ai-model-selector

name	ai-model-selector
description	Quick guidance on choosing AI models (LLM/VLM/Embedding) based on task, VRAM, cost, and quality requirements
short_desc	choose LLM/VLM/embedding model by task + VRAM
keywords	["model selection","which model","pick a model","choose a model","best model for","Qwen","Llama","Gemma","Mistral","VRAM budget","model cost","model latency"]
model	sonnet

AI Model Selector (Sonnet)

Purpose: Quick guidance on choosing AI models (LLM/VLM/Embedding) based on task, VRAM, cost, and quality requirements.

Model: Sonnet 4.5 (balanced reasoning for model comparison)

When to Invoke Autonomously:

Use this skill when:

1. Starting AI Project: "Which model should I use for [task]?"
2. VRAM Constraints: "What fits in 16GB VRAM?"
3. Quality vs Speed: "Need faster inference, which smaller model?"
4. Multi-Model Choice: Embedding model, VLM, or LLM for task?
5. Quantization Decision: "Should I quantize, and to what level?"

DO NOT invoke for:

• Already committed to specific model
• Using enterprise/API models (not self-hosted)
• Model choice is non-critical (prototyping)

Decision Tree

Need to choose:
├─ LLM for text generation? → Use this skill
├─ VLM for image understanding? → Use this skill
├─ Embedding model for RAG? → Use this skill
├─ Already decided on model? → Don't use this skill
└─ Using API (OpenAI/Anthropic)? → Don't use this skill

Usage

/ai-model-selector llm [task] [VRAM limit]
/ai-model-selector vlm [task] [VRAM limit]
/ai-model-selector embedding [use case]
/ai-model-selector compare [model1] vs [model2]

What This Skill Does

1. Task-Based Recommendations

Provides model recommendations for:

• Text Generation (LLMs): Code generation, chat, creative writing, instruction following
• Vision Understanding (VLMs): Document OCR, general vision, chart analysis, fast inference
• Embeddings: General RAG, code search, multilingual search

For specific model recommendations by task, see examples/good-use-cases.md.

2. VRAM Calculation

Estimates VRAM requirements based on:

• Base formula: model_params × bytes_per_param × overhead_factor
• Quantization impact (Q4_K_M ~4GB, Q5_K_M ~5GB per 7B params)
• Context length overhead
• Batch size multipliers

3. Quality-Speed-VRAM Tradeoffs

Analyzes tradeoffs across quality tiers:

• Expert (70B+): Best quality, 48GB+ VRAM
• High (30-34B): Very good, 24GB VRAM
• Balanced (7-14B): Good quality, 8-16GB VRAM
• Fast (1-3B): Acceptable, 4-6GB VRAM

4. Quantization Recommendations

Guidance on quantization levels:

• Q4_K_M: Best balance (4-5% quality loss, 40% VRAM savings)
• Q5_K_M: High quality (2-3% loss)
• Q8_0: Near-FP16 quality (1% loss)

Output Format

See template.md for complete model recommendation structure.

Quick Workflow Reference

Before implementing: Search for proven patterns

.claude/scripts/kg-search search "llm" --type models

For deep research: Ask user "Use hybrid_search to research [model selection]"

Development env: Python 3.12, Weaviate:8081, Ollama:11435, venv: source claude_mcp_servers/.venv/bin/activate

Integration with Knowledge Graph

After model selection:

1. Document choice in project node
2. Link to model spec node (create if needed)
3. Capture benchmarks and real-world performance
4. Tag with use case and hardware requirements

Supporting Files

• Use Cases: See examples/good-use-cases.md and examples/bad-use-cases.md
• Template: Use template.md for structured model recommendations

Success Metrics

This skill is working well if:

• ✅ Recommended model fits VRAM constraints
• ✅ Quality meets user requirements
• ✅ Speed is acceptable for use case
• ✅ User doesn't need to switch models later
• ✅ Quantization recommendations are accurate