General documentation updates (#2355)

Pierrci · claude · web-flow · commit c2bb932e29a7 · 2026-04-08T15:23:45.000-04:00
Co-authored-by: Claude Sonnet 4.6 &lt;noreply@anthropic.com&gt;
diff --git a/docs/hub/allennlp.md b/docs/hub/allennlp.md
@@ -10,7 +10,7 @@ All models on the Hub come up with useful features
 1. A training metrics tab with automatically hosted TensorBoard traces.
 2. Metadata tags that help for discoverability.
 3. An interactive widget you can use to play out with the model directly in the browser.
-4. An Inference API that allows to make inference requests.
+4. An Inference Providers widget that allows to make inference requests.
 
 <div class="flex justify-center">
 <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-allennlp_widget.png"/>
diff --git a/docs/hub/asteroid.md b/docs/hub/asteroid.md
@@ -10,7 +10,7 @@ All models on the Hub come up with the following features:
 1. An automatically generated model card with a description, training configuration, metrics, and more.
 2. Metadata tags that help for discoverability and contain information such as licenses and datasets.
 3. An interactive widget you can use to play out with the model directly in the browser.
-4. An Inference API that allows to make inference requests.
+4. An Inference Providers widget that allows to make inference requests.
 
 <div class="flex justify-center">
 <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-transformers_widget.png"/>
diff --git a/docs/hub/datasets-pyarrow.md b/docs/hub/datasets-pyarrow.md
@@ -84,7 +84,7 @@ Note that Parquet files on Hugging Face are optimized to improve storage efficie
 * [Parquet Content Defined Chunking](https://huggingface.co/blog/parquet-cdc) optimizes Parquet for [Xet](https://huggingface.co/docs/hub/en/xet/index), Hugging Face's storage backend. It accelerates uploads and downloads thanks to chunk-based deduplication and allows efficient file editing
 * Page index accelerates filters when streaming and enables efficient random access, e.g. in the [Dataset Viewer](https://huggingface.co/docs/dataset-viewer)
 
