huggingface-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/dev
Оригинален текст · няма потвърден превод на българскиПърво прочетете инструкцията.
Файлът се зарежда от посочения източник само по ваше действие. Можете да го прочетете тук, да го копирате или да го запазите като .md.
GitHub хранилище- Звезди на репото
- 11 077
- Лиценз
- Apache-2.0
- Последна проверка на записа
- 20.09.2026 г.
Добавете в своя инструмент.
Командата отваря избора на skills от репозитория. Прочетете файла и условията на автора преди изпълнение.
npx skills add huggingface/skillsСтраницата копира текст. Не инсталира и не изпълнява нищо на устройството ви.
Откъс от SKILL.md
Началото на файла на автора, без превод.
HuggingFace Best Model Finder
Finds the best models for a task by querying official HF benchmark leaderboards, enriching results with model size data, filtering for what fits on the user's device, and returning a comparison table with benchmark scores.
Step 1: Parse the request
Extract from the user's message:
- Task: what they want the model to do (coding, math/reasoning, chat, OCR, RAG/retrieval, speech recognition, image classification, multimodal, agents, etc.)
- Device: hardware constraints (MacBook M-series 8/16/32/64GB unified memory, RTX GPU with VRAM amount, CPU-only, cloud/no constraint, etc.)
If device is not mentioned, skip filtering entirely and return the highest-performing models regardless of size. If the task is genuinely ambiguous, ask one clarifying question.
Device → max parameter budget
When a device is specified, extract its available memory (unified RAM for Apple Silicon, VRAM for discrete GPUs) and apply:
- fp16 max params (B) ≈ memory (GB) ÷ 2
- Q4 max params (B) ≈ memory (GB) × 2
Examples: 16GB → 8B fp16 / 32B Q4 — 24GB VRAM → 12B fp16 / 48B Q4 — 8GB → 4B fp16 / 16B Q4
Step 2: Find relevant benchmark datasets
Fetch the full list of official HF benchmarks:
curl -s -H "Authorization: Bearer $(cat ~/.cache/huggingface/token)" \
"https://huggingface.co/api/datasets?filter=benchmark:official&limit=500" | jq '[.[] | {id, tags, description}]'
Read the returned list and select the datasets most relevant to the user's task — match on dataset id, tags, and description. Use your judgment; don't limit yourself to 2-3. Aim for comprehensive coverage: if 5 benchmarks clearly cover the task, use all 5.
Step 3: Fetch top models from leaderboards
For each selected benchmark dataset:
curl -s -H "Authorization: Bearer $(cat ~/.cache/huggingface/token)" \
"https://huggingface.co/api/datasets/<namespace>/<repo>/leaderboard" | jq '[.[:15] | .[] | {rank, modelId, value, verified}]'
Collect model IDs and scores across all benchmarks. If a leaderboard returns an error (404, 401, etc.), skip it and note it in the output.
Step 4: Enrich with model metadata
For the top 10-15 candidate model IDs, get model infos.