Develarper
A two-tier LLM router built solo for the AMD Developer Hackathon (Act II). A local Qwen 2.5 3B classifier scores each task's difficulty and dispatches the hard ones to a cloud API while the easy ones stay on the local model. Tested against a 19-task harness.
Provenance
- Role
- Solo — LLMOps
- Period
- July 2026
- Built at
- AMD Developer Hackathon, Act II
- Stack
- Python · Qwen 2.5 3B via Ollama · FastAPI · Fireworks
Measured
- Correct tier routing
- 18 of 19tasks, scored against self-defined labels
- Ran on the local model
- ~80%a TASK count, not tokens — the cloud-routed fifth were the hard tasks, carrying longer prompts and longer completions
Architecture Pattern
Two-tier router with a local difficulty classifier
A local Qwen 2.5 3B Instruct model served through Ollama classifies incoming task difficulty and emits its routing decision as structured JSON; the service layer is FastAPI, and the cloud tier is Fireworks, with per-call usage telemetry. A dedicated BERT encoder classifier was considered and rejected: holding a second resident model would not fit under the harness's memory ceiling, so the router reuses the generative model already loaded rather than paying for a specialist one.