-Pandas require extra argument to write optimized Parquet files:
+PyArrow requires extra arguments to write optimized Parquet files:
 
 ```python
 import pyarrow.parquet as pq
diff --git a/docs/hub/espnet.md b/docs/hub/espnet.md
@@ -10,7 +10,7 @@ All models on the Hub come up with useful features:
 1. An automatically generated model card with a description, a training configuration, licenses and more.
 2. Metadata tags that help for discoverability and contain information such as license, language and datasets.
 3. An interactive widget you can use to play out with the model directly in the browser.
-4. An Inference API that allows to make inference requests.
+4. An Inference Providers widget that allows to make inference requests.
 
 <div class="flex justify-center">
 <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-espnet_widget.png"/>
diff --git a/docs/hub/fastai.md b/docs/hub/fastai.md
@@ -9,7 +9,7 @@ You can find `fastai` models by filtering at the left of the [models page](https
 All models on the Hub come up with the following features:
 1. An automatically generated model card with a brief description and metadata tags that help for discoverability.
 2. An interactive widget you can use to play out with the model directly in the browser (for Image Classification)
-3. An Inference API that allows to make inference requests (for Image Classification).
+3. An Inference Providers widget that allows to make inference requests (for Image Classification).
 
 
 ## Using existing models
diff --git a/docs/hub/flair.md b/docs/hub/flair.md
@@ -11,7 +11,7 @@ All models on the Hub come with these useful features:
 
 1. An automatically generated model card with a brief description.
 2. An interactive widget you can use to play with the model directly in the browser.
-3. An Inference API that allows you to make inference requests.
+3. An Inference Providers widget that allows you to make inference requests.
 
 ## Installation
 
diff --git a/docs/hub/index.md b/docs/hub/index.md
@@ -48,7 +48,7 @@ The Hugging Face Hub is a platform with over 2M models, 500k datasets, and 1M de
 <a class="no-underline! hover:opacity-60 transform transition-colors hover:translate-x-px" href="./models-libraries">Libraries</a>
 <a class="no-underline! hover:opacity-60 transform transition-colors hover:translate-x-px" href="./models-tasks">Tasks</a>
 <a class="no-underline! hover:opacity-60 transform transition-colors hover:translate-x-px" href="./models-widgets">Widgets</a>
-<a class="no-underline! hover:opacity-60 transform transition-colors hover:translate-x-px" href="./models-inference">Inference API</a>
+<a class="no-underline! hover:opacity-60 transform transition-colors hover:translate-x-px" href="./models-inference">Inference Providers</a>
 <a class="no-underline! hover:opacity-60 transform transition-colors hover:translate-x-px" href="./models-download-stats">Download Stats</a>
 </div>
 
diff --git a/docs/hub/models-libraries.md b/docs/hub/models-libraries.md
@@ -4,7 +4,7 @@ The Hub has support for dozens of libraries in the Open Source ecosystem. Thanks
 
 The table below summarizes the supported libraries and their level of integration. Find all our supported libraries in [the model-libraries.ts file](https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries.ts).
 
-| Library                                                                     | Description                                                                          | Inference API | Widgets | Download from Hub | Push to Hub |
+| Library                                                                     | Description                                                                          | Inference Providers | Widgets | Download from Hub | Push to Hub |
 |-----------------------------------------------------------------------------|--------------------------------------------------------------------------------------|---|---:|---|---|
 | [Adapters](./adapters) | A unified Transformers add-on for parameter-efficient and modular fine-tuning.                                                | ✅  | ✅  | ✅ | ✅ |
 | [AllenNLP](./allennlp)                             | An open-source NLP research library, built on PyTorch.                               | ✅ | ✅ | ✅ | ❌ |
@@ -44,6 +44,6 @@ The table below summarizes the supported libraries and their level of integratio
 | [Transformers.js](./transformers-js)                | State-of-the-art Machine Learning for the web. Run 🤗 Transformers directly in your browser, with no need for a server! | ❌ | ❌ | ✅ | ❌ |
 | [Unity Sentis](./unity-sentis)                | Inference engine for the Unity 3D game engine | ❌ | ❌ | ❌ | ❌ |
 
-### How can I add a new library to the Inference API?
+### How can I add support for a new library?
 
 If you're interested in adding your library, please reach out to us! Read about it in [Adding a Library Guide](./models-adding-libraries).
diff --git a/docs/hub/models-uploading.md b/docs/hub/models-uploading.md
@@ -58,7 +58,7 @@ The UI allows you to explore the model files and commits and to see the diff int
 5. Add metadata
 
 You can add metadata to your model card. You can specify:
-* the type of task this model is for, enabling widgets and the Inference API.
+* the type of task this model is for, enabling widgets and Inference Providers.
 * the used library (`transformers`, `spaCy`, etc.)
 * the language
 * the dataset
diff --git a/docs/hub/notifications.md b/docs/hub/notifications.md
@@ -7,6 +7,7 @@ By default, you'll receive a notification if:
 - Someone mentions you in a discussion/PR.
 - A new comment is posted in a discussion/PR you participated in.
 - A new discussion/PR or comment is posted in one of the repositories of an organization or user you are watching.
+- Someone replies to one of your posts, blog articles, or paper pages.
 
