Bump transformers
, peft
, and diffusers
dependencies
#79
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Description
In order to support PyTorch inference over the recently released models the following dependencies have been bumped:
transformers
bumped to https://github.com/huggingface/transformers/releases/tag/v4.44.0sentencepiece
extra included as required by some models as e.g.black-forest-labs/FLUX.1-schnell
peft
bumped to https://github.com/huggingface/peft/releases/tag/v0.12.0diffusers
bumped to https://github.com/huggingface/diffusers/releases/tag/v0.30.0Additionally,
huggingface_hub
with the extrahf_transfer
has also been included in order to support faster downloads usinghf-transfer
when setting theHF_HUB_ENABLE_HF_TRANSFER=1
environment variable, useful when downloading large models such as most of the text generation and text to image models.Note
At the moment the dependencies are pinned within the
huggingface-inference-toolkit
Python package dependencies. Something to discuss is whether we want those to be set to>=
with the minimum supported versions, so that those are tweaked and bumped within theDockerfile
images that the cloud providers are managing; or just keep the current approach and pin those to==
and then release a new version of the Python package with the bumped dependencies.