Packages

class LLAMA3Transformer extends AnnotatorModel[LLAMA3Transformer] with HasBatchedAnnotate[LLAMA3Transformer] with ParamsAndFeaturesWritable with WriteOnnxModel with WriteOpenvinoModel with HasGeneratorProperties with HasEngine

Llama 3: Cutting-Edge Foundation and Fine-Tuned Chat Models

The Llama 3 release introduces a new family of large language models, ranging from 8B to 70B parameters. Llama 3 models are designed with a greater emphasis on efficiency, performance, and safety, achieving remarkable advancements in training and deployment processes. These models are trained on a diversified dataset that significantly enhances their capability to generate more accurate and contextually relevant outputs.

The fine-tuned variants, known as Llama 3-instruct, are specifically optimized for dialogue-based applications, making use of Reinforcement Learning from Human Feedback (RLHF) with an advanced reward model. Llama 3-instruct models demonstrate state-of-the-art performance across multiple benchmarks and surpass the capabilities of Llama 2, particularly in conversational settings.

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

val llama3 = LLAMA3Transformer.pretrained()
  .setInputCols("document")
  .setOutputCol("generation")

The default model is "llama_3_7b_chat_hf_int8", if no name is provided. For available pretrained models please see the Models Hub.

For extended examples of usage, see LLAMA3TestSpec.

References:

Paper Abstract:

Llama 3 represents Meta’s latest innovation in the development of large language models (LLMs), offering a series of models from 1 billion to 70 billion parameters. These models have been fine-tuned for dialogue applications under the Llama 3-Chat series, ensuring they are highly responsive and context-aware. Our Llama 3 models not only excel in various benchmarks but also incorporate enhanced safety and alignment features to address ethical concerns and ensure responsible AI deployment. We invite the community to explore the capabilities of Llama 3 and contribute to ongoing research in the field of natural language processing.

Note:

This is a resource-intensive module, especially with larger models and sequences. Use of accelerators such as GPUs is strongly recommended.

Example

import spark.implicits._
import com.johnsnowlabs.nlp.base.DocumentAssembler
import com.johnsnowlabs.nlp.annotators.seq2seq.LLAMA3Transformer
import org.apache.spark.ml.Pipeline

val documentAssembler = new DocumentAssembler()
  .setInputCol("text")
  .setOutputCol("documents")

val llama3 = LLAMA3Transformer.pretrained("llama_3_7b_chat_hf_int8")
  .setInputCols(Array("documents"))
  .setMinOutputLength(15)
  .setMaxOutputLength(60)
  .setDoSample(false)
  .setTopK(40)
  .setNoRepeatNgramSize(3)
  .setOutputCol("generation")

val pipeline = new Pipeline().setStages(Array(documentAssembler, llama3))

val data = Seq(
  (
    1,
    """<|start_header_id|>system<|end_header_id|>

    You are a minion chatbot who always responds in minion speak!

    <|start_header_id|>user<|end_header_id|>

    Who are you?

    <|start_header_id|>assistant<|end_header_id|>
    """.stripMargin)
).toDF("id", "text")

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

result.select("generation.result").show(truncate = false)
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result                                                                                                                                                                                                  |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[Oooh, me am Minion! Me help you with things! Me speak Minion language, yeah! Bana-na-na!]                                                                         |
+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
Linear Supertypes
HasEngine, HasGeneratorProperties, WriteOpenvinoModel, WriteOnnxModel, HasBatchedAnnotate[LLAMA3Transformer], AnnotatorModel[LLAMA3Transformer], CanBeLazy, RawAnnotator[LLAMA3Transformer], HasOutputAnnotationCol, HasInputAnnotationCols, HasOutputAnnotatorType, ParamsAndFeaturesWritable, HasFeatures, DefaultParamsWritable, MLWritable, Model[LLAMA3Transformer], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. LLAMA3Transformer
  2. HasEngine
  3. HasGeneratorProperties
  4. WriteOpenvinoModel
  5. WriteOnnxModel
  6. HasBatchedAnnotate
  7. AnnotatorModel
  8. CanBeLazy
  9. RawAnnotator
  10. HasOutputAnnotationCol
  11. HasInputAnnotationCols
  12. HasOutputAnnotatorType
  13. ParamsAndFeaturesWritable
  14. HasFeatures
  15. DefaultParamsWritable
  16. MLWritable
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Serializable
  24. Identifiable
  25. AnyRef
  26. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new LLAMA3Transformer()
  2. new LLAMA3Transformer(uid: String)

    uid

    required uid for storing annotator to disk

Type Members

  1. type AnnotationContent = Seq[Row]

