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
73 lines
No EOL
2.3 KiB
Python
73 lines
No EOL
2.3 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
|
|
import warnings
|
|
|
|
from langchain.chains import LLMChain
|
|
from langchain_community.llms import IpexLLM
|
|
from langchain_core.prompts import PromptTemplate
|
|
|
|
warnings.filterwarnings("ignore", category=UserWarning, message=".*padding_mask.*")
|
|
|
|
|
|
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 = IpexLLM.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
|
|
llm_lowbit = IpexLLM.from_model_id_low_bit(
|
|
model_id=low_bit_model_path,
|
|
tokenizer_id=model_path,
|
|
# tokenizer_name=saved_lowbit_model_path, # copy the tokenizers to saved path if you want to use it this way
|
|
model_kwargs={
|
|
"temperature": 0,
|
|
"max_length": 64,
|
|
"trust_remote_code": True,
|
|
},
|
|
)
|
|
llm_chain = prompt | llm_lowbit
|
|
|
|
output = llm_chain.invoke(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) |