ipex-llm/python/llm/example/CPU/LangChain/low_bit.py
Zhicun b827f534d5
Add tokenizer_id in Langchain (#10588)
* fix low-bit

* fix

* fix style

---------

Co-authored-by: arda <arda@arda-arc12.sh.intel.com>
2024-04-03 14:25:35 +08:00

60 lines
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2.1 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.
#
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
low_bit_model_path = args.target_path
template ="""{question}"""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm = TransformersLLM.from_model_id(
model_id=model_path,
model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True},
)
llm.model.save_low_bit(low_bit_model_path)
del llm
low_bit_llm = TransformersLLM.from_model_id_low_bit(
model_id=low_bit_model_path,
tokenizer_id=model_path,
model_kwargs={"temperature": 0, "max_length": 64, "trust_remote_code": True}
)
llm_chain = LLMChain(prompt=prompt, llm=low_bit_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('-t','--target-path',type=str,required=True,
help='the path to save the low bit model')
parser.add_argument('-q', '--question', type=str, default='What is AI?',
help='qustion you want to ask.')
args = parser.parse_args()
main(args)