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    internal types to show Rows as a relevant StructType Should be deleted once Spark releases UserDefinedTypes to @developerAPI

    Attributes
    protected
    Definition Classes
    AnnotatorModel
  2. type AnnotatorType = String
    Definition Classes
    HasOutputAnnotatorType

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. def $$[T](feature: StructFeature[T]): T
    Attributes
    protected
    Definition Classes
    HasFeatures
  5. def $$[K, V](feature: MapFeature[K, V]): Map[K, V]
    Attributes
    protected
    Definition Classes
    HasFeatures
  6. def $$[T](feature: SetFeature[T]): Set[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  7. def $$[T](feature: ArrayFeature[T]): Array[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  8. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  9. def _transform(dataset: Dataset[_], recursivePipeline: Option[PipelineModel]): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  10. val addedTokens: MapFeature[String, Int]

    Additional tokens to be added to the vocabulary

  11. def afterAnnotate(dataset: DataFrame): DataFrame
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  12. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  13. def batchAnnotate(batchedAnnotations: Seq[Array[Annotation]]): Seq[Seq[Annotation]]

    takes a document and annotations and produces new annotations of this annotator's annotation type

    takes a document and annotations and produces new annotations of this annotator's annotation type

    batchedAnnotations

    Annotations that correspond to inputAnnotationCols generated by previous annotators if any

    returns

    any number of annotations processed for every input annotation. Not necessary one to one relationship

    Definition Classes
    LLAMA3Transformer → HasBatchedAnnotate
  14. def batchProcess(rows: Iterator[_]): Iterator[Row]
    Definition Classes
    HasBatchedAnnotate
  15. val batchSize: IntParam

    Size of every batch (Default depends on model).

    Size of every batch (Default depends on model).

    Definition Classes
    HasBatchedAnnotate
  16. val beamSize: IntParam

    Beam size for the beam search algorithm (Default: 4)

    Beam size for the beam search algorithm (Default: 4)

    Definition Classes
    HasGeneratorProperties
  17. def beforeAnnotate(dataset: Dataset[_]): Dataset[_]
    Attributes
    protected
    Definition Classes
    AnnotatorModel
  18. final def checkSchema(schema: StructType, inputAnnotatorType: String): Boolean
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  19. final def clear(param: Param[_]): LLAMA3Transformer.this.type
    Definition Classes
    Params
  20. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  21. def copy(extra: ParamMap): LLAMA3Transformer

    requirement for annotators copies

    requirement for annotators copies

    Definition Classes
    RawAnnotator → Model → Transformer → PipelineStage → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  23. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  24. val doSample: BooleanParam

    Whether or not to use sampling, use greedy decoding otherwise (Default: false)

    Whether or not to use sampling, use greedy decoding otherwise (Default: false)

    Definition Classes
    HasGeneratorProperties
  25. val engine: Param[String]

    This param is set internally once via loadSavedModel.

    This param is set internally once via loadSavedModel. That's why there is no setter

    Definition Classes
    HasEngine
  26. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  28. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  29. def explainParams(): String
    Definition Classes
    Params
  30. def extraValidate(structType: StructType): Boolean
    Attributes
    protected
    Definition Classes
    RawAnnotator
  31. def extraValidateMsg: String

    Override for additional custom schema checks

    Override for additional custom schema checks

    Attributes
    protected
    Definition Classes
    RawAnnotator
  32. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  33. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  34. val features: ArrayBuffer[Feature[_, _, _]]
    Definition Classes
    HasFeatures
  35. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  36. val generationConfig: StructFeature[GenerationConfig]
  37. def get[T](feature: StructFeature[T]): Option[T]
    Attributes
    protected
    Definition Classes
    HasFeatures
  38. def get[K, V](feature: MapFeature[K, V]): Option[Map[K, V]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  39. def get[T](feature: SetFeature[T]): Option[Set[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  40. def get[T](feature: ArrayFeature[T]): Option[Array[T]]
    Attributes
    protected
    Definition Classes
    HasFeatures
  41. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  42. def getBatchSize: Int

