Home Artificial Intelligence Posit AI Weblog: Hugging Face Integrations

Posit AI Weblog: Hugging Face Integrations

Posit AI Weblog: Hugging Face Integrations


We’re completely happy to announce the primary releases of hfhub and tok are actually on CRAN.
hfhub is an R interface to Hugging Face Hub, permitting customers to obtain and cache recordsdata
from Hugging Face Hub whereas tok implements R bindings for the Hugging Face tokenizers

Hugging Face quickly grew to become the platform to construct, share and collaborate on
deep studying functions and we hope these integrations will assist R customers to
get began utilizing Hugging Face instruments in addition to constructing novel functions.

We even have beforehand introduced the safetensors
package deal permitting to learn and write recordsdata within the safetensors format.


hfhub is an R interface to the Hugging Face Hub. hfhub at present implements a single
performance: downloading recordsdata from Hub repositories. Mannequin Hub repositories are
primarily used to retailer pre-trained mannequin weights along with some other metadata
essential to load the mannequin, such because the hyperparameters configurations and the
tokenizer vocabulary.

Downloaded recordsdata are ached utilizing the identical format because the Python library, thus cached
recordsdata might be shared between the R and Python implementation, for simpler and faster
switching between languages.

We already use hfhub within the minhub package deal and
within the ‘GPT-2 from scratch with torch’ weblog publish to
obtain pre-trained weights from Hugging Face Hub.

You should utilize hub_download() to obtain any file from a Hugging Face Hub repository
by specifying the repository id and the trail to file that you simply wish to obtain.
If the file is already within the cache, then the operate returns the file path imediately,
in any other case the file is downloaded, cached after which the entry path is returned.

weblog publish ‘What are Giant Language Fashions? What are they not?’.

When utilizing a pre-trained mannequin (each for inference or for effective tuning) it’s very
essential that you simply use the very same tokenization course of that has been used throughout
coaching, and the Hugging Face group has completed a tremendous job ensuring that its algorithms
match the tokenization methods used most LLM’s.

tok gives R bindings to the 🤗 tokenizers library. The tokenizers library is itself
applied in Rust for efficiency and our bindings use the extendr mission
to assist interfacing with R. Utilizing tok we are able to tokenize textual content the very same manner most
NLP fashions do, making it simpler to load pre-trained fashions in R in addition to sharing
our fashions with the broader NLP group.

tok might be put in from CRAN, and at present it’s utilization is restricted to loading
tokenizers vocabularies from recordsdata. For instance, you’ll be able to load the tokenizer for the GPT2
mannequin with:

Bear in mind that you could already host
Shiny (for R and Python) on Hugging Face Areas. For example, we’ve constructed a Shiny
app that makes use of:

  • torch to implement GPT-NeoX (the neural community structure of StableLM – the mannequin used for chatting)
  • hfhub to obtain and cache pre-trained weights from the StableLM repository
  • tok to tokenize and pre-process textual content as enter for the torch mannequin. tok additionally makes use of hfhub to obtain the tokenizer’s vocabulary.

The app is hosted at on this Area.
It at present runs on CPU, however you’ll be able to simply swap the the Docker picture in order for you
to run it on a GPU for sooner inference.

The app supply code can also be open-source and might be discovered within the Areas file tab.

Wanting ahead

It’s the very early days of hfhub and tok and there’s nonetheless a number of work to do
and performance to implement. We hope to get group assist to prioritize work,
thus, if there’s a function that you’re lacking, please open a problem within the
GitHub repositories.


Textual content and figures are licensed beneath Artistic Commons Attribution CC BY 4.0. The figures which were reused from different sources do not fall beneath this license and might be acknowledged by a notice of their caption: “Determine from …”.


For attribution, please cite this work as

Falbel (2023, July 12). Posit AI Weblog: Hugging Face Integrations. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2023-07-12-hugging-face-integrations/

BibTeX quotation

  writer = {Falbel, Daniel},
  title = {Posit AI Weblog: Hugging Face Integrations},
  url = {https://blogs.rstudio.com/tensorflow/posts/2023-07-12-hugging-face-integrations/},
  12 months = {2023}



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