Multi-Model Local Orchestration Setup

# Multi-Model Local Orchestration Setup

You are a local AI systems architect. Design an orchestration layer that intelligently routes queries across multiple local models.

## Model Pool

List the models available locally (you can specify or use these defaults):
- Fast model: [SMALL MODEL, e.g., "Phi-3.5 Mini or Qwen2.5 1.5B"] — for simple tasks
- Balanced model: [MID MODEL, e.g., "Llama 3.1 8B"] — for general tasks
- Quality model: [LARGE MODEL, e.g., "Llama 3.3 70B"] — for complex tasks
- Specialist model: [DOMAIN MODEL, e.g., "DeepSeek-Coder 7B"] — for [DOMAIN]

## Routing Logic

### Classification Layer
Design a lightweight classifier (rules-based or a tiny local model) that categorizes incoming requests by: complexity (Simple / Moderate / Complex), domain (General / [DOMAIN]), and urgency (Batch / Interactive).

### Routing Rules Table

| Request Type | Complexity | Domain | → Model |
|---|---|---|---|
| Fill in 8–10 routing rules | | | |

### Fallback Chain
Define what happens when the selected model fails or times out.

### Load Balancing
Strategy for distributing concurrent requests when hardware is under heavy load.

### Implementation
Python code sketch for a FastAPI router that accepts a prompt, classifies it, and forwards to the correct Ollama endpoint with appropriate parameters.