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  43. def getBeamSize: Int

    Definition Classes
    HasGeneratorProperties
  44. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  46. def getDoSample: Boolean

    Definition Classes
    HasGeneratorProperties
  47. def getEngine: String

    Definition Classes
    HasEngine
  48. def getGenerationConfig: GenerationConfig
  49. def getIgnoreTokenIds: Array[Int]

  50. def getInputCols: Array[String]

    returns

    input annotations columns currently used

    Definition Classes
    HasInputAnnotationCols
  51. def getLazyAnnotator: Boolean
    Definition Classes
    CanBeLazy
  52. def getMaxOutputLength: Int

    Definition Classes
    HasGeneratorProperties
  53. def getMinOutputLength: Int

    Definition Classes
    HasGeneratorProperties
  54. def getModelIfNotSet: LLAMA3

  55. def getNReturnSequences: Int

    Definition Classes
    HasGeneratorProperties
  56. def getNoRepeatNgramSize: Int

    Definition Classes
    HasGeneratorProperties
  57. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  58. final def getOutputCol: String

    Gets annotation column name going to generate

    Gets annotation column name going to generate

    Definition Classes
    HasOutputAnnotationCol
  59. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  60. def getRandomSeed: Option[Long]

    Definition Classes
    HasGeneratorProperties
  61. def getRepetitionPenalty: Double

    Definition Classes
    HasGeneratorProperties
  62. def getStopTokenIds: Array[Int]

    Definition Classes
    LLAMA3Transformer → HasGeneratorProperties
  63. def getTask: Option[String]

    Definition Classes
    HasGeneratorProperties
  64. def getTemperature: Double

    Definition Classes
    HasGeneratorProperties
  65. def getTopK: Int

    Definition Classes
    HasGeneratorProperties
  66. def getTopP: Double

    Definition Classes
    HasGeneratorProperties
  67. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  68. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  69. def hasParent: Boolean
    Definition Classes
    Model
  70. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  71. var ignoreTokenIds: IntArrayParam

    A list of token ids which are ignored in the decoder's output (Default: Array())

  72. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  73. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. val inputAnnotatorTypes: Array[AnnotatorType]

    Input annotator type : DOCUMENT

    Input annotator type : DOCUMENT

    Definition Classes
    LLAMA3Transformer → HasInputAnnotationCols
  75. final val inputCols: StringArrayParam

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    columns that contain annotations necessary to run this annotator AnnotatorType is used both as input and output columns if not specified

    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  76. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  77. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  78. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  79. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  80. val lazyAnnotator: BooleanParam
    Definition Classes
    CanBeLazy
  81. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  82. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  89. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  90. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  91. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  92. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  93. val maxInputLength: IntParam

    max length of the input sequence (Default: 0)

    max length of the input sequence (Default: 0)

    Definition Classes
    HasGeneratorProperties
  94. val maxOutputLength: IntParam

    Maximum length of the sequence to be generated (Default: 20)

    Maximum length of the sequence to be generated (Default: 20)

    Definition Classes
    HasGeneratorProperties
  95. val merges: MapFeature[(String, String), Int]

    Holding merges.txt coming from RoBERTa model

  96. val minOutputLength: IntParam

    Minimum length of the sequence to be generated (Default: 0)

    Minimum length of the sequence to be generated (Default: 0)

    Definition Classes
    HasGeneratorProperties
  97. def msgHelper(schema: StructType): String
    Attributes
    protected
    Definition Classes
    HasInputAnnotationCols
  98. val nReturnSequences: IntParam

    The number of sequences to return from the beam search.

    The number of sequences to return from the beam search.

    Definition Classes
    HasGeneratorProperties
  99. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  100. val noRepeatNgramSize: IntParam

    If set to int > 0, all ngrams of that size can only occur once (Default: 0)

    If set to int > 0, all ngrams of that size can only occur once (Default: 0)

    Definition Classes
    HasGeneratorProperties
  101. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  102. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  103. def onWrite(path: String, spark: SparkSession): Unit
    Definition Classes
    LLAMA3Transformer → ParamsAndFeaturesWritable
  104. val optionalInputAnnotatorTypes: Array[String]
    Definition Classes
    HasInputAnnotationCols
  105. val outputAnnotatorType: String

    Output annotator type : DOCUMENT

    Output annotator type : DOCUMENT

    Definition Classes
    LLAMA3Transformer → HasOutputAnnotatorType
  106. final val outputCol: Param[String]
    Attributes
    protected
    Definition Classes
    HasOutputAnnotationCol
  107. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  108. var parent: Estimator[LLAMA3Transformer]
    Definition Classes
    Model
  109. val randomSeed: Option[Long]

    Optional Random seed for the model.

