huggingface-gradio은(는) 무엇을 하나요?
Python으로 Gradio 웹 UI와 데모를 구축합니다. Gradio 앱, 컴포넌트, 이벤트 리스너, 레이아웃 또는 챗봇을 생성하거나 편집할 때 사용하세요.
Python으로 Gradio 웹 UI와 데모를 구축합니다. Gradio 앱, 컴포넌트, 이벤트 리스너, 레이아웃 또는 챗봇을 생성하거나 편집할 때 사용하세요.
이 스킬은 Python 기반의 Gradio 라이브러리를 활용하여 웹 UI와 데모를 빠르게 구축할 수 있도록 지원합니다. 개발자는 Gradio 앱, 컴포넌트, 이벤트 리스너, 레이아웃 및 챗봇 생성/편집 작업을 손쉽게 수행할 수 있습니다. 주로 Hugging Face Spaces, Colab, 로컬 환경 등 다양한 플랫폼에서 Gradio 애플리케이션 개발을 가속화하는 데 사용됩니다. SKILL.md 문서를 기반으로 Gradio의 최신 API와 모범 사례를 반영하여 코드 생성, 디버깅, 레이아웃 구성, 인터랙티브 요소 추가 등을 안내합니다.
npx skills add https://github.com/huggingface/skills/tree/main/skills/huggingface-gradioInstalled? Explore more 코딩 & 개발 skills: steipete/bluebubbles, steipete/eightctl, steipete/blucli · View all 6 →
Python으로 Gradio 웹 UI와 데모를 구축합니다. Gradio 앱, 컴포넌트, 이벤트 리스너, 레이아웃 또는 챗봇을 생성하거나 편집할 때 사용하세요.
This repo contains 5 individual skills — each has its own dedicated page.
Hugging 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`.
Use 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.
Train 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.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain, or analyze an AI research paper.
Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript. Supports NLP (text classification, translation, summarization), computer vision (image classification, object detection), audio (speech recognition, audio classification), and multimodal tasks. Works in browsers and server-side runtimes (Node.js, Bun, Deno) with WebGPU/WASM using pre-trained models from Hugging Face Hub.
Send and manage iMessages via BlueBubbles, including attachments, tapbacks, edits, replies, and groups.
Control Eight Sleep pods (status, temperature, alarms, schedules).
BluOS CLI (blu) for discovery, playback, grouping, and volume.
Create, search, and manage Bear notes via grizzly CLI.
Capture frames or clips from RTSP/ONVIF cameras.
Search GIF providers with CLI/TUI, download results, and extract stills/sheets.