As.PROPERTY,
property = "trainingConfigType")
@com.fasterxml.jackson.annotation.JsonFilter(
com.oracle.bmc.http.client.internal.ExplicitlySetBmcModel.EXPLICITLY_SET_FILTER_NAME)
public final class LoraTrainingConfig extends TrainingConfig {
@com.fasterxml.jackson.databind.annotation.JsonPOJOBuilder(withPrefix = "")
public static class Builder {
@com.fasterxml.jackson.annotation.JsonProperty("totalTrainingEpochs")
private Integer totalTrainingEpochs;
public Builder totalTrainingEpochs(Integer totalTrainingEpochs) {
this.totalTrainingEpochs = totalTrainingEpochs;
this.__explicitlySet__.add("totalTrainingEpochs");
return this;
}
@com.fasterxml.jackson.annotation.JsonProperty("learningRate")
private Double learningRate;
public Builder learningRate(Double learningRate) {
this.learningRate = learningRate;
this.__explicitlySet__.add("learningRate");
return this;
}
@com.fasterxml.jackson.annotation.JsonProperty("trainingBatchSize")
private Integer trainingBatchSize;
public Builder trainingBatchSize(Integer trainingBatchSize) {
this.trainingBatchSize = trainingBatchSize;
this.__explicitlySet__.add("trainingBatchSize");
return this;
}
@com.fasterxml.jackson.annotation.JsonProperty("earlyStoppingPatience")
private Integer earlyStoppingPatience;
public Builder earlyStoppingPatience(Integer earlyStoppingPatience) {
this.earlyStoppingPatience = earlyStoppingPatience;
this.__explicitlySet__.add("earlyStoppingPatience");
return this;
}
@com.fasterxml.jackson.annotation.JsonProperty("earlyStoppingThreshold")
private Double earlyStoppingThreshold;
public Builder earlyStoppingThreshold(Double earlyStoppingThreshold) {
this.earlyStoppingThreshold = earlyStoppingThreshold;
this.__explicitlySet__.add("earlyStoppingThreshold");
return this;
}
@com.fasterxml.jackson.annotation.JsonProperty("logModelMetricsIntervalInSteps")
private Integer logModelMetricsIntervalInSteps;
public Builder logModelMetricsIntervalInSteps(Integer logModelMetricsIntervalInSteps) {
this.logModelMetricsIntervalInSteps = logModelMetricsIntervalInSteps;
this.__explicitlySet__.add("logModelMetricsIntervalInSteps");
return this;
}
/** This parameter represents the LoRA rank of the update matrices. */
@com.fasterxml.jackson.annotation.JsonProperty("loraR")
private Integer loraR;
/**
* This parameter represents the LoRA rank of the update matrices.
*
* @param loraR the value to set
* @return this builder
*/
public Builder loraR(Integer loraR) {
this.loraR = loraR;
this.__explicitlySet__.add("loraR");
return this;
}
/** This parameter represents the scaling factor for the weight matrices in LoRA. */
@com.fasterxml.jackson.annotation.JsonProperty("loraAlpha")
private Integer loraAlpha;
/**
* This parameter represents the scaling factor for the weight matrices in LoRA.
*
* @param loraAlpha the value to set
* @return this builder
*/
public Builder loraAlpha(Integer loraAlpha) {
this.loraAlpha = loraAlpha;
this.__explicitlySet__.add("loraAlpha");
return this;
}
/** This parameter indicates the dropout probability for LoRA layers. */
@com.fasterxml.jackson.annotation.JsonProperty("loraDropout")
private Double loraDropout;
/**
* This parameter indicates the dropout probability for LoRA layers.
*
* @param loraDropout the value to set
* @return this builder
*/
public Builder loraDropout(Double loraDropout) {
this.loraDropout = loraDropout;
this.__explicitlySet__.add("loraDropout");
return this;
}
@com.fasterxml.jackson.annotation.JsonIgnore
private final java.util.Set __explicitlySet__ = new java.util.HashSet();
public LoraTrainingConfig build() {
LoraTrainingConfig model =
new LoraTrainingConfig(
this.totalTrainingEpochs,
this.learningRate,
this.trainingBatchSize,
this.earlyStoppingPatience,
this.earlyStoppingThreshold,
this.logModelMetricsIntervalInSteps,
this.loraR,
this.loraAlpha,
this.loraDropout);
for (String explicitlySetProperty : this.__explicitlySet__) {
model.markPropertyAsExplicitlySet(explicitlySetProperty);
}
return model;
}
@com.fasterxml.jackson.annotation.JsonIgnore
public Builder copy(LoraTrainingConfig model) {
if (model.wasPropertyExplicitlySet("totalTrainingEpochs")) {
this.totalTrainingEpochs(model.getTotalTrainingEpochs());
}
if (model.wasPropertyExplicitlySet("learningRate")) {
this.learningRate(model.getLearningRate());
}
if (model.wasPropertyExplicitlySet("trainingBatchSize")) {
this.trainingBatchSize(model.getTrainingBatchSize());
}
if (model.wasPropertyExplicitlySet("earlyStoppingPatience")) {
this.earlyStoppingPatience(model.getEarlyStoppingPatience());
}
if (model.wasPropertyExplicitlySet("earlyStoppingThreshold")) {
this.earlyStoppingThreshold(model.getEarlyStoppingThreshold());
}
if (model.wasPropertyExplicitlySet("logModelMetricsIntervalInSteps")) {
this.logModelMetricsIntervalInSteps(model.getLogModelMetricsIntervalInSteps());
}
if (model.wasPropertyExplicitlySet("loraR")) {
this.loraR(model.getLoraR());
}
if (model.wasPropertyExplicitlySet("loraAlpha")) {
this.loraAlpha(model.getLoraAlpha());
}
if (model.wasPropertyExplicitlySet("loraDropout")) {
this.loraDropout(model.getLoraDropout());
}
return this;
}
}
/** Create a new builder. */
public static Builder builder() {
return new Builder();
}
public Builder toBuilder() {
return new Builder().copy(this);
}
@Deprecated
public LoraTrainingConfig(
Integer totalTrainingEpochs,
Double learningRate,
Integer trainingBatchSize,
Integer earlyStoppingPatience,
Double earlyStoppingThreshold,
Integer logModelMetricsIntervalInSteps,
Integer loraR,
Integer loraAlpha,
Double loraDropout) {
super(
totalTrainingEpochs,
learningRate,
trainingBatchSize,
earlyStoppingPatience,
earlyStoppingThreshold,
logModelMetricsIntervalInSteps);
this.loraR = loraR;
this.loraAlpha = loraAlpha;
this.loraDropout = loraDropout;
}
/** This parameter represents the LoRA rank of the update matrices. */
@com.fasterxml.jackson.annotation.JsonProperty("loraR")
private final Integer loraR;
/**
* This parameter represents the LoRA rank of the update matrices.
*
* @return the value
*/
public Integer getLoraR() {
return loraR;
}
/** This parameter represents the scaling factor for the weight matrices in LoRA. */
@com.fasterxml.jackson.annotation.JsonProperty("loraAlpha")
private final Integer loraAlpha;
/**
* This parameter represents the scaling factor for the weight matrices in LoRA.
*
* @return the value
*/
public Integer getLoraAlpha() {
return loraAlpha;
}
/** This parameter indicates the dropout probability for LoRA layers. */
@com.fasterxml.jackson.annotation.JsonProperty("loraDropout")
private final Double loraDropout;
/**
* This parameter indicates the dropout probability for LoRA layers.
*
* @return the value
*/
public Double getLoraDropout() {
return loraDropout;
}
@Override
public String toString() {
return this.toString(true);
}
/**
* Return a string representation of the object.
*
* @param includeByteArrayContents true to include the full contents of byte arrays
* @return string representation
*/
public String toString(boolean includeByteArrayContents) {
java.lang.StringBuilder sb = new java.lang.StringBuilder();
sb.append("LoraTrainingConfig(");
sb.append("super=").append(super.toString(includeByteArrayContents));
sb.append(", loraR=").append(String.valueOf(this.loraR));
sb.append(", loraAlpha=").append(String.valueOf(this.loraAlpha));
sb.append(", loraDropout=").append(String.valueOf(this.loraDropout));
sb.append(")");
return sb.toString();
}
@Override
public boolean equals(Object o) {
if (this == o) {
return true;
}
if (!(o instanceof LoraTrainingConfig)) {
return false;
}
LoraTrainingConfig other = (LoraTrainingConfig) o;
return java.util.Objects.equals(this.loraR, other.loraR)
&& java.util.Objects.equals(this.loraAlpha, other.loraAlpha)
&& java.util.Objects.equals(this.loraDropout, other.loraDropout)
&& super.equals(other);
}
@Override
public int hashCode() {
final int PRIME = 59;
int result = super.hashCode();
result = (result * PRIME) + (this.loraR == null ? 43 : this.loraR.hashCode());
result = (result * PRIME) + (this.loraAlpha == null ? 43 : this.loraAlpha.hashCode());
result = (result * PRIME) + (this.loraDropout == null ? 43 : this.loraDropout.hashCode());
return result;
}
}
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