huggingface
26 Skill con documentazione sostanziale e fonti verificate. Apri una scheda per leggere le istruzioni originali e verificare pacchetto e installazione.
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
- Installazioni / 1h
- 2
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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.
- Installazioni / 1h
- 2
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehf-cloud-aws-context-discovery
Discover the user's local AWS context (active profile, region, account ID, caller identity) at the start of any AWS task. Use this skill before any other AWS work — deploying to SageMaker, creating resources, calling AWS APIs, or anything that touches an AWS account. Use it especially when the user has not specified a region or profile explicitly, when they say things like "use my AWS account", "deploy to AWS", "use my profile", or when about to make any AWS CLI or SDK call. Never guess the regi
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehf-cloud-python-env-setup
Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run pip install, when about to invoke boto3, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never pip install into it. Always isolate. This skill prevents the most common
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehf-cloud-sagemaker-iam-preflight
Ensure a usable SageMaker execution role exists before deploying or training. Use this skill whenever about to create a SageMaker endpoint, model, training job, or any resource that requires an execution role. Use it especially when the user has not provided a role ARN explicitly, when scripts are about to call iam:CreateRole, or when an AccessDenied error mentions an IAM action. Never blindly call iam:CreateRole — always check for existing roles first. This skill prevents the most common SageMa
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehf-cloud-sagemaker-production-defaults
Create a SageMaker endpoint (real-time, real-time scale-to-zero, or async) with autoscaling, CloudWatch alarms, and tagging enabled by default. Use this skill whenever about to create a SageMaker endpoint, write deployment code that calls createendpoint, or finalize a deployment after the image URI and IAM role are known. Provides deploy.py for real-time endpoints, deployic.py for real-time endpoints that scale to zero instances via inference components, and deployasync.py for async endpoints (a
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehf-cloud-serving-image-selection
Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Lear
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehf-mcp
Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehf-mem
Hugging Face CLI to estimate the required memory to load Safetensors or GGUF model weights for inference from the Hugging Face Hub
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-best
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 benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this ski
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-community-evals
Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .evalresults publication, or community-evals automation.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-gradio
Build Gradio web UIs and demos in Python. Use when creating or editing Gradio apps, components, event listeners, layouts, or chatbots.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-local-models
Use to select models to run locally with llama.cpp and GGUF on CPU, Mac Metal, CUDA, or ROCm. Covers finding GGUFs, quant selection, running servers, exact GGUF file lookup, conversion, and OpenAI-compatible local serving.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-lora-space-builder
Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and diffusers inference recipe, designing a UI tailored t
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-spaces
Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with hf spaces …, @spaces.GPU, Space README frontmatter, or the spaces Python package.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-tool-builder
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. This is especially useful when chaining or combining API calls or the task will be repeated/automated. This Skill creates a reusable script to fetch, enrich or process data.
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
huggingfacehuggingface-zerogpu
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses @spaces.GPU, configuring pythonversion or requirements.txt for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, gr.State semantics across the worker boundary, no torch.compile (use AoTI instead), CUDA wheel-only builds (no nvcc at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this sk
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott
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
- Installazioni
- —
- GitHub Stars
- 11,2K
- Aggiornato
- 30 ott