ipex-llm/python/llm/example/GPU/LangChain/chat.py
hxsz1997 d86477f14d
Remove native_int4 in LangChain examples (#10510)
* rebase the modify to ipex-llm

* modify the typo
2024-03-27 17:48:16 +08:00

65 lines
2.2 KiB
Python

#
# Copyright 2016 The BigDL Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This would makes sure Python is aware there is more than one sub-package within bigdl,
# physically located elsewhere.
# Otherwise there would be module not found error in non-pip's setting as Python would
# only search the first bigdl package and end up finding only one sub-package.
import argparse
from ipex_llm.langchain.llms import TransformersLLM, TransformersPipelineLLM
from langchain import PromptTemplate, LLMChain
from langchain import HuggingFacePipeline
def main(args):
question = args.question
model_path = args.model_path
template ="""{question}"""
prompt = PromptTemplate(template=template, input_variables=["question"])
# llm = TransformersPipelineLLM.from_model_id(
# model_id=model_path,
# task="text-generation",
# model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
# device_map='xpu'
# )
llm = TransformersLLM.from_model_id(
model_id=model_path,
model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
device_map='xpu'
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
output = llm_chain.run(question)
print("====output=====")
print(output)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='TransformersLLM Langchain Chat Example')
parser.add_argument('-m','--model-path', type=str, required=True,
help='the path to transformers model')
parser.add_argument('-q', '--question', type=str, default='What is AI?',
help='qustion you want to ask.')
args = parser.parse_args()
main(args)