huggingface-gradio
$
npx mdskill add huggingface/skills/huggingface-gradioBuild interactive web UIs and ML demos in Python using Gradio for rapid prototyping.
- Creates functional web interfaces for machine learning models and Python functions.
- Utilizes the core Gradio Python library to manage components and logic.
- Determines necessary UI structure by analyzing required inputs and expected outputs.
- Outputs a shareable, interactive web application accessible via a local or public link.
SKILL.md
.github/skills/huggingface-gradioView on GitHub ↗
---
name: huggingface-gradio
description: Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
---
# Gradio
Gradio is a Python library for building interactive web UIs and ML demos. This skill covers the core API, patterns, and examples.
## Guides
Detailed guides on specific topics (read these when relevant):
- [Quickstart](https://www.gradio.app/guides/quickstart)
- [The Interface Class](https://www.gradio.app/guides/the-interface-class)
- [Blocks and Event Listeners](https://www.gradio.app/guides/blocks-and-event-listeners)
- [Controlling Layout](https://www.gradio.app/guides/controlling-layout)
- [More Blocks Features](https://www.gradio.app/guides/more-blocks-features)
- [Custom CSS and JS](https://www.gradio.app/guides/custom-CSS-and-JS)
- [Streaming Outputs](https://www.gradio.app/guides/streaming-outputs)
- [Streaming Inputs](https://www.gradio.app/guides/streaming-inputs)
- [Sharing Your App](https://www.gradio.app/guides/sharing-your-app)
- [Custom HTML Components](https://www.gradio.app/guides/custom-HTML-components)
- [Getting Started with the Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client)
- [Getting Started with the JS Client](https://www.gradio.app/guides/getting-started-with-the-js-client)
## Core Patterns
**Interface** (high-level): wraps a function with input/output components.
```python
import gradio as gr
def greet(name):
return f"Hello {name}!"
gr.Interface(fn=greet, inputs="text", outputs="text").launch()
```
**Blocks** (low-level): flexible layout with explicit event wiring.
```python
import gradio as gr
with gr.Blocks() as demo:
name = gr.Textbox(label="Name")
output = gr.Textbox(label="Greeting")
btn = gr.Button("Greet")
btn.click(fn=lambda n: f"Hello {n}!", inputs=name, outputs=output)
demo.launch()
```
**ChatInterface**: high-level wrapper for chatbot UIs.
```python
import gradio as gr
def respond(message, history):
return f"You said: {message}"
gr.ChatInterface(fn=respond).launch()
```
## Key Component Signatures
### `Textbox(value: str | I18nData | Callable | None = None, type: Literal['text', 'password', 'email'] = "text", lines: int = 1, max_lines: int | None = None, placeholder: str | I18nData | None = None, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, autofocus: bool = False, autoscroll: bool = True, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", text_align: Literal['left', 'right'] | None = None, rtl: bool = False, buttons: list[Literal['copy'] | Button] | None = None, max_length: int | None = None, submit_btn: str | bool | None = False, stop_btn: str | bool | None = False, html_attributes: InputHTMLAttributes | None = None)`
Creates a textarea for user to enter string input or display string output..
### `Number(value: float | Callable | None = None, label: str | I18nData | None = None, placeholder: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", buttons: list[Button] | None = None, precision: int | None = None, minimum: float | None = None, maximum: float | None = None, step: float = 1)`
Creates a numeric field for user to enter numbers as input or display numeric output..
### `Slider(minimum: float = 0, maximum: float = 100, value: float | Callable | None = None, step: float | None = None, precision: int | None = None, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", randomize: bool = False, buttons: list[Literal['reset']] | None = None)`
Creates a slider that ranges from {minimum} to {maximum} with a step size of {step}..
### `Checkbox(value: bool | Callable = False, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", buttons: list[Button] | None = None)`
Creates a checkbox that can be set to `True` or `False`.
### `Dropdown(choices: Sequence[str | int | float | tuple[str, str | int | float]] | None = None, value: str | int | float | Sequence[str | int | float] | Callable | DefaultValue | None = DefaultValue(), type: Literal['value', 'index'] = "value", multiselect: bool | None = None, allow_custom_value: bool = False, max_choices: int | None = None, filterable: bool = True, label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", buttons: list[Button] | None = None)`
Creates a dropdown of choices from which a single entry or multiple entries can be selected (as an input component) or displayed (as an output component)..
### `Radio(choices: Sequence[str | int | float | tuple[str, str | int | float]] | None = None, value: str | int | float | Callable | None = None, type: Literal['value', 'index'] = "value", label: str | I18nData | None = None, info: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", rtl: bool = False, buttons: list[Button] | None = None)`
Creates a set of (string or numeric type) radio buttons of which only one can be selected..
