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https://github.com/NVIDIA/Model-Optimizer.git
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49 lines
1.7 KiB
Python
49 lines
1.7 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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"""Export a HF checkpoint (with ModelOpt state) for deployment."""
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import argparse
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import torch
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from transformers import AutoModelForCausalLM
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import modelopt.torch.opt as mto
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from modelopt.torch.export import export_hf_checkpoint
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Export a HF checkpoint (with ModelOpt state) for deployment."
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)
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parser.add_argument("--model_path", type=str, default="Path of the trained checkpoint.")
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parser.add_argument(
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"--export_path", type=str, default="Destination directory for exported files."
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)
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return parser.parse_args()
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mto.enable_huggingface_checkpointing()
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args = parse_args()
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model = AutoModelForCausalLM.from_pretrained(args.model_path, torch_dtype="auto")
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model.eval()
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with torch.inference_mode():
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export_hf_checkpoint(
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model, # The quantized model.
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export_dir=args.export_path, # The directory where the exported files will be stored.
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)
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print(f"Exported checkpoint to {args.export_path}")
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