“Hugging Face”

获取 Hugging Face 上的模型、数据集和 Space 的统计信息

Execute Hugging Face Hub operations using the `hf` CLI.

Create and manage datasets on Hugging Face Hub.

Add and manage evaluation results in Hugging Face model cards.

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

Track and visualize ML training experiments with Trackio.

This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs…

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

Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation…

Discover, compare, run, publish, and troubleshoot Hugging Face models, datasets, and Spaces with compatibility, authentication, reproducibility, and safety…

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

Hugging Face Hub CLI (hf) — search, download, and upload models and datasets, manage repos, query datasets with SQL, deploy inference endpoints, manage Spaces…

Plan, implement, or review hugging face gradio work in an existing codebase with compatibility, security, and verification controls.

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

Plan, launch, inspect, monitor, and cancel authenticated Hugging Face Jobs for CPU, GPU, TPU, batch, inference, and data workloads.

Apply hugging face datasets in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation.

Build and package Hugging Face workflows for demos, models, or hosted artifacts.

Discover and analyze Hugging Face papers with traceable sources, linked models and datasets, and clearly separated claims and evidence.

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

Explore, validate, filter, paginate, export, and prepare Hugging Face datasets through the Dataset Viewer API and Hub tooling.

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