2025-01-30-starcoder2_3b_int8_en

docs/_posts/prabod/2025-01-30-starcoder2_3b_int4_en.md

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+---
+layout: model
+title: starcoder2_3b_int4 model from bigcode
+author: John Snow Labs
+name: starcoder2_3b_int4
+date: 2025-01-30
+tags: [en, open_source, openvino]
+task: Text Generation
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: StarCoderTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+StarCoder2-3B model is a 3B parameter model trained on 17 programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 3+ trillion tokens.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/starcoder2_3b_int4_en_5.5.0_3.0_1738276280010.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/starcoder2_3b_int4_en_5.5.0_3.0_1738276280010.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+data = spark.createDataFrame([
+            [1, "def add(a, b):"]]).toDF("id", "text")
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+starcoder_loaded = StarCoderTransformer \
+    .pretrained("starcoder2_3b_int4","en") \
+    .setMaxOutputLength(50) \
+    .setDoSample(False) \
+    .setInputCols(["documents"]) \
+    .setOutputCol("generation")
+
+pipeline = Pipeline().setStages([document_assembler, starcoder_loaded])
+results = pipeline.fit(data).transform(data)
+
+results.select("generation.result").show(truncate=False)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val seq2seq = StarCoderTransformer.pretrained("starcoder2_3b_int4","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, seq2seq))
+val data = Seq(""def add(a, b):").toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|starcoder2_3b_int4|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[generation]|
+|Language:|en|
+|Size:|1.6 GB|
+
+## References
+
+https://huggingface.co/bigcode/starcoder2-3b

docs/_posts/prabod/2025-01-30-starcoder2_3b_int8_en.md

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+---
+layout: model
+title: starcoder2_3b_int8 model from bigcode
+author: John Snow Labs
+name: starcoder2_3b_int8
+date: 2025-01-30
+tags: [en, open_source, openvino]
+task: Text Generation
+language: en
+edition: Spark NLP 5.5.0
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: StarCoderTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+StarCoder2-3B model is a 3B parameter model trained on 17 programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 3+ trillion tokens.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/starcoder2_3b_int8_en_5.5.0_3.0_1738224538904.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/starcoder2_3b_int8_en_5.5.0_3.0_1738224538904.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+data = spark.createDataFrame([
+            [1, "def add(a, b):"]]).toDF("id", "text")
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+starcoder_loaded = StarCoderTransformer \
+    .pretrained("starcoder2_3b_int8","en") \
+    .setMaxOutputLength(50) \
+    .setDoSample(False) \
+    .setInputCols(["documents"]) \
+    .setOutputCol("generation")
+
+pipeline = Pipeline().setStages([document_assembler, starcoder_loaded])
+results = pipeline.fit(data).transform(data)
+
+results.select("generation.result").show(truncate=False)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val seq2seq = StarCoderTransformer.pretrained("starcoder2_3b_int8","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, seq2seq))
+val data = Seq(""def add(a, b):").toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|starcoder2_3b_int8|
+|Compatibility:|Spark NLP 5.5.0+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[generation]|
+|Language:|en|
+|Size:|2.7 GB|
+
+## References
+
+https://huggingface.co/bigcode/starcoder2-3b

