huggingface
10 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
huggingfacehf-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; configur
- 導入数
- 2
- GitHub Stars
- 1.1万
- 更新日
- 10月8日
huggingfacehuggingface-gradio
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
- 導入数
- 2
- GitHub Stars
- 1.1万
- 更新日
- 9月29日
huggingfacehuggingface-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
- 導入数
- 2
- GitHub Stars
- 1.1万
- 更新日
- 10月1日
huggingfacehuggingface-vision-trainer
Trains and fine-tunes vision models for object detection (D-FINE, RT-DETR v2, DETR, YOLOS), image classification (timm models — MobileNetV3, MobileViT, ResNet, ViT/DINOv3 — plus any Transformers classifier), and SAM/SAM2 segmentation using Hugging Face Transformers on Hugging Face Jobs cloud GPUs. Covers COCO-format dataset preparation, Albumentations augmentation, mAP/mAR evaluation, accuracy metrics, SAM segmentation with bbox/point prompts, DiceCE loss, hardware selection, cost estimation, Tr
- 導入数
- 2
- GitHub Stars
- 1.1万
- 更新日
- 9月29日
huggingfacehf-cloud-sagemaker-deployment-planner
Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use thi
- 導入数
- 1
- GitHub Stars
- 1.1万
- 更新日
- 9月29日
huggingfacehuggingface-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.
- 導入数
- 1
- GitHub Stars
- 1.1万
- 更新日
- 10月8日
huggingfacehuggingface-paper-publisher
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
- 導入数
- 1
- GitHub Stars
- 1.1万
- 更新日
- 9月29日
huggingfacehuggingface-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.
- 導入数
- 1
- GitHub Stars
- 1.1万
- 更新日
- 9月29日
huggingfacehuggingface-trackio
Track and visualize ML training experiments with Trackio. Use when logging metrics during training (Python API), firing alerts for training diagnostics, or retrieving/analyzing logged metrics (CLI). Supports real-time dashboard visualization, alerts with webhooks, HF Space syncing, and JSON output for automation.
- 導入数
- 1
- GitHub Stars
- 1.1万
- 更新日
- 10月8日
huggingfacetrain-sentence-transformers
Train or fine-tune sentence-transformers models across SentenceTransformer (bi-encoder, dense or static embedding model for retrieval, similarity, clustering, classification, paraphrase mining, dedup, multimodal), CrossEncoder (reranker, pair scoring for two-stage retrieval / pair classification), SparseEncoder (SPLADE, sparse embedding model for learned-sparse retrieval), and MultiVectorEncoder (ColBERT / late-interaction, per-token embeddings scored with MaxSim). Covers loss selection, hard-ne
- 導入数
- 1
- GitHub Stars
- 1.1万
- 更新日
- 9月29日