fa261b8af1
* torch 2.3 inference docker * Update README.md * add convert code * rename image * remove 2.1 and add graph example * Update README.md
57 lines
2.1 KiB
Python
57 lines
2.1 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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# This is modified from https://github.com/intel-sandbox/customer-ai-test-code/blob/main/convert-model-textgen-to-classfication.py
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#
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import torch
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from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoConfig, AutoModelForCausalLM
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import argparse
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parser = argparse.ArgumentParser(description='Process some integers.')
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parser.add_argument('--model_path', type=str, help='an string for the device')
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args = parser.parse_args()
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model_path = args.model_path
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dtype=torch.bfloat16
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num_labels = 5
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model_name=model_path
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save_directory = model_name + "-classification"
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# Initialize the tokenizer
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# Need padding from the left and padding to 1024
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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# tokenizer.padding_side = "left"
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tokenizer.pad_token = tokenizer.eos_token
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tokenizer.save_pretrained(save_directory)
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model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=dtype, pad_token_id=tokenizer.eos_token_id,)
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config = AutoConfig.from_pretrained(model_name)
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print("text gen model")
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print(model)
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print(config)
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model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=num_labels, torch_dtype=dtype)
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save_directory = model_name + "-classification"
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model.save_pretrained(save_directory)
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model = AutoModelForSequenceClassification.from_pretrained(save_directory, torch_dtype=dtype, pad_token_id=tokenizer.eos_token_id)
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config = AutoConfig.from_pretrained(save_directory)
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print("text classification model")
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print(model)
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print(config)
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