Instructions to use jonl521/modeling_simple with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jonl521/modeling_simple with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jonl521/modeling_simple", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .venv/lib/python3.14/site-packages/torch/distributed/_checkpointable.py from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 1.31 kB
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/distributed/_checkpointable.py
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/torch/distributed/_checkpointable.py
-
curl -L -o _checkpointable.py https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/distributed/_checkpointable.py
1.31 kB
| # Copyright (c) Meta Platforms, Inc. and affiliates | |
| from typing_extensions import Protocol, runtime_checkable | |
| import torch | |
| class _Checkpointable(Protocol): # noqa: PYI046 | |
| """ | |
| Interface for checkpointable objects. | |
| Implemented as a protocol, implicit subtyping is supported so subclasses do not need to inherit this explicitly. | |
| This is to allow arbitrary objects/tensor subclasses to hook into DCP seamlessly through implementing the interface. | |
| """ | |
| def __create_write_items__(self, fqn: str, object: object) -> list[object]: | |
| """ | |
| Return a list of WriteItems based on object's contents. | |
| """ | |
| raise NotImplementedError( | |
| "_Checkpointable._create_write_items is not implemented" | |
| ) | |
| def __create_chunk_list__(self) -> list[object]: | |
| """ | |
| Return a list of `ChunkStorageMetadata` based on object's contents. | |
| """ | |
| raise NotImplementedError( | |
| "_Checkpointable._create_chunk_list is not implemented" | |
| ) | |
| def __get_tensor_shard__(self, index: int) -> torch.Tensor: | |
| """ | |
| Return a 'torch.Tensor' shard based on 'MetadataIndex'. | |
| """ | |
| raise NotImplementedError( | |
| "_Checkpointable._get_tensor_shard is not implemented" | |
| ) | |