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This release re-aligns the package with the current Hugging Face
Inference Providers platform. Since mid-2025 the first-party
hf-inference provider has narrowed to CPU-friendly classic
models, and many popular models are now served only by third-party
providers. Requests that assumed hf-inference could fail
with a confusing “not found” error even when the model was perfectly
healthy.
Task requests now route to a provider that actually
serves the model. Previously every task-style request was sent
to hf-inference, regardless of which providers served the
model. huggingfaceR now resolves the provider from the
Hub’s inferenceProviderMapping, preferring
hf-inference when it is live and falling back to a live
third-party provider otherwise. This fixes
hf_text_to_image(), whose default model
(black-forest-labs/FLUX.1-schnell) is served by
nscale, fal-ai, and wavespeed but
not by hf-inference. Resolved mappings are
cached for the session; if the Hub is unreachable the previous
hf-inference route is used, so a Hub outage cannot break
inference.
hf_text_to_speech() works again.
The previous default model, facebook/mms-tts-eng, is no
longer served by any Inference Provider, so every call failed. The
default is now hexgrad/Kokoro-82M, which is live on
fal-ai and deepinfra.
HF_TOKEN is now recognised. The
package previously read only the legacy
HUGGING_FACE_HUB_TOKEN variable, so a token configured by
following current Hugging Face documentation (or set up for the
hf CLI or Python client) was ignored and users were told no
token was found. HF_TOKEN is now checked first, with
HUGGING_FACE_HUB_TOKEN still honoured as a fallback.
hf_set_token() sets both and stores HF_TOKEN
in .Renviron.
hf_list_providers() now covers non-chat
models. It previously queried only the router catalogue, which
contains chat-completion models, and so returned an empty tibble for
embedding, classification, and other task models. It now merges the Hub
provider mapping (all models, all tasks) with router pricing and latency
metrics where available, and gained a task column.
Router routing policies are no longer mistaken for
providers. Hugging Face accepts policy suffixes such as
model:cheapest, model:fastest, and
model:preferred. These were previously treated as literal
provider names and produced invalid URLs; they are now resolved
properly.
Organization billing. Set the
HF_BILL_TO environment variable to send the
X-HF-Bill-To header so Team and Enterprise usage is billed
to the organization instead of the individual user.
hf_clear_provider_cache() discards the session’s
cached provider mappings, which is useful after provider availability
changes.
Error messages for unroutable models now name the providers that do serve the model, so a failure points at a working route. Token errors link to the fine-grained token page with the required “Make calls to Inference Providers” permission, and rate-limit errors mention the monthly credit allowance.
Documentation now describes the Inference Providers token
permission, HF_TOKEN, and organization billing, and no
longer links to the retired
huggingface.co/docs/api-inference page.
hf_extract() turns unstructured text into tidy
columns with chat-model structured JSON output. Pass a lightweight named
schema such as c(name = "string", score = "number") or a
full JSON Schema list, and the function returns one row per input text
with one column per schema field (#55).
hf_chat() now supports tool/function calling,
streaming callbacks, and image inputs for vision-capable chat models.
New helpers hf_tool(), hf_run_tools(), and
hf_describe_image() make these capabilities available from
R pipelines (#55).
Multimodal inference wrappers. New functions add
audio, image, and generation workflows: hf_transcribe(),
hf_text_to_image(), hf_classify_image(),
hf_caption_image(), hf_detect_objects(), and
hf_text_to_speech() (#55). Live verification passed for
ASR, text-to-image, image classification, captioning, and object
detection; public hosted TTS provider support is currently blocked, so
hf_text_to_speech() is documented for compatible providers
or dedicated Inference Endpoints.
Hub files, providers, and guarded writes. New
Hub helpers include hf_hub_download(),
hf_list_repo_files(), hf_search_spaces(),
hf_search_papers(), hf_list_providers(),
hf_create_repo(), hf_upload_file(),
hf_push_dataset(), and guarded
hf_delete_repo() (#55). Search helpers now follow Hub
pagination links, and write/destructive operations require
confirm = TRUE.
