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huggingface/hf-cli

CLI de Hugging Face Hub (`hf`) para descargar, subir y gestionar modelos, datasets, spaces, buckets, repos, papers, trabajos y más en el ecosistema de Hugging Face.

¿Qué es hf-cli?

Esta habilidad proporciona el CLI `hf`, la herramienta de línea de comandos moderna y oficial de Hugging Face Hub, que reemplaza al obsoleto `huggingface-cli`. Permite realizar operaciones completas sobre el ecosistema de Hugging Face, incluyendo la autenticación, gestión de caché local, descarga y subida de modelos, conjuntos de datos y Spaces, así como la administración de repositorios, discusiones y pull requests. También facilita la exploración y búsqueda de papers académicos, la gestión de colecciones, la configuración de Spaces, la creación de webhooks, y el despliegue y administración de HF Inference Endpoints. Es especialmente útil para necesidades de almacenamiento en la nube, como checkpoints de entrenamiento, pipelines de datos o trazas de agentes. Es compatible con Claude Code, Cursor, Codex y otros agentes de IA, y debe activarse siempre que el usuario mencione términos como 'hf', 'huggingface' o 'Hugging Face', incluso si no solicita explícitamente un comando CLI.

Compatible con~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/huggingface/skills/tree/main/skills/hf-cli

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Documentación

¿Qué hace hf-cli?

CLI de Hugging Face Hub (hf) para descargar, subir y gestionar modelos, datasets, spaces, buckets, repos, papers, trabajos y más en el Hugging Face Hub. Úsalo cuando: manejes autenticación; administres caché local; gestiones Hugging Face Buckets; ejecutes o programes trabajos en la infraestructura de Hugging Face; gestiones repos de Hugging Face; trabajes con discusiones y pull requests; explores modelos, datasets y spaces; leas, busques o explores papers académicos; administres colecciones; consultes datasets; configures spaces; configures webhooks; o despliegues y administres HF Inference Endpoints. Asegúrate de usar esta habilidad siempre que el usuario mencione 'hf', 'huggingface', 'Hugging Face', 'huggingface-cli' o 'hugging face cli', o quiera hacer algo relacionado con el ecosistema de Hugging Face y con IA y ML en general. Úsala también para necesidades de almacenamiento en la nube, como checkpoints de entrenamiento, pipelines de datos o trazas de agentes. Úsala incluso si el usuario no pide explícitamente un comando CLI. Reemplaza al obsoleto huggingface-cli.

Individual skills in this repo

This repo contains 5 individual skills — each has its own dedicated page.

huggingface/huggingface-datasets

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.

huggingface/huggingface-gradio

Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.

huggingface/huggingface-llm-trainer

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.

huggingface/huggingface-papers

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.

huggingface/transformers-js

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.

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