    Optional Random seed for the model. Needs to be of type Int.

    Definition Classes
    HasGeneratorProperties
  110. val repetitionPenalty: DoubleParam

    The parameter for repetition penalty (Default: 1.0).

    The parameter for repetition penalty (Default: 1.0). 1.0 means no penalty. See this paper for more details.

    Definition Classes
    HasGeneratorProperties
  111. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  112. def set[T](feature: StructFeature[T], value: T): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  113. def set[K, V](feature: MapFeature[K, V], value: Map[K, V]): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  114. def set[T](feature: SetFeature[T], value: Set[T]): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  115. def set[T](feature: ArrayFeature[T], value: Array[T]): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  116. final def set(paramPair: ParamPair[_]): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  117. final def set(param: String, value: Any): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  118. final def set[T](param: Param[T], value: T): LLAMA3Transformer.this.type
    Definition Classes
    Params
  119. def setAddedTokens(value: Map[String, Int]): LLAMA3Transformer.this.type

  120. def setBatchSize(size: Int): LLAMA3Transformer.this.type

    Size of every batch.

    Size of every batch.

    Definition Classes
    HasBatchedAnnotate
  121. def setBeamSize(beamNum: Int): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  122. def setDefault[T](feature: StructFeature[T], value: () ⇒ T): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  123. def setDefault[K, V](feature: MapFeature[K, V], value: () ⇒ Map[K, V]): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  124. def setDefault[T](feature: SetFeature[T], value: () ⇒ Set[T]): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  125. def setDefault[T](feature: ArrayFeature[T], value: () ⇒ Array[T]): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    HasFeatures
  126. final def setDefault(paramPairs: ParamPair[_]*): LLAMA3Transformer.this.type
    Attributes
    protected
    Definition Classes
    Params
  127. final def setDefault[T](param: Param[T], value: T): LLAMA3Transformer.this.type
    Attributes
    protected[org.apache.spark.ml]
    Definition Classes
    Params
  128. def setDoSample(value: Boolean): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  129. def setGenerationConfig(value: GenerationConfig): LLAMA3Transformer.this.type
  130. def setIgnoreTokenIds(tokenIds: Array[Int]): LLAMA3Transformer.this.type

  131. final def setInputCols(value: String*): LLAMA3Transformer.this.type
    Definition Classes
    HasInputAnnotationCols
  132. def setInputCols(value: Array[String]): LLAMA3Transformer.this.type

    Overrides required annotators column if different than default

    Overrides required annotators column if different than default

    Definition Classes
    HasInputAnnotationCols
  133. def setLazyAnnotator(value: Boolean): LLAMA3Transformer.this.type
    Definition Classes
    CanBeLazy
  134. def setMaxInputLength(value: Int): LLAMA3Transformer.this.type
    Definition Classes
    HasGeneratorProperties
  135. def setMaxOutputLength(value: Int): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  136. def setMerges(value: Map[(String, String), Int]): LLAMA3Transformer.this.type

  137. def setMinOutputLength(value: Int): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  138. def setModelIfNotSet(spark: SparkSession, onnxWrappers: Option[DecoderWrappers], openvinoWrapper: Option[OpenvinoWrapper]): LLAMA3Transformer.this.type

  139. def setNReturnSequences(beamNum: Int): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  140. def setNoRepeatNgramSize(value: Int): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  141. final def setOutputCol(value: String): LLAMA3Transformer.this.type

    Overrides annotation column name when transforming

    Overrides annotation column name when transforming

    Definition Classes
    HasOutputAnnotationCol
  142. def setParent(parent: Estimator[LLAMA3Transformer]): LLAMA3Transformer
    Definition Classes
    Model
  143. def setRandomSeed(value: Int): LLAMA3Transformer.this.type

  144. def setRandomSeed(value: Long): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  145. def setRepetitionPenalty(value: Double): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  146. def setStopTokenIds(value: Array[Int]): LLAMA3Transformer.this.type

