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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# 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.
import argparse
from pathlib import Path
from nemo.collections import vlm
from nemo.collections.llm import import_ckpt
HF_MODEL_ID_TO_NEMO_CLASS = {
"llava-hf/llava-1.5-7b-hf": vlm.LlavaModel,
"llava-hf/llava-1.5-13b-hf": vlm.LlavaModel,
"meta-llama/Llama-3.2-11B-Vision": vlm.MLlamaModel,
"meta-llama/Llama-3.2-90B-Vision": vlm.MLlamaModel,
"meta-llama/Llama-3.2-11B-Vision-Instruct": vlm.MLlamaModel,
"meta-llama/Llama-3.2-90B-Vision-Instruct": vlm.MLlamaModel,
"OpenGVLab/InternViT-300M-448px-V2_5": vlm.InternViTModel,
"google/siglip-base-patch16-224": vlm.SigLIPViTModel,
"OpenGVLab/InternViT-6B-448px-V2_5": vlm.InternViTModel,
"openai/clip-vit-large-patch14": vlm.CLIPViTModel,
}
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Import NeMo checkpoint from Hugging Face format.")
parser.add_argument(
"--input_name_or_path",
type=str,
required=True,
help="Hugging Face model id or path to the Hugging Face checkpoint directory.",
)
parser.add_argument(
"--output_path",
type=str,
default=None,
help="Path to save the converted NeMo version Hugging Face checkpoint directory.",
)
parser.add_argument(
"--nemo_class",
type=str,
default=None,
help="If input is a local checkpoint path, specify the corresponding NeMo model class (e.g., 'vlm.LlavaModel').",
)
args = parser.parse_args()
model_name_or_path = args.input_name_or_path
local_path = Path(model_name_or_path)
if local_path.exists():
try:
model_class = eval(args.nemo_class)
except Exception as e:
raise ValueError(f"Could not import the specified NeMo class '{args.nemo_class}': {e}")
else:
model_class = HF_MODEL_ID_TO_NEMO_CLASS[model_name_or_path]
import_ckpt(model_class(), f"hf://{model_name_or_path}", output_path=args.output_path)
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