OpenAmer Fine-Tune Plan (the organism learns its own brain)

A step-by-step, honest plan to go from collected activity data → a curated dataset → an OpenAmer-specific model replica. Do NOT promise "superintelligence in a day"; this is a real but incremental pipeline.

When to use

• You've accumulated enough ~/.openamer/a2a/*.jsonl (activity/trajectories) and mesh insights that a specialist model would generalize.
• You want a "distilled OpenAmer brain" that encodes the swarm's learned skills and style (tool-calling, concise output, privacy-first defaults).

The pipeline (data → curated → train → evaluate → deploy)

1. Collect (already built)

Every node already logs activity:

~/.openamer/a2a/activity.jsonl        # brainlog (autolog ON by default)
~/.openamer/a2a/openamer-brain.jsonl  # `a2a brain collect` output (ChatML)
~/.openamer/MEMORY-official-mesh.md   # adopted mesh insights

Aggregate across nodes (scp/rsync or the GitHub directory/a2a/ for shared curated insights). Only ever train on redacted data (privacy.redact runs at write time; re-verify before training).

2. Curate (klein aber fein)

• De-dupe by digest() (already in braindata).
• Drop secrets and near-duplicate tool logs (keep structure, drop PII).
• Balance: prefer turns with tool calls + thinking + final answer; cap at ~50 messages per sample.

3. Prepare ChatML / messages format

Already produced by brain collect: JSONL {"messages":[...,"engine":...]} with system/user/thinking/tool/assistant roles. Perfect for:

• Supervised fine-tune (SFT) with standard tool-call templates, or
• LoRA/QLoRA on limited VRAM (your RTX 3060 Ti 8GB fits a 7B-ish with QLoRA).

Tools (already in OpenAmer skills/ecosystem): huggingface-hub skill, llama-cpp skill for GGUF inference. Use libraries like trl, peft, unsloth/torchtune, or mlx (Apple).

4. Train

• Split train/valid (~95/5). Evaluate on a held-out set of hoped-for tasks (tool use, short answers, refusal of private-data prompts).
• Start from a strong base (e.g. a capable chat model) to avoid forgetting general ability; LoRA on the collected data only.
• Metrics: loss on validation + a small human-read gold set.

5. Evaluate honestly

• Test on: tool-call correctness, privacy refusal (must never echo a phone), and its own system-prompt knowledge.
• Compare vs base model. If it's not clearly better on the target slice, don't ship it — keep the base.

6. Deploy

• Export GGUF (llama.cpp skill) for local; or the standard checkpoint.
• openamer model points to the new endpoint/file. Keep the model name distinct (e.g. openamer/...brain-v1).

Pitfalls

• Data hygiene first. One leaked private string poisons trust. Run openamer_cli.a2a.privacy.redact + scan before training.
• Curation > volume. 10k clean, tool-rich samples beat 1M dumps.
• Don't overfit the swarm's niche — you'll lose general usefulness. LoRA, low rank, modest epochs.
• Neither promise instant superintelligence — this is incremental; each version generalizes cheaply if the base is strong.

Verify

openamer a2a brain collect --out brain.jsonl    # dataset exists, JSONL valid
python -c "import json; [json.loads(l) for l in open('brain.jsonl')]"  # all parse
openamer security check                          # privacy posture before publishing