    Definition Classes
    LLAMA3Transformer → HasGeneratorProperties
  147. def setTask(value: String): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  148. def setTemperature(value: Double): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  149. def setTopK(value: Int): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  150. def setTopP(value: Double): LLAMA3Transformer.this.type

    Definition Classes
    HasGeneratorProperties
  151. def setVocabulary(value: Map[String, Int]): LLAMA3Transformer.this.type

  152. val stopTokenIds: IntArrayParam

    Stop tokens to terminate the generation

    Stop tokens to terminate the generation

    Definition Classes
    LLAMA3Transformer → HasGeneratorProperties
  153. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  154. val task: Param[String]

    Set transformer task, e.g.

    Set transformer task, e.g. "summarize:" (Default: "").

    Definition Classes
    HasGeneratorProperties
  155. val temperature: DoubleParam

    The value used to module the next token probabilities (Default: 1.0)

    The value used to module the next token probabilities (Default: 1.0)

    Definition Classes
    HasGeneratorProperties
  156. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  157. val topK: IntParam

    The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)

    The number of highest probability vocabulary tokens to keep for top-k-filtering (Default: 50)

    Definition Classes
    HasGeneratorProperties
  158. val topP: DoubleParam

    If set to float < 1.0, only the most probable tokens with probabilities that add up to topP or higher are kept for generation (Default: 1.0)

    If set to float < 1.0, only the most probable tokens with probabilities that add up to topP or higher are kept for generation (Default: 1.0)

    Definition Classes
    HasGeneratorProperties
  159. final def transform(dataset: Dataset[_]): DataFrame

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    Given requirements are met, this applies ML transformation within a Pipeline or stand-alone Output annotation will be generated as a new column, previous annotations are still available separately metadata is built at schema level to record annotations structural information outside its content

    dataset

    Dataset[Row]

    Definition Classes
    AnnotatorModel → Transformer
  160. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  161. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  162. final def transformSchema(schema: StructType): StructType

    requirement for pipeline transformation validation.

    requirement for pipeline transformation validation. It is called on fit()

    Definition Classes
    RawAnnotator → PipelineStage
  163. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  164. val uid: String
    Definition Classes
    LLAMA3Transformer → Identifiable
  165. def validate(schema: StructType): Boolean

    takes a Dataset and checks to see if all the required annotation types are present.

    takes a Dataset and checks to see if all the required annotation types are present.

    schema

    to be validated

    returns

    True if all the required types are present, else false

    Attributes
    protected
    Definition Classes
    RawAnnotator
  166. val vocabulary: MapFeature[String, Int]

    Vocabulary used to encode the words to ids with bpeTokenizer.encode

  167. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  168. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  169. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  170. def wrapColumnMetadata(col: Column): Column
    Attributes
    protected
    Definition Classes
    RawAnnotator
  171. def write: MLWriter
    Definition Classes
    ParamsAndFeaturesWritable → DefaultParamsWritable → MLWritable
  172. def writeOnnxModel(path: String, spark: SparkSession, onnxWrapper: OnnxWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOnnxModel
  173. def writeOnnxModels(path: String, spark: SparkSession, onnxWrappersWithNames: Seq[(OnnxWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOnnxModel
  174. def writeOpenvinoModel(path: String, spark: SparkSession, openvinoWrapper: OpenvinoWrapper, suffix: String, fileName: String): Unit
    Definition Classes
    WriteOpenvinoModel
  175. def writeOpenvinoModels(path: String, spark: SparkSession, ovWrappersWithNames: Seq[(OpenvinoWrapper, String)], suffix: String): Unit
    Definition Classes
    WriteOpenvinoModel

Inherited from HasEngine

Inherited from HasGeneratorProperties

Inherited from WriteOpenvinoModel

Inherited from WriteOnnxModel

Inherited from HasBatchedAnnotate[LLAMA3Transformer]

Inherited from AnnotatorModel[LLAMA3Transformer]

Inherited from CanBeLazy

Inherited from RawAnnotator[LLAMA3Transformer]

Inherited from HasOutputAnnotationCol

Inherited from HasInputAnnotationCols

Inherited from HasOutputAnnotatorType

Inherited from ParamsAndFeaturesWritable

Inherited from HasFeatures

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from Model[LLAMA3Transformer]

Inherited from Transformer

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this annotator can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters