/huggingface-best

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 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 skill when the user wants
  model recommendations or comparisons, even if they don't explicitly mention HuggingFace
  or benchmarks.

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.

# REST API
curl -s -H "Authorization: Bearer $(cat ~/.cache/huggingface/token)" \
  "https://huggingface.co/api/models/org/model1" | jq '{safetensors, tags, cardData}'

# CLI (hf-cli)
hf models info org/model1 --json | jq '{safetensors, tags, cardData}'

Extract from each response:

• **Parameters**: `safetensors.total` → convert to B (e.g., 7_241_748_480 → "7.2B")
• **License**: from model card tags (look for `license:apache-2.0`, `license:mit`, etc.)
• If `safetensors` is absent, parse size from the model name (look for "7b", "8b", "13b", "70b", "72b", etc.)

---

Step 5: Filter and rank

**If a device was specified:** 1. Remove models exceeding the fp16 parameter budget for the device 2. Flag models that fit only with Q4 quantization (multiply budget by ~4 for Q4 capacity) 3. If a highly-ranked model is slightly over budget, keep it with a "needs Q4" note — don't silently drop it

**If no device was mentioned:** skip all size filtering — just rank by benchmark score.

Then: rank by benchmark score (descending), keep top 5-8 models.

Include proprietary models (GPT-4, Claude, Gemini) if they appear on leaderboards, but flag them as "API only / not self-hostable". If the user explicitly asked for local/open models only, exclude them.

---

Step 6: Output

Comparison table

| # | Model | Params | [Benchmark 1] | [Benchmark 2] | License | On device |
|---|-------|--------|--------------|--------------|---------|-----------|
| ⭐1 | [org/name](https://huggingface.co/org/name) | 7B | 85.2% | — | Apache 2.0 | Yes (fp16) |
| 2 | [org/name](https://huggingface.co/org/name) | 13B | 83.1% | 71.5% | MIT | Q4 only |
| 3 | [org/name](https://huggingface.co/org/name) | 70B | 90.0% | 81.0% | Llama | Too large |

• Link model names to `https://huggingface.co/<model_id>`
• Use `—` for benchmarks where the model wasn't evaluated
• Star the top recommended pick with ⭐
• "On device" values: `Yes (fp16)`, `Q4 only`, `Too large`, `API only`

Follow-up

After presenting the table, ask the user: "Would you like to run **[top recommended model]**?"

If they say yes, ask whether they'd prefer to:

• **Run locally** — ask about their device if not already known, then give appropriate setup instructions
• **Run on HF Jobs** — point them to the HF Jobs guide: https://huggingface.co/docs/huggingface_hub/en/guides/jobs

---

Error handling

• **Leaderboard not found**: skip, note "leaderboard unavailable" in output
• **Model missing from hub_repo_details**: fall back to parsing size from model name
• **No benchmarks found for task**: use the curated fallback table above, or try `hub_repo_search` with `filters=["<task>"]` sorted by `trendingScore`
• **All leaderboards fail**: fall back to `hub_repo_search` for popular models tagged with the task, note that results are by popularity rather than benchmark score