`sparknlp.annotator.cv.mllama_for_multimodal`#

Module Contents#

Classes#

MLLamaForMultimodal

MLLamaForMultimodal can load LLAMA 3.2 Vision models for visual question answering.

class MLLamaForMultimodal(classname='com.johnsnowlabs.nlp.annotators.cv.MLLamaForMultimodal', java_model=None)[source]#

MLLamaForMultimodal can load LLAMA 3.2 Vision models for visual question answering. The model consists of a vision encoder, a text encoder, and a text decoder. The vision encoder encodes the input image, the text encoder processes the input question alongside the image encoding, and the text decoder generates the answer to the question.

The Llama 3.2-Vision collection comprises pretrained and instruction-tuned multimodal large language models (LLMs) available in 11B and 90B sizes. These models are optimized for visual recognition, image reasoning, captioning, and answering general questions about images. The models outperform many open-source and proprietary multimodal models on standard industry benchmarks.

Pretrained models can be loaded with pretrained() of the companion object:

>>> visualQAClassifier = MLLamaForMultimodal.pretrained() \
...     .setInputCols(["image_assembler"]) \
...     .setOutputCol("answer")

The default model is “llama_3_2_11b_vision_instruct_int4”, if no name is provided.

For available pretrained models, refer to the Models Hub.

Models from the HuggingFace 🤗 Transformers library are also compatible with Spark NLP 🚀. To check compatibility and learn how to import them, see Import Transformers into Spark NLP 🚀. For extended examples, refer to the MLLamaForMultimodal Test Suite.

Input Annotation types	Output Annotation type
`IMAGE`	`DOCUMENT`

Parameters:

batchSizeint, optional: Batch size. Larger values allow faster processing but require more memory, by default 2.
configProtoBytesbytes, optional: ConfigProto from TensorFlow, serialized into a byte array.
maxSentenceLengthint, optional: Maximum sentence length to process, by default 50.

Examples

>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> from pyspark.sql.functions import lit
>>> image_df = SparkSessionForTest.spark.read.format("image").load(path=images_path)
>>> test_df = image_df.withColumn(
...     "text",
...     lit("<|begin_of_text|><|begin_of_text|><|start_header_id|>user<|end_header_id|>

“) … ) >>> imageAssembler = ImageAssembler() … .setInputCol(“image”) … .setOutputCol(“image_assembler”) >>> visualQAClassifier = MLLamaForMultimodal.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(truncate=False) +————————————–+———————————————————————-+ |origin |result | +--------------------------------------+----------------------------------------------------------------------+ |[file:///content/images/cat_image.jpg]|[The unusual aspect of this picture is the presence of two cats lying on a pink couch]| +————————————–+———————————————————————-+

name = 'MLLamaForMultimodal'[source]#

inputAnnotatorTypes[source]#

outputAnnotatorType = 'document'[source]#

configProtoBytes[source]#

minOutputLength[source]#

maxOutputLength[source]#

doSample[source]#

temperature[source]#

topK[source]#

topP[source]#

repetitionPenalty[source]#

noRepeatNgramSize[source]#

ignoreTokenIds[source]#

beamSize[source]#

setMaxSentenceSize(value)[source]#

Sets Maximum sentence length that the annotator will process, by default 50.

Parameters:

valueint: Maximum sentence length that the annotator will process

setIgnoreTokenIds(value)[source]#

A list of token ids which are ignored in the decoder’s output.

Parameters:

valueList[int]: The words to be filtered out

setConfigProtoBytes(b)[source]#

Sets configProto from tensorflow, serialized into byte array.

Parameters:

bList[int]: ConfigProto from tensorflow, serialized into byte array

setMinOutputLength(value)[source]#

Sets minimum length of the sequence to be generated.

Parameters:

valueint: Minimum length of the sequence to be generated

setMaxOutputLength(value)[source]#

Sets maximum length of output text.

Parameters:

valueint: Maximum length of output text

setDoSample(value)[source]#

Sets whether or not to use sampling, use greedy decoding otherwise.

Parameters:

valuebool: Whether or not to use sampling; use greedy decoding otherwise

setTemperature(value)[source]#

Sets the value used to module the next token probabilities.

Parameters:

valuefloat: The value used to module the next token probabilities

setTopK(value)[source]#

Sets the number of highest probability vocabulary tokens to keep for top-k-filtering.

Parameters:

valueint: Number of highest probability vocabulary tokens to keep

setTopP(value)[source]#

Sets the top cumulative probability for vocabulary tokens.

If set to float < 1, only the most probable tokens with probabilities that add up to topP or higher are kept for generation.

Parameters:

valuefloat: Cumulative probability for vocabulary tokens

setRepetitionPenalty(value)[source]#

Sets the parameter for repetition penalty. 1.0 means no penalty.

Parameters:

valuefloat: The repetition penalty

References

See Ctrl: A Conditional Transformer Language Model For Controllable Generation for more details.

setNoRepeatNgramSize(value)[source]#

Sets size of n-grams that can only occur once.

If set to int > 0, all ngrams of that size can only occur once.

Parameters:

valueint: N-gram size can only occur once

setBeamSize(value)[source]#

Sets the number of beam size for beam search, by default 4.

Parameters:

valueint: Number of beam size for beam search

static loadSavedModel(folder, spark_session, use_openvino=False)[source]#

Loads a locally saved model.

Parameters:

folderstr: Folder of the saved model
spark_sessionpyspark.sql.SparkSession: The current SparkSession

Returns:

CLIPForZeroShotClassification: The restored model

static pretrained(name='llama_3_2_11b_vision_instruct_int4', lang='en', remote_loc=None)[source]#

Downloads and loads a pretrained model.

Parameters:

namestr, optional: Name of the pretrained model, by default “llama_3_2_11b_vision_instruct_int4”
langstr, optional: Language of the pretrained model, by default “en”
remote_locstr, optional: Optional remote address of the resource, by default None. Will use Spark NLPs repositories otherwise.

Returns:

MLLamaForMultimodal: The restored model

sparknlp.annotator.cv.mllama_for_multimodal#

Module Contents#

Classes#

`sparknlp.annotator.cv.mllama_for_multimodal`#