 ![Notifications page](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/notifications-page.png)
 
diff --git a/docs/hub/ollama.md b/docs/hub/ollama.md
@@ -2,7 +2,7 @@
 
 ![cover](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/ollama/cover.png)
 
-🆕 You can now also run private GGUFs from the Hugging Face Hub.
+You can also run private GGUFs from the Hugging Face Hub.
 
 Ollama is an application based on llama.cpp to interact with LLMs directly through your computer. You can use any GGUF quants created by the community ([bartowski](https://huggingface.co/bartowski), [MaziyarPanahi](https://huggingface.co/MaziyarPanahi) and [many more](https://huggingface.co/models?pipeline_tag=text-generation&library=gguf&sort=trending)) on Hugging Face directly with Ollama, without creating a new `Modelfile`. At the time of writing there are 45K public GGUF checkpoints on the Hub, you can run any of them with a single `ollama run` command. We also provide customisations like choosing quantization type, system prompt and more to improve your overall experience. 
 
diff --git a/docs/hub/paddlenlp.md b/docs/hub/paddlenlp.md
@@ -24,7 +24,7 @@ All models on the Hub come up with the following features:
 </div>
 
 
-3. An Inference API that allows to make inference requests.
+3. An Inference Providers widget that allows to make inference requests.
 
 <div class="flex justify-center">
 <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/PaddleNLP-3.jpg"/>
diff --git a/docs/hub/sentence-transformers.md b/docs/hub/sentence-transformers.md
@@ -10,7 +10,7 @@ All models on the Hub come up with features:
 1. An automatically generated model card with a description, example code snippets, architecture overview, and more. 
 2. Metadata tags that help for discoverability and contain information such as license.
 3. An interactive widget you can use to play out with the model directly in the browser.
-4. An Inference API that allows to make inference requests.
+4. An Inference Providers widget that allows to make inference requests.
 
 <div class="flex justify-center">
 <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-sentence_transformers_widget.png"/>
diff --git a/docs/hub/setfit.md b/docs/hub/setfit.md
@@ -15,7 +15,7 @@ You can find SetFit models by filtering at the left of the [models page](https:/
 All models on the Hub come with these useful features:
 1. An automatically generated model card with a brief description.
 2. An interactive widget you can use to play with the model directly in the browser.
-3. An Inference API that allows you to make inference requests.
+3. An Inference Providers widget that allows you to make inference requests.
 
 ## Installation
 
diff --git a/docs/hub/spaces-dependencies.md b/docs/hub/spaces-dependencies.md
@@ -4,9 +4,9 @@
 
 The default Gradio Spaces environment comes with several pre-installed dependencies:
 
-* The [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/index) client library allows you to manage your repository and files on the Hub with Python and programmatically access the Inference API from your Space. If you choose to instantiate the model in your app with the Inference API, you can benefit from the built-in acceleration optimizations. This option also consumes less computing resources, which is always nice for the environment! 🌎 
+* The [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/index) client library allows you to manage your repository and files on the Hub with Python and programmatically access [Inference Providers](./models-inference) from your Space. If you choose to instantiate the model in your app with Inference Providers, you can benefit from the built-in acceleration optimizations. This option also consumes less computing resources, which is always nice for the environment! 🌎 
 
-  Refer to this [page](https://huggingface.co/docs/huggingface_hub/how-to-inference) for more information on how to programmatically access the Inference API.
+  Refer to this [page](https://huggingface.co/docs/huggingface_hub/how-to-inference) for more information on how to programmatically access Inference Providers.
 
 * [`requests`](https://docs.python-requests.org/en/master/) is useful for calling third-party APIs from your app.
 
diff --git a/docs/hub/spaces-more-ways-to-create.md b/docs/hub/spaces-more-ways-to-create.md
@@ -6,7 +6,7 @@ You can duplicate a Space by clicking the three dots at the top right and select
 
 ## Creating a Space from a model
 
-New! You can now create a Gradio demo directly from most model pages, using the "Deploy -> Spaces" button.
+You can create a Gradio demo directly from most model pages, using the "Deploy -> Spaces" button.
 
 <video src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/model-page-deploy-to-spaces.mp4" controls autoplay muted loop />
 
diff --git a/docs/hub/spaces-sdks-docker-label-studio.md b/docs/hub/spaces-sdks-docker-label-studio.md
@@ -117,7 +117,7 @@ definition.
 * `POSTGRE_HOST`: Set this to the host that your Postgres database is running
    on.
 
-* `POSTGRE_PORT`: Set this to the port that your Pogtgres database is running
+* `POSTGRE_PORT`: Set this to the port that your Postgres database is running
   on.
 
 * `STORAGE_PERSISTENCE`: Set this to `1` to remove the warning about ephemeral
diff --git a/docs/hub/spaces-zerogpu.md b/docs/hub/spaces-zerogpu.md
@@ -13,7 +13,7 @@ Unlike traditional single-GPU allocations, ZeroGPU's efficient system lowers bar
 
 - **Using existing ZeroGPU Spaces**
   - ZeroGPU Spaces are available to use for free to all users. (Visit [the curated list](https://huggingface.co/spaces/enzostvs/zero-gpu-spaces)).
-  - [PRO users](https://huggingface.co/subscribe/pro) get x7 more daily usage quota, highest priority in GPU queues, and can go beyond their daily quota using pre-paid credits when using any ZeroGPU Spaces.
