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huggingface/transformers-js

Usa Transformers.js para ejecutar modelos de machine learning de última generación directamente en JavaScript/TypeScript. Soporta NLP, visión por computadora, audio y tareas multimodales.

¿Qué es transformers-js?

Este skill te permite utilizar Transformers.js para integrar modelos de machine learning de vanguardia directamente en tus aplicaciones JavaScript/TypeScript, sin necesidad de backend. Es útil para desarrolladores que quieren agregar capacidades de NLP (clasificación de texto, traducción, resumen), visión por computadora (clasificación de imágenes, detección de objetos), audio (reconocimiento de voz, clasificación de audio) y tareas multimodales. Funciona tanto en navegadores como en entornos de servidor (Node.js, Bun, Deno) aprovechando WebGPU/WASM y modelos pre-entrenados del Hugging Face Hub. Es una herramienta poderosa para democratizar el acceso a la IA en el ecosistema JavaScript.

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

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Documentación

¿Qué hace transformers-js?

Usa Transformers.js para ejecutar modelos de machine learning de última generación directamente en JavaScript/TypeScript. Soporta NLP (clasificación de texto, traducción, resumen), visión por computadora (clasificación de imágenes, detección de objetos), audio (reconocimiento de voz, clasificación de audio) y tareas multimodales. Funciona tanto en navegadores como en entornos de servidor (Node.js, Bun, Deno) con WebGPU/WASM utilizando modelos pre-entrenados del Hugging Face Hub.

Individual skills in this repo

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

huggingface/hf-cli

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`.

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.

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