The Context Course

You are coordinating local llama.cpp inference optimization in this project using Pi and pi-subagents.

Arguments:

• target: $1 if provided, otherwise current running llama-server endpoint
• max candidates: $2 if provided, otherwise 3

Read:

• AGENTS.md
• README.md
• docs/pi-subagents-guide.md
• .agents/skills/huggingface-local-models/SKILL.md
• research/results.tsv
• research/notes.md
• research/do-not-repeat.md

Use Pi project agents:

• benchmarker for baseline and candidate measurements
• optimizer for one-variable speed candidates

First ask benchmarker to measure a reproducible baseline for the target. Then ask optimizer for up to the requested max candidates. For the best candidate, ask benchmarker to run the same benchmark and append the result to research/results.tsv.

Keep all comparisons honest:

• same model and exact GGUF unless the change is explicitly quant/model choice
• same prompt and max_tokens
• same context
• same llama.cpp build/backend
• one changed speed variable per benchmark