+  - [PRO users](https://huggingface.co/subscribe/pro) get x8 more daily usage quota, highest priority in GPU queues, and can go beyond their daily quota using pre-paid credits when using any ZeroGPU Spaces.
 - **Hosting your own ZeroGPU Spaces**
   - Personal accounts: [Subscribe to PRO](https://huggingface.co/settings/billing/subscription) to access ZeroGPU in the hardware options when creating a new Gradio SDK Space.
   - Organizations: [Subscribe to a Team or Enterprise plan](https://huggingface.co/enterprise) to enable ZeroGPU Spaces for all organization members.
diff --git a/docs/hub/spacy.md b/docs/hub/spacy.md
@@ -11,7 +11,7 @@ All models on the Hub come up with useful features
 2. An evaluation sections at top right where you can look at the metrics.
 3. Metadata tags that help for discoverability and contain information such as license and language.
 4. An interactive widget you can use to play out with the model directly in the browser
-5. An Inference API that allows to make inference requests.
+5. An Inference Providers widget that allows to make inference requests.
 
 <div class="flex justify-center">
 <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-spacy_widget.png"/>
diff --git a/docs/hub/span_marker.md b/docs/hub/span_marker.md
@@ -9,7 +9,7 @@ You can find `span_marker` models by filtering at the left of the [models page](
 All models on the Hub come with these useful features:
 1. An automatically generated model card with a brief description.
 2. An interactive widget you can use to play with the model directly in the browser.
-3. An Inference API that allows you to make inference requests.
+3. An Inference Providers widget that allows you to make inference requests.
 
 ## Installation
 
diff --git a/docs/hub/speechbrain.md b/docs/hub/speechbrain.md
@@ -10,7 +10,7 @@ All models on the Hub come up with the following features:
 1. An automatically generated model card with a brief description.
 2. Metadata tags that help for discoverability with information such as the language, license, paper, and more.
 3. An interactive widget you can use to play out with the model directly in the browser.
-4. An Inference API that allows to make inference requests.
+4. An Inference Providers widget that allows to make inference requests.
 
 ## Using existing models
 
diff --git a/docs/hub/stanza.md b/docs/hub/stanza.md
@@ -9,7 +9,7 @@ You can find `stanza` models by filtering at the left of the [models page](https
 All models on the Hub come up with the following features:
 1. An automatically generated model card with a brief description and metadata tags that help for discoverability.
 2. An interactive widget you can use to play out with the model directly in the browser (for named entity recognition and part of speech).
-3. An Inference API that allows to make inference requests (for named entity recognition and part of speech).
+3. An Inference Providers widget that allows to make inference requests (for named entity recognition and part of speech).
 
 
 ## Using existing models
diff --git a/docs/hub/timm.md b/docs/hub/timm.md
@@ -10,7 +10,7 @@ All models on the Hub come with several useful features:
 1. An automatically generated model card, which model authors can complete with [information about their model](./model-cards).
 2. Metadata tags help users discover the relevant `timm` models.
 3. An [interactive widget](./models-widgets) you can use to play with the model directly in the browser.
-4. An [Inference API](./models-inference) that allows users to make inference requests.
+4. An [Inference Providers](./models-inference) that allows users to make inference requests.
 
 ## Using existing models from the Hub
 
@@ -142,7 +142,7 @@ model_reloaded = timm.create_model(
 
 ## Inference Widget and API
 
-All `timm` models on the Hub are automatically equipped with an [inference widget](./models-widgets), pictured below for [nateraw/timm-resnet50-beans](https://huggingface.co/nateraw/timm-resnet50-beans). Additionally, `timm` models are available through the [Inference API](./models-inference), which you can access through HTTP with cURL, Python's `requests` library, or your preferred method for making network requests. 
+All `timm` models on the Hub are automatically equipped with an [inference widget](./models-widgets), pictured below for [nateraw/timm-resnet50-beans](https://huggingface.co/nateraw/timm-resnet50-beans). Additionally, `timm` models are available through the [Inference Providers](./models-inference), which you can access through HTTP with cURL, Python's `requests` library, or your preferred method for making network requests. 
 
 <div class="flex justify-center">
 <img class="block dark:hidden" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/libraries-timm_widget.png"/>
diff --git a/docs/hub/webhooks-guide-auto-retrain.md b/docs/hub/webhooks-guide-auto-retrain.md
@@ -1,8 +1,5 @@
 # Webhook guide: Setup an automatic system to re-train a model when a dataset changes
 
-> [!TIP]
-> Webhooks are now publicly available!
-
 This guide will help walk you through the setup of an automatic training pipeline on the Hugging Face platform
 using HF Datasets, Webhooks, Spaces, and AutoTrain.
 
diff --git a/docs/hub/webhooks-guide-discussion-bot.md b/docs/hub/webhooks-guide-discussion-bot.md
@@ -1,9 +1,6 @@
 # Webhook guide: build a Discussion bot based on BLOOM
 
-> [!TIP]
-> Webhooks are now publicly available!
-
-Here's a short guide on how to use Hugging Face Webhooks to build a bot that replies to Discussion comments on the Hub with a response generated by BLOOM, a multilingual language model, using the free Inference API.
+Here's a short guide on how to use Hugging Face Webhooks to build a bot that replies to Discussion comments on the Hub with a response generated by BLOOM, a multilingual language model, using Inference Providers.
 
 ## Create your Webhook in your user profile
 
@@ -87,7 +84,7 @@ In that case, we will continue to the next step:
 		...
 ```
 
