Feature Extraction
Transformers
Safetensors
vila
omni-modal
multimodal
vision
audio
video
llm
custom_code
Eval Results (legacy)
Instructions to use nvidia/omnivinci with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nvidia/omnivinci with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nvidia/omnivinci", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("nvidia/omnivinci", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| # SPDX-FileCopyrightText: Copyright (c) 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. | |
| import os | |
| import warnings | |
| from typing import Any, List, Optional | |
| from torch import distributed as dist | |
| __all__ = [ | |
| "init", | |
| "is_initialized", | |
| "size", | |
| "rank", | |
| "local_size", | |
| "local_rank", | |
| "is_main", | |
| "barrier", | |
| "gather", | |
| "all_gather", | |
| ] | |
| def init() -> None: | |
| if "RANK" not in os.environ: | |
| warnings.warn("Environment variable `RANK` is not set. Skipping distributed initialization.") | |
| return | |
| dist.init_process_group(backend="nccl", init_method="env://") | |
| def is_initialized() -> bool: | |
| return dist.is_initialized() | |
| def size() -> int: | |
| return int(os.environ.get("WORLD_SIZE", 1)) | |
| def rank() -> int: | |
| return int(os.environ.get("RANK", 0)) | |
| def local_size() -> int: | |
| return int(os.environ.get("LOCAL_WORLD_SIZE", 1)) | |
| def local_rank() -> int: | |
| return int(os.environ.get("LOCAL_RANK", 0)) | |
| def is_main() -> bool: | |
| return rank() == 0 | |
| def barrier() -> None: | |
| dist.barrier() | |
| def gather(obj: Any, dst: int = 0) -> Optional[List[Any]]: | |
| if not is_initialized(): | |
| return [obj] | |
| if is_main(): | |
| objs = [None for _ in range(size())] | |
| dist.gather_object(obj, objs, dst=dst) | |
| return objs | |
| else: | |
| dist.gather_object(obj, dst=dst) | |
| return None | |
| def all_gather(obj: Any) -> List[Any]: | |
| if not is_initialized(): | |
| return [obj] | |
| objs = [None for _ in range(size())] | |
| dist.all_gather_object(objs, obj) | |
| return objs | |