### `Image(value: str | PIL.Image.Image | np.ndarray | Callable | None = None, format: str = "webp", height: int | str | None = None, width: int | str | None = None, image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None = "RGB", sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None = None, type: Literal['numpy', 'pil', 'filepath'] = "numpy", label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, buttons: list[Literal['download', 'share', 'fullscreen'] | Button] | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, streaming: bool = False, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", webcam_options: WebcamOptions | None = None, placeholder: str | None = None, watermark: WatermarkOptions | None = None)`
Creates an image component that can be used to upload images (as an input) or display images (as an output)..
### `Audio(value: str | Path | tuple[int, np.ndarray] | Callable | None = None, sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None = None, type: Literal['numpy', 'filepath'] = "numpy", label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, streaming: bool = False, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", format: Literal['wav', 'mp3'] | None = None, autoplay: bool = False, editable: bool = True, buttons: list[Literal['download', 'share'] | Button] | None = None, waveform_options: WaveformOptions | dict | None = None, loop: bool = False, recording: bool = False, subtitles: str | Path | list[dict[str, Any]] | None = None, playback_position: float = 0)`
Creates an audio component that can be used to upload/record audio (as an input) or display audio (as an output)..
### `Video(value: str | Path | Callable | None = None, format: str | None = None, sources: list[Literal['upload', 'webcam']] | Literal['upload', 'webcam'] | None = None, height: int | str | None = None, width: int | str | None = None, label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", webcam_options: WebcamOptions | None = None, include_audio: bool | None = None, autoplay: bool = False, buttons: list[Literal['download', 'share'] | Button] | None = None, loop: bool = False, streaming: bool = False, watermark: WatermarkOptions | None = None, subtitles: str | Path | list[dict[str, Any]] | None = None, playback_position: float = 0)`
Creates a video component that can be used to upload/record videos (as an input) or display videos (as an output).
### `File(value: str | list[str] | Callable | None = None, file_count: Literal['single', 'multiple', 'directory'] = "single", file_types: list[str] | None = None, type: Literal['filepath', 'binary'] = "filepath", label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, height: int | str | float | None = None, interactive: bool | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", allow_reordering: bool = False, buttons: list[Button] | None = None)`
Creates a file component that allows uploading one or more generic files (when used as an input) or displaying generic files or URLs for download (as output).
### `Chatbot(value: list[MessageDict | Message] | Callable | None = None, label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, container: bool = True, scale: int | None = None, min_width: int = 160, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, autoscroll: bool = True, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", height: int | str | None = 400, resizable: bool = False, max_height: int | str | None = None, min_height: int | str | None = None, editable: Literal['user', 'all'] | None = None, latex_delimiters: list[dict[str, str | bool]] | None = None, rtl: bool = False, buttons: list[Literal['share', 'copy', 'copy_all'] | Button] | None = None, watermark: str | None = None, avatar_images: tuple[str | Path | None, str | Path | None] | None = None, sanitize_html: bool = True, render_markdown: bool = True, feedback_options: list[str] | tuple[str, ...] | None = ('Like', 'Dislike'), feedback_value: Sequence[str | None] | None = None, line_breaks: bool = True, layout: Literal['panel', 'bubble'] | None = None, placeholder: str | None = None, examples: list[ExampleMessage] | None = None, allow_file_downloads: <class 'inspect._empty'> = True, group_consecutive_messages: bool = True, allow_tags: list[str] | bool = True, reasoning_tags: list[tuple[str, str]] | None = None, like_user_message: bool = False)`
Creates a chatbot that displays user-submitted messages and responses.
### `Button(value: str | I18nData | Callable = "Run", every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, variant: Literal['primary', 'secondary', 'stop', 'huggingface'] = "secondary", size: Literal['sm', 'md', 'lg'] = "lg", icon: str | Path | None = None, link: str | None = None, link_target: Literal['_self', '_blank', '_parent', '_top'] = "_self", visible: bool | Literal['hidden'] = True, interactive: bool = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", scale: int | None = None, min_width: int | None = None)`
Creates a button that can be assigned arbitrary .click() events.
### `Markdown(value: str | I18nData | Callable | None = None, label: str | I18nData | None = None, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool | None = None, rtl: bool = False, latex_delimiters: list[dict[str, str | bool]] | None = None, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", sanitize_html: bool = True, line_breaks: bool = False, header_links: bool = False, height: int | str | None = None, max_height: int | str | None = None, min_height: int | str | None = None, buttons: list[Literal['copy']] | None = None, container: bool = False, padding: bool = False)`
Used to render arbitrary Markdown output.
