Source code for sparknlp.annotator.seq2seq.qwen_transformer
# Copyright 2017-2022 John Snow Labs
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"""Contains classes for the QwenTransformer."""
from sparknlp.common import *
[docs]class QwenTransformer(AnnotatorModel, HasBatchedAnnotate, HasEngine):
"""Qwen: comprehensive language model series
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model
pretrained on a large amount of data. In comparison with the previous released Qwen, the
improvements include:
6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B; Significant performance improvement
in Chat models; Multilingual support of both base and chat models; Stable support of 32K
context length for models of all sizes
Qwen1.5 is a language model series including decoder language models of different model sizes.
For each size, we release the base language model and the aligned chat model. It is based on
the Transformer architecture with SwiGLU activation, attention QKV bias, group query
attention, mixture of sliding window attention and full attention, etc. Additionally, we have
an improved tokenizer adaptive to multiple natural languages and codes. For the beta version,
temporarily we did not include GQA and the mixture of SWA and full attention.
Pretrained models can be loaded with :meth:`.pretrained` of the companion
object:
>>> qwen = QwenTransformer.pretrained() \\
... .setInputCols(["document"]) \\
... .setOutputCol("generation")
The default model is ``"qwen-13b"``, if no name is provided. For available
pretrained models please see the `Models Hub
<https://sparknlp.org/models?q=qwen>`__.
====================== ======================
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 20
doSample
Whether or not to use sampling; use greedy decoding otherwise, by default False
temperature
The value used to module the next token probabilities, by default 1.0
topK
The number of highest probability vocabulary tokens to keep for
top-k-filtering, by default 50
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
sequence. The use of an accelerator such as GPU is recommended.
References
----------
- `Qwen Technical Report
<https://arxiv.org/pdf/2309.16609.pdf>`__
- https://qwenlm.github.io/blog/qwen1.5/
- https://github.com/QwenLM/Qwen1.5
**Paper Abstract:**
*Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to
humans. In this work, we introduce Qwen, the first installment of our large language model
series. Qwen is a comprehensive language model series that encompasses distinct models with
varying parameter counts. It includes Qwen, the base pretrained language models, and
Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models
consistently demonstrate superior performance across a multitude of downstream tasks, and the
chat models, particularly those trained using Reinforcement Learning from Human Feedback
(RLHF), are highly competitive. The chat models possess advanced tool-use and planning
capabilities for creating agent applications, showcasing impressive performance even when
compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we
have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as
mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These
models demonstrate significantly improved performance in comparison with open-source models,
and slightly fall behind the proprietary models.*
Examples
--------
>>> import sparknlp
>>> from sparknlp.base import *
>>> from sparknlp.annotator import *
>>> from pyspark.ml import Pipeline
>>> documentAssembler = DocumentAssembler() \\
... .setInputCol("text") \\
... .setOutputCol("documents")
>>> qwen = QwenTransformer.pretrained("qwen-7b") \\
... .setInputCols(["documents"]) \\
... .setMaxOutputLength(50) \\
... .setOutputCol("generation")
>>> pipeline = Pipeline().setStages([documentAssembler, qwen])
>>> data = spark.createDataFrame([["My name is Leonardo."]]).toDF("text")
>>> result = pipeline.fit(data).transform(data)
>>> result.select("summaries.generation").show(truncate=False)
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|result |
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
|[My name is Leonardo . I am a student of the University of California, Berkeley. I am interested in the field of Artificial Intelligence and its applications in the real world. I have a strong |
| passion for learning and am always looking for ways to improve my knowledge and skills] |
-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
"""
name = "QwenTransformer"
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)
[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)
@keyword_only
def __init__(self, classname="com.johnsnowlabs.nlp.annotators.seq2seq.QwenTransformer", java_model=None):
super(QwenTransformer, self).__init__(classname=classname, java_model=java_model)
self._setDefault(minOutputLength=0, maxOutputLength=50, doSample=False, temperature=0.6, topK=50, topP=0.9,
repetitionPenalty=1.0, noRepeatNgramSize=0, ignoreTokenIds=[], batchSize=1)
@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
-------
QwenTransformer
The restored model
"""
from sparknlp.internal import _QwenLoader
jModel = _QwenLoader(folder, spark_session._jsparkSession, use_openvino)._java_obj
return QwenTransformer(java_model=jModel)
@staticmethod
[docs] def pretrained(name="qwen-7b", lang="en", remote_loc=None):
"""Downloads and loads a pretrained model.
Parameters
----------
name : str, optional
Name of the pretrained model, by default "qwen-7b"
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
-------
QwenTransformer
The restored model
"""
from sparknlp.pretrained import ResourceDownloader
return ResourceDownloader.downloadModel(QwenTransformer, name, lang, remote_loc)