“Hugging Face”

Connect to Hugging Face Hub and thousands of Gradio AI Applications

Hugging Face Hub CLI (`hf`) for downloading, uploading, and managing models, datasets, spaces, buckets, repos, papers, jobs, and more on the Hugging Face Hub.

Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging…

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…

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.

AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU.

Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub

Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet…

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA.

Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT…

Publish and manage research papers on Hugging Face Hub.

Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help.

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware.

Use Hugging Face Hub via MCP server tools.

Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by…

Track and visualize ML training experiments with Trackio.

Train or fine-tune sentence-transformers models across `SentenceTransformer` (bi-encoder, dense or static embedding model for retrieval, similarity…

Build Gradio web UIs and demos in Python.

Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm.

Use Transformers.js to run state-of-the-art machine learning models directly in JavaScript/TypeScript.