62 lines
		
	
	
	
		
			2.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			62 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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import torch
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import time
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import argparse
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import numpy as np
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from ipex_llm.transformers import AutoModel
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from transformers import AutoTokenizer
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if __name__ == '__main__':
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    parser = argparse.ArgumentParser(description='Stream Chat for GLM-4 model')
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    parser.add_argument('--repo-id-or-model-path', type=str, default="THUDM/glm-4-9b-chat",
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                        help='The huggingface repo id for the GLM-4 model to be downloaded'
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                             ', or the path to the huggingface checkpoint folder')
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    parser.add_argument('--question', type=str, default="晚上睡不着应该怎么办",
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                        help='Qustion you want to ask')
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    parser.add_argument('--disable-stream', action="store_true",
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                        help='Disable stream chat')
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    args = parser.parse_args()
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    model_path = args.repo_id_or_model_path
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    disable_stream = args.disable_stream
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    # Load model in 4 bit,
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    # which convert the relevant layers in the model into INT4 format
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    model = AutoModel.from_pretrained(model_path,
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                                      load_in_4bit=True,
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                                      trust_remote_code=True)
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    # Load tokenizer
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    tokenizer = AutoTokenizer.from_pretrained(model_path,
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                                              trust_remote_code=True)
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    with torch.inference_mode():
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        if disable_stream:
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            # Chat
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            response, history = model.chat(tokenizer, args.question, history=[])
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            print('-'*20, 'Chat Output', '-'*20)
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            print(response)
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        else:
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            # Stream chat
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            response_ = ""
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            print('-'*20, 'Stream Chat Output', '-'*20)
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            for response, history in model.stream_chat(tokenizer, args.question, history=[]):
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                print(response.replace(response_, ""), end="")
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                response_ = response
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