Changelog

Source: NEWS.md

huggingfaceR 2.3.0

Optional local models

• hf_load_local_model() uses processor_kwargs on newer Sentence Transformers interfaces, avoiding the deprecated tokenizer_kwargs argument, while retaining compatibility with older constructors and preserving local-only loading flags.
• hf_local_setup(), hf_download_model(), and hf_load_local_model() provide an explicit setup, revision-aware download, and reusable local-model workflow. Python dependencies are optional and are not initialized at package load.
• hf_embed_local() and hf_classify_local() run standard safetensors-based embedding and text-classification models on the CPU by default, returning the same tidy columns as their API counterparts. Input order, duplicates, and missing values are preserved.
• Local loading keeps tokenizer and weights in the same snapshot, refuses remote custom code, supports cached offline use, and never falls back to hosted inference. Existing API-first functions and defaults are unchanged. Embedding snapshots require Sentence Transformers module metadata so a partial cache cannot silently change the model’s pooling configuration.
• A new local-model vignette and standalone online/offline validation script cover real downloads, predictions, and reuse across fresh R sessions.
• Local internals are separated into public operations, Python integration, and validation/error handling. Loading a Hub model initializes its Python environment and validates its snapshot once, without introducing a session-wide validation cache.
• Developer profiling compares the installed pre-refactor and current versions on the same native Linux runner, including cached loading, Python-native inference, R wrapper allocations, and unchanged prediction results.

huggingfaceR 2.2.0

CRAN release: 2026-08-29

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 several models the package shipped as defaults are no longer served by it.

Scope of task-based inference

huggingfaceR’s task functions (hf_classify(), hf_summarize(), hf_embed(), hf_text_to_image() and friends) speak the Hugging Face task API contract: POST .../models/{model} with an {"inputs": ...} body. Only the first-party hf-inference provider implements that contract. Other Inference Providers (Fal AI, nscale, DeepInfra, Together, and so on) expose their own routes, payloads, and response shapes, and are reachable through the Hugging Face clients rather than this contract.

This release makes that boundary explicit instead of silently producing confusing failures. Chat (hf_chat(), hf_generate(), hf_describe_image()) is unaffected: it uses the OpenAI-compatible router endpoint, which selects a provider server-side and supports every routed chat model.

Bug fixes

• hf_load_dataset() no longer multiplies rows for datasets with variable-length fields (#61). Row payloads were converted with tibble::as_tibble(), which recycles a row to the length of its longest field. A dataset such as openai/gdpval, whose rows carry fields like reference_files, therefore returned more rows than requested: asking for 3 rows returned 8, silently duplicating scalar values. Fields that are ever non-scalar are now returned as list-columns, so one source row always yields exactly one output row. Scalar fields are unchanged, and a JSON null is still read as NA rather than forcing a list-column.

• Documentation links updated. 36 links pointed at huggingface.co/docs/api-inference, which Hugging Face retired; they now point at the corresponding Inference Providers task pages. This also clears an R CMD check --as-cran NOTE about unreachable URLs.

• Short dataset names resolve more reliably. hf_load_dataset("imdb") expands short names to their full owner/name form by querying the Hub. That lookup had no retry, so a transient network failure silently fell through to the unexpanded name and surfaced as a confusing “dataset has been renamed” error. The lookup now retries, and if it still cannot expand the name the error says so and suggests passing the full ID.

• Unroutable models now fail with an actionable error. Task requests previously went to hf-inference unconditionally, so a model that provider does not serve returned an opaque “not found”. The package now checks the Hub’s provider mapping and raises an error naming the providers that do serve the model, and suggesting endpoint_url for a dedicated Inference Endpoint. Resolved mappings are cached for the session. If the Hub is unreachable the historical hf-inference route is used unchanged, so Hub metadata is not a new hard dependency for models that already worked.

• hf_text_to_image() default replaced. The previous default, black-forest-labs/FLUX.1-schnell, is served only by third-party providers and so was never reachable through the task contract. The default is now stabilityai/stable-diffusion-3-medium-diffusers, which hf-inference serves. It is a gated model: accept the licence on its model page once before first use.

• hf_text_to_speech() default replaced, and its limits documented. The previous default, facebook/mms-tts-eng, is no longer served by any Inference Provider. The default is now hexgrad/Kokoro-82M. Note that hf-inference currently serves no text-to-speech model, so serverless text-to-speech is not reachable through the task contract; the function now says so clearly and points to endpoint_url.

• HF_TOKEN is now recognised. The package previously read only the legacy HUGGING_FACE_HUB_TOKEN, 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. This also applies to the legacy hf_inference() and hf_ez_*() functions, which previously read the legacy variable directly.

• 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. Select columns by name rather than position.

• Router routing policies are no longer mistaken for providers. Model specifications such as model:cheapest, model:fastest, and model:preferred were previously treated as literal provider names and would have produced invalid task URLs. These policies are resolved by the router for chat completions, where they are passed through unchanged; for task requests the suffix is now recognised and ignored rather than corrupting the route.

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

• Token errors now 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 describes the Inference Providers token permission, HF_TOKEN, organization billing, and the hf-inference task-contract boundary, and no longer links to the retired huggingface.co/docs/api-inference page.

Known limitations

• Provider availability is read from the Hub at call time and can change; a provider may report status = "error" transiently. Successful lookups are cached for 15 minutes and failed lookups for 60 seconds; call hf_clear_provider_cache() to discard them sooner.
• The cache is keyed by model and by whether a token was supplied, not by token value. Switching between tokens with different access within one session may reuse a mapping; clear the cache when doing so.
• Third-party providers are not used for task requests. Reaching them requires provider-specific adapters, which this release does not implement.
• An empty or unavailable Hub provider mapping is treated as “unknown” and the historical hf-inference route is still attempted, so Hub metadata gaps cannot block a request that would otherwise have worked.

huggingfaceR 2.1.0

CRAN release: 2026-06-30

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.