docs/_posts/prabod/2025-02-12-olmo_1b_int4_en.md

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+---
+layout: model
+title: OLMo 1B
+author: John Snow Labs
+name: olmo_1b_int4
+date: 2025-02-12
+tags: [en, open_source, onnx]
+task: Text Generation
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: onnx
+annotator: OLMoTransformer
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+OLMo is a series of Open Language Models designed to enable the science of language models. The OLMo models are trained on the Dolma dataset. We release all code, checkpoints, logs (coming soon), and details involved in training these models. This model has been converted from allenai/OLMo-1B for the Hugging Face Transformers format.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/olmo_1b_int4_en_5.5.1_3.0_1739362135440.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/olmo_1b_int4_en_5.5.1_3.0_1739362135440.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+data = spark.createDataFrame([
+            [1, "My name is Leo, "]]).toDF("id", "text")
+document_assembler = DocumentAssembler() \
+    .setInputCol("text") \
+    .setOutputCol("documents")
+
+olmor_loaded = OLMoTransformer \
+    .pretrained("olmo_1b_int4","en") \
+    .setMaxOutputLength(50) \
+    .setDoSample(False) \
+    .setInputCols(["documents"]) \
+    .setOutputCol("generation")
+
+pipeline = Pipeline().setStages([document_assembler, olmor_loaded])
+results = pipeline.fit(data).transform(data)
+
+results.select("generation.result").show(truncate=False)
+```
+```scala
+val documentAssembler = new DocumentAssembler() 
+    .setInputCol("text") 
+    .setOutputCol("document")
+
+val seq2seq = OLMoTransformer.pretrained("olmo_1b_int4","en") 
+    .setInputCols(Array("document")) 
+    .setOutputCol("generation")
+
+val pipeline = new Pipeline().setStages(Array(documentAssembler, seq2seq))
+val data = Seq(""My name is Leo,").toDF("text")
+val pipelineModel = pipeline.fit(data)
+val pipelineDF = pipelineModel.transform(data)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|olmo_1b_int4|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[documents]|
+|Output Labels:|[generation]|
+|Language:|en|
+|Size:|1.1 GB|
+
+## References
+
+https://huggingface.co/allenai/OLMo-1B-hf

docs/_posts/prabod/2025-02-13-llava_1_5_7b_hf_en.md

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+---
+layout: model
+title: llava-1.5-7b-hf
+author: John Snow Labs
+name: llava_1_5_7b_hf
+date: 2025-02-13
+tags: [en, open_source, openvino]
+task: Image Captioning
+language: en
+edition: Spark NLP 5.5.1
+spark_version: 3.0
+supported: true
+engine: openvino
+annotator: LLAVAForMultiModal
+article_header:
+  type: cover
+use_language_switcher: "Python-Scala-Java"
+---
+
+## Description
+
+LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
+
+## Predicted Entities
+
+
+
+{:.btn-box}
+<button class="button button-orange" disabled>Live Demo</button>
+<button class="button button-orange" disabled>Open in Colab</button>
+[Download](https://s3.amazonaws.com/auxdata.johnsnowlabs.com/public/models/llava_1_5_7b_hf_en_5.5.1_3.0_1739432390529.zip){:.button.button-orange.button-orange-trans.arr.button-icon}
+[Copy S3 URI](s3://auxdata.johnsnowlabs.com/public/models/llava_1_5_7b_hf_en_5.5.1_3.0_1739432390529.zip){:.button.button-orange.button-orange-trans.button-icon.button-copy-s3}
+
+## How to use
+
+
+
+<div class="tabs-box" markdown="1">
+{% include programmingLanguageSelectScalaPythonNLU.html %}
+```python
+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("USER: \n <|image|> \n What's this picture about? \n ASSISTANT:\n"))
+
+imageAssembler = ImageAssembler()   
+          .setInputCol("image")   
+          .setOutputCol("image_assembler")
+
+visualQAClassifier = LLAVAForMultiModal.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)
+```
+```scala
+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("USER: \n <|image|> \nWhat is unusual on this picture? \n ASSISTANT:\n"))
+
+val imageAssembler: ImageAssembler = new ImageAssembler()
+     .setInputCol("image")
+     .setOutputCol("image_assembler")
+
+val visualQAClassifier = LLAVAForMultiModal.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)
+```
+</div>
+
+{:.model-param}
+## Model Information
+
+{:.table-model}
+|---|---|
+|Model Name:|llava_1_5_7b_hf|
+|Compatibility:|Spark NLP 5.5.1+|
+|License:|Open Source|
+|Edition:|Official|
+|Input Labels:|[image_assembler]|
+|Output Labels:|[answer]|
+|Language:|en|
+|Size:|3.9 GB|
+
+## References
+
+https://huggingface.co/llava-hf/llava-1.5-7b-hf