| name | huggingface-best |
| description | 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... |
| risk | unknown |
| source | https://github.com/huggingface/skills/tree/main/skills/huggingface-best |
| source_repo | huggingface/skills |
| source_type | official |
| date_added | 2026-07-01T00:00:00.000Z |
| license | Apache-2.0 |
| license_source | https://github.com/huggingface/skills/blob/main/LICENSE |
HuggingFace Best Model Finder
When to Use
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...
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
