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
75 lines
No EOL
2.4 KiB
Python
75 lines
No EOL
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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import argparse
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import warnings
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from langchain.chains import LLMChain
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from langchain_community.llms import IpexLLM
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from langchain_core.prompts import PromptTemplate
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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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low_bit_model_path = args.target_path
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template ="""{question}"""
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prompt = PromptTemplate(template=template, input_variables=["question"])
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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": 64,
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"trust_remote_code": True,
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"device": "xpu",
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},
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)
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llm.model.save_low_bit(low_bit_model_path)
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del llm
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llm_lowbit = IpexLLM.from_model_id_low_bit(
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model_id=low_bit_model_path,
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tokenizer_id=model_path,
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# tokenizer_name=saved_lowbit_model_path, # copy the tokenizers to saved path if you want to use it this way
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model_kwargs={
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"temperature": 0,
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"max_length": 64,
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"trust_remote_code": True,
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"device": "xpu",
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},
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)
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llm_chain = prompt | llm_lowbit
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output = llm_chain.invoke(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('-t','--target-path',type=str,required=True,
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help='the path to save the low bit 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) |