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huggingface/transformers-js

Utilisez Transformers.js pour exécuter des modèles de machine learning de pointe directement en JavaScript/TypeScript. Prend en charge le NLP, la vision par ordinateur, l'audio et les tâches multimodales.

Qu'est-ce que transformers-js ?

Ce skill permet aux agents comme Claude Code, Cursor ou Codex d'intégrer Transformers.js dans des projets JavaScript/TypeScript. Il simplifie l'utilisation de modèles pré-entraînés depuis le Hugging Face Hub pour des tâches comme la classification de texte, la traduction, le résumé, la classification d'images, la détection d'objets, la reconnaissance vocale et la classification audio. Le skill est compatible avec les environnements navigateur (via WebGPU/WASM) ainsi que les runtime serveurs (Node.js, Bun, Deno). En résumé, il offre un moyen accessible et performant d'ajouter des capacités d'IA avancées à des applications web et serveur sans nécessiter de backend Python ou de GPU dédié.

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

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Documentation

Que fait transformers-js ?

Utilisez Transformers.js pour exécuter des modèles de machine learning de pointe directement en JavaScript/TypeScript. Prend en charge le NLP (classification de texte, traduction, résumé), la vision par ordinateur (classification d'images, détection d'objets), l'audio (reconnaissance vocale, classification audio) et les tâches multimodales. Fonctionne dans les navigateurs et les runtime serveur (Node.js, Bun, Deno) avec WebGPU/WASM en utilisant des modèles pré-entraînés depuis le 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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