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  • John Snow Labs
  • May 16, 2025

InternVL 2.5 4B INT4

  • en
  • openvino
  • open_source

Description

Visual Question Answering using InternVL.

InternVLForMultiModal can load InternVL 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.

InternVL 2.5 is an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. Key features include:

  • Large context window support
  • Multilingual support
  • Multimodal capabilities handling both text and image inputs
  • Optimized for deployment with int4 quantization

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("<|im_start|><image>\nDescribe this image in detail.<|im_end|><|im_start|>assistant\n"))

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

visualQAClassifier = InternVLForMultiModal.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("<|im_start|><image>\nDescribe this image in detail.<|im_end|><|im_start|>assistant\n"))

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

val visualQAClassifier = InternVLForMultiModal.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: internvl2_5_4b_int4
Compatibility: Spark NLP 5.5.1+
License: Open Source
Edition: Official
Input Labels: [image_assembler]
Output Labels: [answer]
Language: en
Size: 2.1 GB