We’re glad to announce the primary releases of hfhub and tok at the moment are 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
library.
Hugging Face quickly grew to become the platform to construct, share and collaborate on
deep studying purposes and we hope these integrations will assist R customers to
get began utilizing Hugging Face instruments in addition to constructing novel purposes.
We even have beforehand introduced the safetensors
package deal permitting to learn and write recordsdata within the safetensors format.
hfhub
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 every other metadata
essential to load the mannequin, such because the hyperparameters configurations and the
tokenizer vocabulary.
Downloaded recordsdata are ached utilizing the identical structure 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 submit to
obtain pre-trained weights from Hugging Face Hub.
You need to use hub_download() to obtain any file from a Hugging Face Hub repository
by specifying the repository id and the trail to file that you just need to obtain.
If the file is already within the cache, then the perform returns the file path imediately,
in any other case the file is downloaded, cached after which the entry path is returned.
path <- hfhub::hub_download("gpt2", "mannequin.safetensors")
path
#> /Customers/dfalbel/.cache/huggingface/hub/models--gpt2/snapshots/11c5a3d5811f50298f278a704980280950aedb10/mannequin.safetensorstok
Tokenizers are accountable for changing uncooked textual content into the sequence of integers that
is commonly used because the enter for NLP fashions, making them an important element of the
NLP pipelines. If you’d like the next stage overview of NLP pipelines, you would possibly need to learn
our earlier weblog submit ‘What are Massive Language Fashions? What are they not?’.
When utilizing a pre-trained mannequin (each for inference or for nice tuning) it’s very
essential that you just use the very same tokenization course of that has been used throughout
coaching, and the Hugging Face group has finished an incredible job ensuring that its algorithms
match the tokenization methods used most LLM’s.
tok offers R bindings to the 🤗 tokenizers library. The tokenizers library is itself
applied in Rust for efficiency and our bindings use the extendr challenge
to assist interfacing with R. Utilizing tok we will tokenize textual content the very same method most
NLP fashions do, making it simpler to load pre-trained fashions in R in addition to sharing
our fashions with the broader NLP neighborhood.
tok might be put in from CRAN, and at present it’s utilization is restricted to loading
tokenizers vocabularies from recordsdata. For instance, you possibly can load the tokenizer for the GPT2
mannequin with:
tokenizer <- tok::tokenizer$from_pretrained("gpt2")
ids <- tokenizer$encode("Hiya world! You need to use tokenizers from R")$ids
ids
#> [1] 15496 995 0 921 460 779 11241 11341 422 371
tokenizer$decode(ids)
#> [1] "Hiya world! You need to use tokenizers from R"Areas
Keep in mind that you would be able to already host
Shiny (for R and Python) on Hugging Face Areas. For instance, 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 House.
It at present runs on CPU, however you possibly can simply swap the the Docker picture if you’d like
to run it on a GPU for sooner inference.
The app supply code can 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 lots of work to do
and performance to implement. We hope to get neighborhood assist to prioritize work,
thus, if there’s a characteristic that you’re lacking, please open a problem within the
GitHub repositories.
Reuse
Textual content and figures are licensed below Inventive Commons Attribution CC BY 4.0. The figures which were reused from different sources do not fall below this license and might be acknowledged by a word of their caption: “Determine from …”.
Quotation
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
@misc{hugging-face-integrations,
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}
}