9df70d95eb
* Rename bigdl/llm to ipex_llm * rm python/llm/src/bigdl * from bigdl.llm to from ipex_llm
61 lines
2.4 KiB
Python
61 lines
2.4 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.
|
|
#
|
|
|
|
from ipex_llm.transformers import AutoModelForCausalLM
|
|
from transformers import AutoTokenizer
|
|
from transformers.generation import GenerationConfig
|
|
import torch
|
|
import time
|
|
import os
|
|
import argparse
|
|
from ipex_llm import optimize_model
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='Predict Tokens using `chat()` API for InternLM-XComposer model')
|
|
parser.add_argument('--repo-id-or-model-path', type=str, default="internlm/internlm-xcomposer-vl-7b",
|
|
help='The huggingface repo id for the InternLM-XComposer model to be downloaded'
|
|
', or the path to the huggingface checkpoint folder')
|
|
parser.add_argument('--image-path', type=str, required=True,
|
|
help='Image path for the input image that the chat will focus on')
|
|
parser.add_argument('--n-predict', type=int, default=512, help='Max tokens to predict')
|
|
|
|
args = parser.parse_args()
|
|
model_path = args.repo_id_or_model_path
|
|
image = args.image_path
|
|
|
|
# Load model
|
|
# For successful BigDL-LLM optimization on InternLM-XComposer, skip the 'qkv' module during optimization
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, device='cpu', load_in_4bit=True,
|
|
trust_remote_code=True, modules_to_not_convert=['qkv'])
|
|
|
|
# Load tokenizer
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
|
model.tokenizer = tokenizer
|
|
|
|
history = None
|
|
while True:
|
|
try:
|
|
user_input = input("User: ")
|
|
except EOFError:
|
|
user_input = ""
|
|
if not user_input:
|
|
print("exit...")
|
|
break
|
|
|
|
response, history = model.chat(text=user_input, image=image , history = history)
|
|
print(f'Bot: {response}', end="")
|
|
image = None
|
|
|