c2efa264d9
* Update LangChain examples to use upstream * Update README and fix links * Update LangChain CPU examples to use upstream * Update LangChain CPU voice_assistant example * Update CPU README * Update GPU README * Remove GPU Langchain vLLM example and fix comments * Change langchain -> LangChain * Add reference for both upstream llms and embeddings * Fix comments * Fix comments * Fix comments * Fix comments * Fix comment
63 lines
No EOL
2 KiB
Python
63 lines
No EOL
2 KiB
Python
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#
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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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# Code is adapted from https://python.langchain.com/docs/modules/chains/additional/llm_math
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import argparse
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import warnings
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from langchain.chains import LLMMathChain
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from langchain_community.llms import IpexLLM
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warnings.filterwarnings("ignore", category=UserWarning, message=".*padding_mask.*")
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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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llm = IpexLLM.from_model_id(
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model_id=model_path,
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model_kwargs={
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"temperature": 0,
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"max_length": 1024,
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"trust_remote_code": True,
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},
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)
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llm_math = LLMMathChain.from_llm(llm, verbose=True)
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output = llm_math.run(question)
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print("====output=====")
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print(output)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='TransformersLLM Langchain Math Example')
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parser.add_argument('-m','--model-path', type=str, required=True,
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help='the path to transformers model')
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parser.add_argument('-q', '--question', type=str, default='What is 13 raised to the .3432 power?',
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help='qustion you want to ask.')
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args = parser.parse_args()
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main(args) |