Edit on GitHub
  • John Snow Labs
  • Apr 28, 2025

Gemma 3 4B IT INT4

  • en
  • open_source
  • openvino

Description

Gemma3ForMultiModal can load Gemma 3 Vision models for visual question answering. The model consists of a vision encoder, a text encoder, a text decoder and a model merger. The vision encoder will encode the input image, the text encoder will encode the input text, the model merger will merge the image and text embeddings, and the text decoder will output the answer.

Gemma 3 is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. It features:

  • Large 128K context window
  • Multilingual support in over 140 languages
  • Multimodal capabilities handling both text and image inputs
  • Optimized for deployment on limited resources (laptops, desktops, cloud)

Predicted Entities

Download Copy S3 URI

How to use

import sparknlp
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline
from pyspark.sql.functions import lit

image_df = spark.read.format("image").load(path=images_path) # Replace with your image path
test_df = image_df.withColumn("text", lit("<bos><start_of_turn>user\nYou are a helpful assistant.\n\n<start_of_image>Describe this image in detail.<end_of_turn>\n<start_of_turn>model\n"))

imageAssembler = ImageAssembler()   
          .setInputCol("image")   
          .setOutputCol("image_assembler")

visualQAClassifier = Gemma3ForMultiModal.pretrained()   
          .setInputCols("image_assembler")   
          .setOutputCol("answer")

pipeline = Pipeline().setStages([
          imageAssembler,
          visualQAClassifier
])

result = pipeline.fit(test_df).transform(test_df)
result.select("image_assembler.origin", "answer.result").show(False)

import spark.implicits._
import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline
import org.apache.spark.sql.DataFrame
import org.apache.spark.sql.functions.lit

val imageFolder = "path/to/your/images" // Replace with your image path

val imageDF: DataFrame = spark.read
     .format("image")
     .option("dropInvalid", value = true)
     .load(imageFolder)

val testDF: DataFrame = imageDF.withColumn("text", lit("<bos><start_of_turn>user\nYou are a helpful assistant.\n\n<start_of_image>Describe this image in detail.<end_of_turn>\n<start_of_turn>model\n"))

val imageAssembler: ImageAssembler = new ImageAssembler()
     .setInputCol("image")
     .setOutputCol("image_assembler")

val visualQAClassifier = Gemma3ForMultiModal.pretrained()
     .setInputCols("image_assembler")
     .setOutputCol("answer")

val pipeline = new Pipeline().setStages(Array(
     imageAssembler,
     visualQAClassifier
))

val result = pipeline.fit(testDF).transform(testDF)

result.select("image_assembler.origin", "answer.result").show(false)

Model Information

Model Name: gemma_3_4b_it_int4
Compatibility: Spark NLP 5.5.1+
License: Open Source
Edition: Official
Input Labels: [image_assembler]
Output Labels: [answer]
Language: en
Size: 3.1 GB