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/include/kineto/ThreadUtil.h from jonl521/modeling_simple: direct link, hf CLI and curl.
- Browser
- Download file 925 Bytes
-
https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/include/kineto/ThreadUtil.h
- Command line
-
hf download hf://jonl521/modeling_simple/.venv/lib/python3.14/site-packages/torch/include/kineto/ThreadUtil.h
-
curl -L -o ThreadUtil.h https://huggingface.co/jonl521/modeling_simple/resolve/main/.venv/lib/python3.14/site-packages/torch/include/kineto/ThreadUtil.h
925 Bytes
| /* | |
| * Copyright (c) Meta Platforms, Inc. and affiliates. | |
| * All rights reserved. | |
| * | |
| * This source code is licensed under the BSD-style license found in the | |
| * LICENSE file in the root directory of this source tree. | |
| */ | |
| namespace libkineto { | |
| int32_t systemThreadId(bool cache = true); | |
| int32_t threadId(); | |
| bool setThreadName(const std::string& name); | |
| std::string getThreadName(); | |
| int32_t pidNamespace(ino_t& ns); | |
| int32_t processId(bool cache = true); | |
| std::string processName(int32_t pid); | |
| // Return a list of pids and process names for the current process | |
| // and its parents. | |
| std::vector<std::pair<int32_t, std::string>> pidCommandPairsOfAncestors(); | |
| // Resets all cached Thread local state, this must be done on | |
| // forks to prevent stale values from being retained. | |
| void resetTLS(); | |
| } // namespace libkineto | |