-This is the coolest part: we call the Inference API for the BLOOM model, prompting it with `PROMPT`, and we get the continuation text, i.e., the part generated by the model.
+This is the coolest part: we call Inference Providers for the BLOOM model, prompting it with `PROMPT`, and we get the continuation text, i.e., the part generated by the model.
 
 Finally, we will post it as a reply in the same discussion thread:
 
diff --git a/docs/hub/webhooks-guide-metadata-review.md b/docs/hub/webhooks-guide-metadata-review.md
@@ -1,8 +1,5 @@
 # Webhook guide: Setup an automatic metadata quality review for models and datasets 
 
-> [!TIP]
-> Webhooks are now publicly available!
-
 This guide will walk you through creating a system that reacts to changes to a user's or organization's models or datasets on the Hub and creates a 'metadata review' for the changed repository. 
 
 ## What are we building and why?
diff --git a/docs/hub/webhooks.md b/docs/hub/webhooks.md
@@ -1,11 +1,8 @@
 # Webhooks
 
-> [!TIP]
-> Webhooks are now publicly available!
-
 Webhooks are a foundation for MLOps-related features. They allow you to listen for new changes on specific repos or to all repos belonging to particular set of users/organizations (not just your repos, but any repo).
 
-You can use them to auto-convert models, build community bots, or build CI/CD for your models, datasets, and Spaces (and much more!).
+You can use them to auto-convert models, build community bots, or build CI/CD for your models, datasets, and Spaces (and much more!). Webhooks can also [trigger Jobs](./jobs-webhooks) to automate compute tasks in response to repo events.
 
 
 The documentation for Webhooks is below – or you can also browse our **guides** showcasing a few possible use cases of Webhooks:
