Source code for sparknlp.annotator.seq2seq.llama3_transformer
# Copyright 2017-2022 John Snow Labs
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"""Contains classes for the LLAMA3Transformer."""
from sparknlp.common import *
[docs]class LLAMA3Transformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""Llama 3: Cutting-Edge Foundation and Fine-Tuned Chat Models
The Llama 3 release introduces a new family of pretrained and fine-tuned LLMs, ranging in scale
from 8B and 70B parameters. Llama 3 models are designed with enhanced
efficiency, performance, and safety, making them more capable than previous versions. These models
are trained on a more diverse and expansive dataset, offering improved contextual understanding
and generation quality.
The fine-tuned models, known as Llama 3-instruct, are optimized for dialogue applications using an advanced
version of Reinforcement Learning from Human Feedback (RLHF). Llama 3-instruct models demonstrate superior
performance across multiple benchmarks, outperforming Llama 2 and even matching or exceeding the capabilities
of some closed-source models.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> llama3 = LLAMA3Transformer.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("generation")
The default model is ``"llama3-7b"``, if no name is provided. For available
pretrained models please see the `Models Hub
<https://sparknlp.org/models?q=llama3>`__.
====================== ======================
Input Annotation types Output Annotation type
====================== ======================
``DOCUMENT`` ``DOCUMENT``
====================== ======================
Parameters
----------
configProtoBytes
ConfigProto from tensorflow, serialized into byte array.
minOutputLength
Minimum length of the sequence to be generated, by default 0
maxOutputLength
Maximum length of output text, by default 60
doSample
Whether or not to use sampling; use greedy decoding otherwise, by default False
temperature
The value used to modulate the next token probabilities, by default 1.0
topK
The number of highest probability vocabulary tokens to keep for
top-k-filtering, by default 40
topP
Top cumulative probability for vocabulary tokens, by default 1.0
If set to float < 1, only the most probable tokens with probabilities
that add up to ``topP`` or higher are kept for generation.
repetitionPenalty
The parameter for repetition penalty, 1.0 means no penalty. , by default
1.0
noRepeatNgramSize
If set to int > 0, all ngrams of that size can only occur once, by
default 0
ignoreTokenIds
A list of token ids which are ignored in the decoder's output, by
default []
Notes
-----
This is a very computationally expensive module, especially on larger
sequences. The use of an accelerator such as GPU is recommended.
References
----------
- `Llama 3: Cutting-Edge Foundation and Fine-Tuned Chat Models
<https://ai.meta.com/blog/meta-llama-3/>`__
- https://github.com/facebookresearch/llama
**Paper Abstract:**
*Llama 3 is the latest iteration of large language models from Meta, offering a range of models
from 1 billion to 70 billion parameters. The fine-tuned versions, known as Llama 3-Chat, are
specifically designed for dialogue applications and have been optimized using advanced techniques
such as RLHF. Llama 3 models show remarkable improvements in both safety and performance, making
them a leading choice in both open-source and commercial settings. Our comprehensive approach to
training and fine-tuning these models is aimed at encouraging responsible AI development and fostering
community collaboration.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("documents")
>>> llama3 = LLAMA3Transformer.pretrained("llama_3_7b_chat_hf_int8") \\
... .setInputCols(["documents"]) \\
... .setMaxOutputLength(60) \\
... .setOutputCol("generation")
>>> pipeline = Pipeline().setStages([documentAssembler, llama3])
>>> data = spark.createDataFrame([
... (
... 1,
... "<|start_header_id|>system<|end_header_id|> \\n"+ \
... "You are a minion chatbot who always responds in minion speak! \\n" + \
... "<|start_header_id|>user<|end_header_id|> \\n" + \
... "Who are you? \\n" + \
... "<|start_header_id|>assistant<|end_header_id|> \\n"
... )
... ]).toDF("id", "text")
>>> 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!]|
+------------------------------------------------+
"""
name = "LLAMA3Transformer"
inputAnnotatorTypes = [AnnotatorType.DOCUMENT]
outputAnnotatorType = AnnotatorType.DOCUMENT
configProtoBytes = Param(Params._dummy(),
"configProtoBytes",
"ConfigProto from tensorflow, serialized into byte array. Get with config_proto.SerializeToString()",
TypeConverters.toListInt)
minOutputLength = Param(Params._dummy(), "minOutputLength", "Minimum length of the sequence to be generated",
typeConverter=TypeConverters.toInt)
maxOutputLength = Param(Params._dummy(), "maxOutputLength", "Maximum length of output text",
typeConverter=TypeConverters.toInt)
doSample = Param(Params._dummy(), "doSample", "Whether or not to use sampling; use greedy decoding otherwise",
typeConverter=TypeConverters.toBoolean)
temperature = Param(Params._dummy(), "temperature", "The value used to module the next token probabilities",
typeConverter=TypeConverters.toFloat)
topK = Param(Params._dummy(), "topK",
"The number of highest probability vocabulary tokens to keep for top-k-filtering",
typeConverter=TypeConverters.toInt)
topP = Param(Params._dummy(), "topP",
"If set to float < 1, only the most probable tokens with probabilities that add up to ``top_p`` or higher are kept for generation",
typeConverter=TypeConverters.toFloat)
repetitionPenalty = Param(Params._dummy(), "repetitionPenalty",
"The parameter for repetition penalty. 1.0 means no penalty. See `this paper <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details",
typeConverter=TypeConverters.toFloat)
noRepeatNgramSize = Param(Params._dummy(), "noRepeatNgramSize",
"If set to int > 0, all ngrams of that size can only occur once",
typeConverter=TypeConverters.toInt)
ignoreTokenIds = Param(Params._dummy(), "ignoreTokenIds",
"A list of token ids which are ignored in the decoder's output",
typeConverter=TypeConverters.toListInt)
beamSize = Param(Params._dummy(), "beamSize",
"The number of beams to use for beam search",
typeConverter=TypeConverters.toInt)
stopTokenIds = Param(Params._dummy(), "stopTokenIds",
"A list of token ids which are considered as stop tokens in the decoder's output",
typeConverter=TypeConverters.toListInt)
[docs] def setIgnoreTokenIds(self, value):
"""A list of token ids which are ignored in the decoder's output.
Parameters
----------
value : List[int]
The words to be filtered out
"""
return self._set(ignoreTokenIds=value)
[docs] def setConfigProtoBytes(self, b):
"""Sets configProto from tensorflow, serialized into byte array.
Parameters
----------
b : List[int]
ConfigProto from tensorflow, serialized into byte array
"""
return self._set(configProtoBytes=b)
[docs] def setMinOutputLength(self, value):
"""Sets minimum length of the sequence to be generated.
Parameters
----------
value : int
Minimum length of the sequence to be generated
"""
return self._set(minOutputLength=value)
[docs] def setMaxOutputLength(self, value):
"""Sets maximum length of output text.
Parameters
----------
value : int
Maximum length of output text
"""
return self._set(maxOutputLength=value)
[docs] def setDoSample(self, value):
"""Sets whether or not to use sampling, use greedy decoding otherwise.
Parameters
----------
value : bool
Whether or not to use sampling; use greedy decoding otherwise
"""
return self._set(doSample=value)
[docs] def setTemperature(self, value):
"""Sets the value used to module the next token probabilities.
Parameters
----------
value : float
The value used to module the next token probabilities
"""
return self._set(temperature=value)
[docs] def setTopK(self, value):
"""Sets the number of highest probability vocabulary tokens to keep for
top-k-filtering.
Parameters
----------
value : int
Number of highest probability vocabulary tokens to keep
"""
return self._set(topK=value)
[docs] def setTopP(self, value):
"""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
----------
value : float
Cumulative probability for vocabulary tokens
"""
return self._set(topP=value)
[docs] def setRepetitionPenalty(self, value):
"""Sets the parameter for repetition penalty. 1.0 means no penalty.
Parameters
----------
value : float
The repetition penalty
References
----------
See `Ctrl: A Conditional Transformer Language Model For Controllable
Generation <https://arxiv.org/pdf/1909.05858.pdf>`__ for more details.
"""
return self._set(repetitionPenalty=value)
[docs] def setNoRepeatNgramSize(self, value):
"""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
----------
value : int
N-gram size can only occur once
"""
return self._set(noRepeatNgramSize=value)
[docs] def setBeamSize(self, value):
"""Sets the number of beams to use for beam search.
Parameters
----------
value : int
The number of beams to use for beam search
"""
return self._set(beamSize=value)
[docs] def setStopTokenIds(self, value):
"""Sets a list of token ids which are considered as stop tokens in the decoder's output.
Parameters
----------
value : List[int]
The words to be considered as stop tokens
"""
return self._set(stopTokenIds=value)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.LLAMA3Transformer", java_model=None):
super(LLAMA3Transformer, self).__init__(
classname=classname,
java_model=java_model
)
self._setDefault(
minOutputLength=0,
maxOutputLength=20,
doSample=False,
temperature=0.6,
topK=-1,
topP=0.9,
repetitionPenalty=1.0,
noRepeatNgramSize=3,
ignoreTokenIds=[],
batchSize=1,
beamSize=1,
stopTokenIds=[128001,]
)
@staticmethod
[docs] def loadSavedModel(folder, spark_session, use_openvino = False):
"""Loads a locally saved model.
Parameters
----------
folder : str
Folder of the saved model
spark_session : pyspark.sql.SparkSession
The current SparkSession
Returns
-------
LLAMA3Transformer
The restored model
"""
from sparknlp.internal import _LLAMA3Loader
jModel = _LLAMA3Loader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return LLAMA3Transformer(java_model=jModel)
@staticmethod
[docs] def pretrained(name="llama_3_7b_chat_hf_int4", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "llama_2_7b_chat_hf_int4"
lang : str, optional
Language of the pretrained model, by default "en"
remote_loc : str, optional
Optional remote address of the resource, by default None. Will use
Spark NLPs repositories otherwise.
Returns
-------
LLAMA3Transformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(LLAMA3Transformer, name, lang, remote_loc)