diff --git a/aiplatform/src/main/java/aiplatform/EmbeddingModelTuningSample.java b/aiplatform/src/main/java/aiplatform/EmbeddingModelTuningSample.java
index 86805bbdeda..2e859958e41 100644
--- a/aiplatform/src/main/java/aiplatform/EmbeddingModelTuningSample.java
+++ b/aiplatform/src/main/java/aiplatform/EmbeddingModelTuningSample.java
@@ -29,11 +29,8 @@
 import java.util.regex.Matcher;
 import java.util.regex.Pattern;
 
-// [END aiplatform_sdk_embedding_model_tuning]
-
 public class EmbeddingModelTuningSample {
   public static void main(String[] args) throws IOException {
-    // [START aiplatform_sdk_embedding_model_tuning]
     // TODO(developer): Replace these variables before running this sample.
     String apiEndpoint = "us-central1-aiplatform.googleapis.com:443";
     String project = "PROJECT";
@@ -41,12 +38,14 @@ public static void main(String[] args) throws IOException {
     String taskType = "DEFAULT";
     String pipelineJobDisplayName = "PIPELINE_JOB_DISPLAY_NAME";
     String outputDir = "OUTPUT_DIR";
-    String queriesPath = "QUERIES";
-    String corpusPath = "CORPUS";
-    String trainLabelPath = "TRAIN_LABEL";
-    String testLabelPath = "TEST_LABEL";
+    String queriesPath = "QUERIES_PATH";
+    String corpusPath = "CORPUS_PATH";
+    String trainLabelPath = "TRAIN_LABEL_PATH";
+    String testLabelPath = "TEST_LABEL_PATH";
+    double learningRateMultiplier = 1.0;
+    int outputDimensionality = 768;
     int batchSize = 128;
-    int iterations = 1000;
+    int trainSteps = 1000;
 
     createEmbeddingModelTuningPipelineJob(
         apiEndpoint,
@@ -59,12 +58,12 @@ public static void main(String[] args) throws IOException {
         corpusPath,
         trainLabelPath,
         testLabelPath,
+        learningRateMultiplier,
+        outputDimensionality,
         batchSize,
-        iterations);
-    // [END aiplatform_sdk_embedding_model_tuning]
+        trainSteps);
   }
 
-  // [START aiplatform_sdk_embedding_model_tuning]
   public static PipelineJob createEmbeddingModelTuningPipelineJob(
       String apiEndpoint,
       String project,
@@ -76,28 +75,30 @@ public static PipelineJob createEmbeddingModelTuningPipelineJob(
       String corpusPath,
       String trainLabelPath,
       String testLabelPath,
+      double learningRateMultiplier,
+      int outputDimensionality,
       int batchSize,
-      int iterations)
+      int trainSteps)
       throws IOException {
     Matcher matcher = Pattern.compile("^(?<Location>\\w+-\\w+)").matcher(apiEndpoint);
     String location = matcher.matches() ? matcher.group("Location") : "us-central1";
     String templateUri =
-        "https://us-kfp.pkg.dev/ml-pipeline/llm-text-embedding/tune-text-embedding-model/v1.1.2";
+        "https://us-kfp.pkg.dev/ml-pipeline/llm-text-embedding/tune-text-embedding-model/v1.1.3";
     PipelineServiceSettings settings =
         PipelineServiceSettings.newBuilder().setEndpoint(apiEndpoint).build();
     try (PipelineServiceClient client = PipelineServiceClient.create(settings)) {
       Map<String, Value> parameterValues =
           Map.of(
-              "project", valueOf(project),
               "base_model_version_id", valueOf(baseModelVersionId),
               "task_type", valueOf(taskType),
-              "location", valueOf(location),
               "queries_path", valueOf(queriesPath),
               "corpus_path", valueOf(corpusPath),
               "train_label_path", valueOf(trainLabelPath),
               "test_label_path", valueOf(testLabelPath),
+              "learning_rate_multiplier", valueOf(learningRateMultiplier),
+              "output_dimensionality", valueOf(outputDimensionality),
               "batch_size", valueOf(batchSize),
-              "iterations", valueOf(iterations));
+              "train_steps", valueOf(trainSteps));
       PipelineJob pipelineJob =
           PipelineJob.newBuilder()
               .setTemplateUri(templateUri)
@@ -124,5 +125,9 @@ private static Value valueOf(String s) {
   private static Value valueOf(int n) {
     return Value.newBuilder().setNumberValue(n).build();
   }
-  // [END aiplatform_sdk_embedding_model_tuning]
+
+  private static Value valueOf(double n) {
+    return Value.newBuilder().setNumberValue(n).build();
+  }
 }
+// [END aiplatform_sdk_embedding_model_tuning]
diff --git a/aiplatform/src/test/java/aiplatform/EmbeddingModelTuningSampleTest.java b/aiplatform/src/test/java/aiplatform/EmbeddingModelTuningSampleTest.java
index de07adffed3..61e8b77926b 100644
--- a/aiplatform/src/test/java/aiplatform/EmbeddingModelTuningSampleTest.java
+++ b/aiplatform/src/test/java/aiplatform/EmbeddingModelTuningSampleTest.java
@@ -53,7 +53,7 @@ public class EmbeddingModelTuningSampleTest {
 
   private static final String API_ENDPOINT = "us-central1-aiplatform.googleapis.com:443";
   private static final String PROJECT = System.getenv("UCAIP_PROJECT_ID");
-  private static final String BASE_MODEL_VERSION_ID = "textembedding-gecko@003";
+  private static final String BASE_MODEL_VERSION_ID = "text-embedding-004";
   private static final String TASK_TYPE = "DEFAULT";
   private static final String JOB_DISPLAY_NAME = "embedding-customization-pipeline-sample";
   private static final String QUERIES =
@@ -64,6 +64,8 @@ public class EmbeddingModelTuningSampleTest {
   private static final String TEST_LABEL = "gs://embedding-customization-pipeline/dataset/test.tsv";
   private static final String OUTPUT_DIR =
       "gs://ucaip-samples-us-central1/training_pipeline_output";
+  private static final double LEARNING_RATE_MULTIPLIER = 0.3;
+  private static final int OUTPUT_DIMENSIONALITY = 512;
   private static final int BATCH_SIZE = 50;
   private static final int ITERATIONS = 300;
 
@@ -135,6 +137,8 @@ public void createPipelineJobEmbeddingModelTuningSample() throws IOException {
             CORPUS,
             TRAIN_LABEL,
             TEST_LABEL,
+            LEARNING_RATE_MULTIPLIER,
+            OUTPUT_DIMENSIONALITY,
             BATCH_SIZE,
             ITERATIONS);
     assertThat(job.getState()).isNotEqualTo(PipelineState.PIPELINE_STATE_FAILED);