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