### `HTML(value: Any | Callable | None = None, label: str | I18nData | None = None, html_template: str = "${value}", css_template: str = "", js_on_load: str | None = "element.addEventListener('click', function() { trigger('click') });", apply_default_css: bool = True, every: Timer | float | None = None, inputs: Component | Sequence[Component] | set[Component] | None = None, show_label: bool = False, visible: bool | Literal['hidden'] = True, elem_id: str | None = None, elem_classes: list[str] | str | None = None, render: bool = True, key: int | str | tuple[int | str, ...] | None = None, preserved_by_key: list[str] | str | None = "value", min_height: int | None = None, max_height: int | None = None, container: bool = False, padding: bool = False, autoscroll: bool = False, buttons: list[Button] | None = None, server_functions: list[Callable] | None = None, props: Any)`
Creates a component with arbitrary HTML.
## Custom HTML Components
If a task requires significant customization of an existing component or a component that doesn't exist in Gradio, you can create one with `gr.HTML`. It supports `html_template` (with `${}` JS expressions and `{{}}` Handlebars syntax), `css_template` for scoped styles, and `js_on_load` for interactivity — where `props.value` updates the component value and `trigger('event_name')` fires Gradio events. For reuse, subclass `gr.HTML` and define `api_info()` for API/MCP support. See the [full guide](https://www.gradio.app/guides/custom-HTML-components).
Here's an example that shows how to create and use these kinds of components:
```python
import gradio as gr
class StarRating(gr.HTML):
def __init__(self, label, value=0, **kwargs):
html_template = """
<h2>${label} rating:</h2>
${Array.from({length: 5}, (_, i) => `<img class='${i < value ? '' : 'faded'}' src='https://upload.wikimedia.org/wikipedia/commons/d/df/Award-star-gold-3d.svg'>`).join('')}
"""
css_template = """
img { height: 50px; display: inline-block; cursor: pointer; }
.faded { filter: grayscale(100%); opacity: 0.3; }
"""
js_on_load = """
const imgs = element.querySelectorAll('img');
imgs.forEach((img, index) => {
img.addEventListener('click', () => {
props.value = index + 1;
});
});
"""
super().__init__(value=value, label=label, html_template=html_template, css_template=css_template, js_on_load=js_on_load, **kwargs)
def api_info(self):
return {"type": "integer", "minimum": 0, "maximum": 5}
with gr.Blocks() as demo:
gr.Markdown("# Restaurant Review")
food_rating = StarRating(label="Food", value=3)
service_rating = StarRating(label="Service", value=3)
ambience_rating = StarRating(label="Ambience", value=3)
average_btn = gr.Button("Calculate Average Rating")
rating_output = StarRating(label="Average", value=3)
def calculate_average(food, service, ambience):
return round((food + service + ambience) / 3)
average_btn.click(
fn=calculate_average,
inputs=[food_rating, service_rating, ambience_rating],
outputs=rating_output
)
demo.launch()
```
## Event Listeners
All event listeners share the same signature:
```python
component.event_name(
fn: Callable | None | Literal["decorator"] = "decorator",
inputs: Component | Sequence[Component] | set[Component] | None = None,
outputs: Component | Sequence[Component] | set[Component] | None = None,
api_name: str | None = None,
api_description: str | None | Literal[False] = None,
scroll_to_output: bool = False,
show_progress: Literal["full", "minimal", "hidden"] = "full",
show_progress_on: Component | Sequence[Component] | None = None,
queue: bool = True,
batch: bool = False,
max_batch_size: int = 4,
preprocess: bool = True,
postprocess: bool = True,
cancels: dict[str, Any] | list[dict[str, Any]] | None = None,
trigger_mode: Literal["once", "multiple", "always_last"] | None = None,
js: str | Literal[True] | None = None,
concurrency_limit: int | None | Literal["default"] = "default",
concurrency_id: str | None = None,
api_visibility: Literal["public", "private", "undocumented"] = "public",
time_limit: int | None = None,
stream_every: float = 0.5,
key: int | str | tuple[int | str, ...] | None = None,
validator: Callable | None = None,
) -> Dependency
```
Supported events per component:
- **AnnotatedImage**: select
- **Audio**: stream, change, clear, play, pause, stop, pause, start_recording, pause_recording, stop_recording, upload, input
- **BarPlot**: select, double_click
- **BrowserState**: change
- **Button**: click
- **Chatbot**: change, select, like, retry, undo, example_select, option_select, clear, copy, edit
- **Checkbox**: change, input, select
- **CheckboxGroup**: change, input, select
- **ClearButton**: click
- **Code**: change, input, focus, blur
- **ColorPicker**: change, input, submit, focus, blur
- **Dataframe**: change, input, select, edit
- **Dataset**: click, select
- **DateTime**: change, submit
- **DeepLinkButton**: click
- **Dialogue**: change, input, submit
- **DownloadButton**: click
- **Dropdown**: change, input, select, focus, blur, key_up
- **DuplicateButton**: click
