9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
64 lines
2.5 KiB
Python
64 lines
2.5 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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from transformers import AutoTokenizer, AutoModelForCausalLM
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from transformers.generation import GenerationConfig
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import torch
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import time
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import os
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import argparse
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from ipex_llm import optimize_model
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for InternLM-XComposer model')
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parser.add_argument('--repo-id-or-model-path', type=str, default="internlm/internlm-xcomposer-vl-7b",
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help='The huggingface repo id for the InternLM-XComposer model to be downloaded'
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', or the path to the huggingface checkpoint folder')
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parser.add_argument('--image-path', type=str, required=True,
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help='Image path for the input image that the chat will focus on')
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parser.add_argument('--n-predict', type=int, default=512, help='Max tokens to predict')
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args = parser.parse_args()
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model_path = args.repo_id_or_model_path
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image = args.image_path
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# Load model
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model = AutoModelForCausalLM.from_pretrained(model_path, device='cpu', trust_remote_code=True)
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# With only one line to enable BigDL-LLM optimization on model
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# For successful BigDL-LLM optimization on InternLM-XComposer, skip the 'qkv' module during optimization
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model = optimize_model(model,
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low_bit='sym_int4',
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modules_to_not_convert=['qkv'])
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model.tokenizer = tokenizer
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history = None
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while True:
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try:
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user_input = input("User: ")
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except EOFError:
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user_input = ""
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if not user_input:
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print("exit...")
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break
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response, history = model.chat(text=user_input, image=image , history = history)
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print(f'Bot: {response}', end="")
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image = None
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