Utilize o Transformers.js para executar modelos de aprendizado de máquina de última geração diretamente em JavaScript/TypeScript. Suporta PLN (classificação de texto, tradução, sumarização), visão computacional (classificação de imagens, detecção de objetos), áudio (reconhecimento de fala, classificação de áudio) e tarefas multimodais. Funciona em navegadores e runtimes server-side (Node.js, Bun, Deno) com WebGPU/WASM usando modelos pré-treinados do Hugging Face Hub.
O skill huggingface/transformers-js permite que desenvolvedores integrem e executem modelos de machine learning de última geração diretamente em aplicações JavaScript e TypeScript. Ele suporta uma ampla gama de tarefas, incluindo processamento de linguagem natural (PLN) como classificação de texto, tradução e sumarização; visão computacional como classificação de imagens e detecção de objetos; áudio como reconhecimento de fala e classificação de áudio; além de tarefas multimodais. O skill funciona tanto em navegadores quanto em runtimes server-side como Node.js, Bun e Deno, utilizando aceleração via WebGPU e WASM, com modelos pré-treinados obtidos do Hugging Face Hub. Isso torna o Transformers.js uma ferramenta poderosa e flexível para adicionar inteligência artificial a qualquer aplicação web ou servidor sem depender de APIs externas.
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Utilize o Transformers.js para executar modelos de aprendizado de máquina de última geração diretamente em JavaScript/TypeScript. Suporta PLN (classificação de texto, tradução, sumarização), visão computacional (classificação de imagens, detecção de objetos), áudio (reconhecimento de fala, classificação de áudio) e tarefas multimodais. Funciona em navegadores e runtimes server-side (Node.js, Bun, Deno) com WebGPU/WASM usando modelos pré-treinados do Hugging Face Hub.
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.
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
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.
Generate spectrograms and feature-panel visualizations from audio with the songsee CLI.
Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.
Scan a codebase for deepening opportunities, present them as a visual HTML report, then grill through whichever one you pick.
Add documentation for a new AI provider — usage docs, env vars, Docker config, image resources.
Tests in real browsers via Chrome DevTools MCP. Use when building or debugging anything that runs in a browser. Use when you need to inspect the DOM, capture console errors, analyze network requests, profile performance, or verify visual output with real runtime data. Requires the chrome-devtools MCP server to be configured.
This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a JavaScript script.