- **File**: change, select, clear, upload, delete, download
- **FileExplorer**: change, input, select
- **Gallery**: select, upload, change, delete, preview_close, preview_open
- **HTML**: change, input, click, double_click, submit, stop, edit, clear, play, pause, end, start_recording, pause_recording, stop_recording, focus, blur, upload, release, select, stream, like, example_select, option_select, load, key_up, apply, delete, tick, undo, retry, expand, collapse, download, copy
- **HighlightedText**: change, select
- **Image**: clear, change, stream, select, upload, input
- **ImageEditor**: clear, change, input, select, upload, apply
- **ImageSlider**: clear, change, stream, select, upload, input
- **JSON**: change
- **Label**: change, select
- **LinePlot**: select, double_click
- **LoginButton**: click
- **Markdown**: change, copy
- **Model3D**: change, upload, edit, clear
- **MultimodalTextbox**: change, input, select, submit, focus, blur, stop
- **Navbar**: change
- **Number**: change, input, submit, focus, blur
- **ParamViewer**: change, upload
- **Plot**: change
- **Radio**: select, change, input
- **ScatterPlot**: select, double_click
- **SimpleImage**: clear, change, upload
- **Slider**: change, input, release
- **State**: change
- **Textbox**: change, input, select, submit, focus, blur, stop, copy
- **Timer**: tick
- **UploadButton**: click, upload
- **Video**: change, clear, start_recording, stop_recording, stop, play, pause, end, upload, input
## Prediction CLI
The `gradio` CLI includes `info` and `predict` commands for interacting with Gradio apps programmatically. These are especially useful for coding agents that need to use Spaces in their workflows.
### `gradio info` — Discover endpoints and parameters
```bash
gradio info <space_id_or_url>
```
Returns a JSON payload describing all endpoints, their parameters (with types and defaults), and return values.
```bash
gradio info gradio/calculator
# {
# "/predict": {
# "parameters": [
# {"name": "num1", "required": true, "default": null, "type": {"type": "number"}},
# {"name": "operation", "required": true, "default": null, "type": {"enum": ["add", "subtract", "multiply", "divide"], "type": "string"}},
# {"name": "num2", "required": true, "default": null, "type": {"type": "number"}}
# ],
# "returns": [{"name": "output", "type": {"type": "number"}}],
# "description": ""
# }
# }
```
File-type parameters show `"type": "filepath"` with instructions to include `"meta": {"_type": "gradio.FileData"}` — this signals the file will be uploaded to the remote server.
### `gradio predict` — Send predictions
```bash
gradio predict <space_id_or_url> <endpoint> <json_payload>
```
Returns a JSON object with named output keys.
```bash
# Simple numeric prediction
gradio predict gradio/calculator /predict '{"num1": 5, "operation": "multiply", "num2": 3}'
# {"output": 15}
# Image generation
gradio predict black-forest-labs/FLUX.2-dev /infer '{"prompt": "A majestic dragon"}'
# {"Result": "/tmp/gradio/.../image.webp", "Seed": 1117868604}
# File upload (must include meta key)
gradio predict gradio/image_mod /predict '{"image": {"path": "/path/to/image.png", "meta": {"_type": "gradio.FileData"}}}'
# {"output": "/tmp/gradio/.../output.png"}
```
Both commands accept `--token` for accessing private Spaces.
## Additional Reference
- [End-to-End Examples](examples.md) — complete working apps
More from huggingface/skills
- hf-cliHugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub. Use when: handling authentication; managing local cache; managing Hugging Face Buckets; running or scheduling jobs on Hugging Face infrastructure; managing Hugging Face repos; discussions and pull requests; browsing models, datasets and spaces; reading, searching, or browsing academic papers; managing collections; querying datasets; configuring spaces; setting up webhooks; or deploying and managing HF Inference Endpoints. Make sure to use this skill whenever the user mentions 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli', or 'hugging face cli', or wants to do anything related to the Hugging Face ecosystem and to AI and ML in general. Also use for cloud storage needs like training checkpoints, data pipelines, or agent traces. Use even if the user doesn't explicitly ask for a CLI command. Replaces the deprecated `huggingface-cli`.
- hf-mcpUse Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
- hf-memHugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
- huggingface-best>
- huggingface-community-evalsRun evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.
- huggingface-datasetsUse this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
- huggingface-llm-trainerTrain or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
- huggingface-local-modelsUse to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
- huggingface-lora-space-builderBuild and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.
- huggingface-paper-publisherPublish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.