First-class text tasks. New API-first,
tidyverse-native wrappers round out the text toolkit, each accepting
character vectors and returning tibbles: hf_summarize()
(summarization), hf_translate() (translation),
hf_ner() (named-entity recognition, one tidy row per entity
with character offsets), hf_question_answer() (extractive
QA), and hf_table_question_answer() (ask a data frame a
question in plain language).
Centralized default models. A new exported
helper, hf_default_model(), is the single source of truth
for every task’s default model. All hf_* functions now
resolve their model default through it (no behavior change
— the resolved values are identical), so defaults can be audited or
updated in one place. Call hf_default_model() to see the
whole registry, or hf_default_model("translate") for a
single task’s default.
hf_whoami() now returns billing/pro status and
token-role metadata so users can check whether their token is read-only
or write-capable before Hub write operations.
Beginner-friendly default translation model.
hf_translate() now defaults to
Helsinki-NLP/opus-mt-en-fr (English to French) instead of
facebook/nllb-200-distilled-600M. The Helsinki-NLP
opus-mt-* family encodes the translation direction in the
model ID, so hf_translate("Hello") works with no FLORES-200
language codes — a smoother first experience. NLLB remains fully
supported for multilingual translation via the model,
source, and target arguments.
Unified request engine with inference-provider
routing. Internal request construction is consolidated in
R/request.R (hf_parse_model(),
hf_inference_url(), hf_error_body(),
hf_is_transient(), hf_task_request()). As a
result, the model = "id:provider" suffix now selects an
inference provider for all serverless tasks — including
embeddings, classification, and the new text tasks — not just chat.
Retries now back off only on genuinely transient status codes (429/5xx),
and error messages are consistent across every inference
function.
The package no longer requires Python or reticulate for core functionality. All inference is handled through the Hugging Face Inference API via httr2. Legacy functions that depend on Python/reticulate remain available but are not required for new workflows.
Default chat and generation model changed from
HuggingFaceTB/SmolLM3-3B to
meta-llama/Llama-3.1-8B-Instruct, which has broader
provider support.
API-first architecture: All core functions
(hf_classify(), hf_embed(),
hf_chat(), hf_generate(),
hf_fill_mask()) use the Hugging Face Inference API
directly. No Python installation needed.
Text classification: hf_classify()
for sentiment analysis and hf_classify_zero_shot() for
custom label classification without training.
Embeddings and similarity:
hf_embed() generates dense vector representations.
hf_similarity() computes pairwise cosine similarity.
hf_nearest_neighbors(), hf_cluster_texts(),
and hf_extract_topics() provide higher-level semantic
analysis. hf_embed_umap() reduces embeddings to 2D for
visualization.
Chat and generation: hf_chat() for
single-turn LLM interaction with system prompts.
hf_conversation() and chat() for multi-turn
conversations with persistent history. hf_generate() for
text completion. hf_fill_mask() for BERT-style masked token
prediction.
Hub discovery: hf_search_models(),
hf_model_info(), hf_search_datasets(),
hf_dataset_info(), and hf_list_tasks() for
exploring the Hugging Face Hub from R.
Datasets: hf_load_dataset() loads
dataset rows directly into tibbles, with support for splits, pagination,
and column selection.
Batch processing: hf_embed_batch(),
hf_classify_batch(), and
hf_classify_zero_shot_batch() process large inputs with
parallel requests. hf_embed_chunks() and
hf_classify_chunks() add disk checkpointing for datasets
too large to hold in memory.
tidymodels integration:
step_hf_embed() recipe step embeds text columns as part of
a tidymodels preprocessing pipeline.
tidytext integration:
hf_embed_text() works directly with data frame text columns
for tidytext-style workflows.
Model availability checking:
hf_check_inference() queries model metadata to verify
whether a model supports the free serverless Inference API before you
make inference calls.
Dedicated Inference Endpoints: All inference
functions accept an endpoint_url parameter to route
requests to a dedicated Inference Endpoint instead of the public
serverless API. This supports models not available on the free tier and
production workloads requiring dedicated capacity.
All functions return tibbles and accept character vectors, enabling natural composition with dplyr, tidyr, and the rest of the tidyverse.
Improved error messages for 404 responses explain that the model
may exist on the Hub but not be available for serverless inference, and
suggest using hf_check_inference().
Documentation updated to clarify that the Inference API serves a curated subset of the Hub’s 500,000+ models, not all of them.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.