Qwen3-Embedding-0.6B-GGUF

Description

The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.

Imported from https://huggingface.co/Qwen/Qwen3-Embedding-0.6B-GGUF

Predicted Entities

Live Demo Open in Colab Download Copy S3 URI

How to use

import sparknlp
from sparknlp.base import *
from sparknlp.annotator import *
from pyspark.ml import Pipeline

document = DocumentAssembler() \
    .setInputCol("text") \
    .setOutputCol("document")
autoGGUFEmbeddings = AutoGGUFEmbeddings.pretrained("Qwen3_Embedding_0.6B_Q8_0_gguf") \
    .setInputCols(["document"]) \
    .setOutputCol("embeddings") \
    .setBatchSize(4) \
    .setNGpuLayers(99) \
    .setPoolingType("MEAN")
pipeline = Pipeline().setStages([document, autoGGUFEmbeddings])

data = spark.createDataFrame([["The moons of Jupiter are 77 in total, with 79 confirmed natural satellites and 2 man-made ones."]]).toDF("text")
result = pipeline.fit(data).transform(data)
result.select("embeddings.embeddings").show(truncate = False)

import com.johnsnowlabs.nlp.base._
import com.johnsnowlabs.nlp.annotator._
import org.apache.spark.ml.Pipeline
import spark.implicits._

val document = new DocumentAssembler().setInputCol("text").setOutputCol("document")

val autoGGUFModel = AutoGGUFEmbeddings
  .pretrained("Qwen3_Embedding_0.6B_Q8_0_gguf")
  .setInputCols("document")
  .setOutputCol("embeddings")
  .setBatchSize(4)
  .setPoolingType("MEAN")

val pipeline = new Pipeline().setStages(Array(document, autoGGUFModel))

val data = Seq(
  "The moons of Jupiter are 77 in total, with 79 confirmed natural satellites and 2 man-made ones.")
  .toDF("text")
val result = pipeline.fit(data).transform(data)
result.select("embeddings.embeddings").show(truncate = false)

Model Information