huggingfaceR 2.2.0

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.

Bug fixes

• 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.

New features

• 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.

Improvements

• 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.

huggingfaceR 2.1.0

New features

• 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).

Improvements

• 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.

huggingfaceR 2.0.0

Breaking changes

• 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.

New features

• 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.

Improvements

• 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.