huggingface-best

Query HuggingFace benchmark leaderboards and aggregate model performance data into a device-aware comparison.

Updated Aug 23, 2026

Search Tags:#ai #benchmarks #on-device #ml #model-selection #huggingface #leaderboard

Ready to run

npx skills add https://github.com/domattioli/DomI --skill huggingface-best

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: huggingface-best
Source: https://github.com/domattioli/DomI/tree/main/plugins/huggingface-skills/skills/huggingface-best
Command: npx skills add https://github.com/domattioli/DomI --skill huggingface-best

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Find the best model candidates for a given task by querying official HuggingFace benchmarks, enriching results with model size data, and presenting a clear, device-aware comparison.

Core Features & Use Cases

Gather official leaderboard data for relevant benchmarks and rank candidates by performance.
Enrich results with model size, license, and compatibility with your device constraints, including on-device fp16 budgets and Q4 considerations.
Produce an actionable comparison table for on-device inference or API deployment.
Use case: you need a task-specific model that runs within your hardware limits and want a concise shortlist with licensing details.

Quick Start

Provide a task description and device constraints to receive a ranked model comparison.

Frequently Asked Questions about huggingface-best

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I find the best Hugging Face models for a specific task?▼

To find the best Hugging Face models, query official benchmark leaderboards to rank candidates by performance. This aggregates benchmark scores, model parameters, and licenses into a structured comparison for clear decision-making.

Can I filter AI models by available memory for on-device inference?▼

Yes, you can filter AI models by available memory for on-device inference. The selection applies device-aware filtering against fp16 memory budgets and flags models requiring Q4 quantization to ensure hardware compatibility.

What is the best way to compare model sizes and licenses from Hugging Face benchmarks?▼

The best way to compare model sizes and licenses is to enrich benchmark leaderboard data with parameter counts and licensing details. This produces a markdown-friendly comparison table showing model IDs, scores, and on-device viability.

How do I select models that fit within my device constraints?▼

Select models that fit your device constraints by providing a task description and available memory. The system evaluates fp16 budgets, flags Q4 quantization needs, and returns a tailored shortlist of viable candidates.

Does this model selection approach work for API deployment as well as local execution?▼

Yes, this model selection approach works for API deployment and local execution. It produces an actionable comparison table evaluating on-device inference viability alongside performance scores for deployment decision-making.