Instructions to use realitisoft/ai-bot-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use realitisoft/ai-bot-test with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("automatic-speech-recognition", model="realitisoft/ai-bot-test")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq processor = AutoProcessor.from_pretrained("realitisoft/ai-bot-test") model = AutoModelForSpeechSeq2Seq.from_pretrained("realitisoft/ai-bot-test", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download venv/lib/python3.11/site-packages/huggingface_hub/utils/_cache_assets.py from realitisoft/ai-bot-test: direct link, hf CLI and curl.
- Browser
- Download file 5.73 kB
-
https://huggingface.co/realitisoft/ai-bot-test/resolve/main/venv/lib/python3.11/site-packages/huggingface_hub/utils/_cache_assets.py
- Command line
-
hf download hf://realitisoft/ai-bot-test/venv/lib/python3.11/site-packages/huggingface_hub/utils/_cache_assets.py
-
curl -L -o _cache_assets.py https://huggingface.co/realitisoft/ai-bot-test/resolve/main/venv/lib/python3.11/site-packages/huggingface_hub/utils/_cache_assets.py
5.73 kB
| # coding=utf-8 | |
| # Copyright 2019-present, the HuggingFace Inc. team. | |
| # | |
| # 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. | |
| from pathlib import Path | |
| from typing import Union | |
| from ..constants import HF_ASSETS_CACHE | |
| def cached_assets_path( | |
| library_name: str, | |
| namespace: str = "default", | |
| subfolder: str = "default", | |
| *, | |
| assets_dir: Union[str, Path, None] = None, | |
| ): | |
| """Return a folder path to cache arbitrary files. | |
| `huggingface_hub` provides a canonical folder path to store assets. This is the | |
| recommended way to integrate cache in a downstream library as it will benefit from | |
| the builtins tools to scan and delete the cache properly. | |
| The distinction is made between files cached from the Hub and assets. Files from the | |
| Hub are cached in a git-aware manner and entirely managed by `huggingface_hub`. See | |
| [related documentation](https://huggingface.co/docs/huggingface_hub/how-to-cache). | |
| All other files that a downstream library caches are considered to be "assets" | |
| (files downloaded from external sources, extracted from a .tar archive, preprocessed | |
| for training,...). | |
| Once the folder path is generated, it is guaranteed to exist and to be a directory. | |
| The path is based on 3 levels of depth: the library name, a namespace and a | |
| subfolder. Those 3 levels grants flexibility while allowing `huggingface_hub` to | |
| expect folders when scanning/deleting parts of the assets cache. Within a library, | |
| it is expected that all namespaces share the same subset of subfolder names but this | |
| is not a mandatory rule. The downstream library has then full control on which file | |
| structure to adopt within its cache. Namespace and subfolder are optional (would | |
| default to a `"default/"` subfolder) but library name is mandatory as we want every | |
| downstream library to manage its own cache. | |
| Expected tree: | |
| ```text | |
| assets/ | |
| └── datasets/ | |
| │ ├── SQuAD/ | |
| │ │ ├── downloaded/ | |
| │ │ ├── extracted/ | |
| │ │ └── processed/ | |
| │ ├── Helsinki-NLP--tatoeba_mt/ | |
| │ ├── downloaded/ | |
| │ ├── extracted/ | |
| │ └── processed/ | |
| └── transformers/ | |
| ├── default/ | |
| │ ├── something/ | |
| ├── bert-base-cased/ | |
| │ ├── default/ | |
| │ └── training/ | |
| hub/ | |
| └── models--julien-c--EsperBERTo-small/ | |
| ├── blobs/ | |
| │ ├── (...) | |
| │ ├── (...) | |
| ├── refs/ | |
| │ └── (...) | |
| └── [ 128] snapshots/ | |
| ├── 2439f60ef33a0d46d85da5001d52aeda5b00ce9f/ | |
| │ ├── (...) | |
| └── bbc77c8132af1cc5cf678da3f1ddf2de43606d48/ | |
| └── (...) | |
| ``` | |
| Args: | |
| library_name (`str`): | |
| Name of the library that will manage the cache folder. Example: `"dataset"`. | |
| namespace (`str`, *optional*, defaults to "default"): | |
| Namespace to which the data belongs. Example: `"SQuAD"`. | |
| subfolder (`str`, *optional*, defaults to "default"): | |
| Subfolder in which the data will be stored. Example: `extracted`. | |
| assets_dir (`str`, `Path`, *optional*): | |
| Path to the folder where assets are cached. This must not be the same folder | |
| where Hub files are cached. Defaults to `HF_HOME / "assets"` if not provided. | |
| Can also be set with `HF_ASSETS_CACHE` environment variable. | |
| Returns: | |
| Path to the cache folder (`Path`). | |
| Example: | |
| ```py | |
| >>> from huggingface_hub import cached_assets_path | |
| >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="download") | |
| PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/download') | |
| >>> cached_assets_path(library_name="datasets", namespace="SQuAD", subfolder="extracted") | |
| PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/SQuAD/extracted') | |
| >>> cached_assets_path(library_name="datasets", namespace="Helsinki-NLP/tatoeba_mt") | |
| PosixPath('/home/wauplin/.cache/huggingface/extra/datasets/Helsinki-NLP--tatoeba_mt/default') | |
| >>> cached_assets_path(library_name="datasets", assets_dir="/tmp/tmp123456") | |
| PosixPath('/tmp/tmp123456/datasets/default/default') | |
| ``` | |
| """ | |
| # Resolve assets_dir | |
| if assets_dir is None: | |
| assets_dir = HF_ASSETS_CACHE | |
| assets_dir = Path(assets_dir).expanduser().resolve() | |
| # Avoid names that could create path issues | |
| for part in (" ", "/", "\\"): | |
| library_name = library_name.replace(part, "--") | |
| namespace = namespace.replace(part, "--") | |
| subfolder = subfolder.replace(part, "--") | |
| # Path to subfolder is created | |
| path = assets_dir / library_name / namespace / subfolder | |
| try: | |
| path.mkdir(exist_ok=True, parents=True) | |
| except (FileExistsError, NotADirectoryError): | |
| raise ValueError(f"Corrupted assets folder: cannot create directory because of an existing file ({path}).") | |
| # Return | |
| return path | |