02c583144c
* langchain intergrations and examples * add licences and rename * add licences * fix license issues and change backbone to model_family * update examples to use model_family param * fix linting * fix code style * exclude langchain integration from stylecheck * update langchain examples and update integrations based on latets changes * update simple llama-cpp-python style API example * remove bloom in README * change default n_threads to 2 and remove redundant code --------- Co-authored-by: leonardozcm <changmin.zhao@intel.com>
70 lines
2.5 KiB
Python
70 lines
2.5 KiB
Python
#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# This would makes sure Python is aware there is more than one sub-package within bigdl,
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# physically located elsewhere.
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# Otherwise there would be module not found error in non-pip's setting as Python would
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# only search the first bigdl package and end up finding only one sub-package.
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import argparse
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from bigdl.llm.langchain.llms import BigdlLLM
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from langchain import PromptTemplate, LLMChain
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from langchain.callbacks.manager import CallbackManager
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from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
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def main(args):
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question = args.question
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model_path = args.model_path
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model_family = args.model_family
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n_threads = args.thread_num
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template ="""{question}"""
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prompt = PromptTemplate(template=template, input_variables=["question"])
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# Callbacks support token-wise streaming
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callback_manager = CallbackManager([StreamingStdOutCallbackHandler()])
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# Verbose is required to pass to the callback manager
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llm = BigdlLLM(
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model_path=model_path,
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model_family=model_family,
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n_threads=n_threads,
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callback_manager=callback_manager,
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verbose=True
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)
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llm_chain = LLMChain(prompt=prompt, llm=llm)
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llm_chain.run(question)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Llama-CPP-Python style API Simple Example')
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parser.add_argument('-x','--model-family', type=str, required=True,
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help='the model family')
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parser.add_argument('-m','--model-path', type=str, required=True,
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help='the path to the converted llm model')
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parser.add_argument('-q', '--question', type=str, default='What is AI?',
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help='qustion you want to ask.')
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parser.add_argument('-t','--thread-num', type=int, default=2,
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help='Number of threads to use for inference')
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args = parser.parse_args()
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main(args)
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