67 lines
2.4 KiB
Python
67 lines
2.4 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 ipex_llm.langchain.llms import TransformersLLM, TransformersPipelineLLM
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from langchain import PromptTemplate, LLMChain
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from langchain import HuggingFacePipeline
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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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# Below is the prompt format for LLaMa-2 according to
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# https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
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# If you're using a different language model,
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# please adjust the template according to its own model card.
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template = """<s>[INST] <<SYS>>\n \n<</SYS>>\n\n{question} [/INST]"""
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prompt = PromptTemplate(template=template, input_variables=["question"])
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# llm = TransformersPipelineLLM.from_model_id(
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# model_id=model_path,
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# task="text-generation",
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# model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
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# )
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llm = TransformersLLM.from_model_id(
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model_id=model_path,
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model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
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)
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llm_chain = LLMChain(prompt=prompt, llm=llm)
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output = llm_chain.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 Chat 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 AI?',
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help='qustion you want to ask.')
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args = parser.parse_args()
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main(args)
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