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allenai / cached_path

A file utility for accessing both local and remote files through a unified interface.
https://cached-path.readthedocs.io/
Apache License 2.0
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Update huggingface-hub requirement from <0.13.0,>=0.8.1 to >=0.8.1,<0.17.0 #175

Closed dependabot[bot] closed 1 year ago

dependabot[bot] commented 1 year ago

Updates the requirements on huggingface-hub to permit the latest version.

Release notes

Sourced from huggingface-hub's releases.

v0.16.2: Inference, CommitScheduler and Tensorboard

Inference

Introduced in the v0.15 release, the InferenceClient got a big update in this one. The client is now reaching a stable point in terms of features. The next updates will be focused on continuing to add support for new tasks.

Async client

Asyncio calls are supported thanks to AsyncInferenceClient. Based on asyncio and aiohttp, it allows you to make efficient concurrent calls to the Inference endpoint of your choice. Every task supported by InferenceClient is supported in its async version. Method inputs and outputs and logic are strictly the same, except that you must await the coroutine.

>>> from huggingface_hub import AsyncInferenceClient
>>> client = AsyncInferenceClient()

>>> image = await client.text_to_image("An astronaut riding a horse on the moon.")

Text-generation

Support for text-generation task has been added. It is focused on fully supporting endpoints running on the text-generation-inference framework. In fact, the code is heavily inspired by TGI's Python client initially implemented by @​OlivierDehaene.

Text generation has 4 modes depending on details (bool) and stream (bool) values. By default, a raw string is returned. If details=True, more information about the generated tokens is returned. If stream=True, generated tokens are returned one by one as soon as the server generated them. For more information, check out the documentation.

>>> from huggingface_hub import InferenceClient
>>> client = InferenceClient()

stream=False, details=False


>>> client.text_generation("The huggingface_hub library is ", max_new_tokens=12)
'100% open source and built to be easy to use.'


stream=True, details=True


>>> for details in client.text_generation("The huggingface_hub library is ", max_new_tokens=12, details=True, stream=True):
>>>     print(details)
TextGenerationStreamResponse(token=Token(id=1425, text='100', logprob=-1.0175781, special=False), generated_text=None, details=None)
...
TextGenerationStreamResponse(token=Token(
id=25,
text='.',
logprob=-0.5703125,
special=False),
generated_text='100% open source and built to be easy to use.',
details=StreamDetails(finish_reason=<FinishReason.Length: 'length'>, generated_tokens=12, seed=None)
)

Of course, the async client also supports text-generation (see docs):

</tr></table>

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