Prayer bots (ingame/prayer_bots.js):
- 3 Mineflayer bots that actively pray, sudo, and bug_log on dev server
- Gemini 2.5 Flash Lite generates diverse natural prompts on the fly
- Falls back to static pool if Gemini unavailable
- 15-45s interval per bot, 50/35/10/5 pray/sudo/bug/chat split
POS status printer (scripts/training_status_printer.py):
- Prints training data collection status to Epson TM-m30
- Tracks: dataset size, audit logs, bot activity, Gemini API cost, service status
- Triggers on $0.50 cost threshold (configurable), checks every 15 min
- --dry-run, --check, --force flags
Training:
- First LoRA run completed (233 examples, 3 epochs, loss 1.5→0.10)
- GGUF exported and loaded into Ollama as qwen3-8b-mc-lora on steel141
- Model is bad (expected) — hallucinating Chinese, leaking system prompt
- Deployed to dev server for live testing and data collection
- bf16 fix for Ampere GPU, system prompts included in training conversations
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Switch fp16 to bf16 (RTX 3090 Ti is Ampere, supports BF16 natively)
- Include system prompt in training conversations (mode-aware: sudo/god/god_system)
- Include message field only for god modes
- Add determine_mode() and get_system_prompt() helpers
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Expanded dataset from 31 to 182 examples (45 manual + 106 extracted from server logs)
- Built eval/harness.py with per-category breakdowns and baseline tracking
- Built eval/live_bakeoff.py for RCON-verified model comparison on live server
- Extracted training data from prayer logs, sudo logs, and bug reports on CT 644
- Added Reddit post draft and modmail for playtester recruitment
- Updated server context: all servers now online-mode=false + whitelist
- Updated PLAN.md with Phase 2 progress
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Key decisions: gemma3n:e4b for serving (RTX 4000), qwen3:8b for
fine-tuning base (RTX 3090 Ti). Phase 1 complete.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- training/scripts/train_lora.py: Unsloth QLoRA trainer for qwen3:8b
- training/scripts/train_lora.sh: Launch script for steel141 RTX 3090 Ti
- eval/bakeoff.py: Fixed token budget (400->1500) that caused qwen3
models to exhaust tokens on thinking, added --no-think flag
- agent/serve.py: Default model changed to gemma3n:e4b
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
gemma3n:e4b wins for production serving (80.6% cmd match, 100% safety).
qwen3:8b recommended as fine-tuning base. Full per-model analysis and
scoring methodology documented.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Bake-off tested 7 models on 31 seed examples via GPU-accelerated Ollama
on node-197 RTX 4000. gemma3n:e4b leads for serving (80.6% cmd match,
100% safety, 5.9s). qwen3:8b recommended as fine-tuning base (Apache 2.0,
best syntax quality, strong ecosystem). Full research in MODEL_RESEARCH.md.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- knowledge/mc-commands/commands.json: 14 MC commands with JE syntax, args, examples, common errors, 1.21 version notes
- knowledge/server-context/servers.json: all 4 servers (mc1, shrink, paper-ai, paper-dev) with full config
- knowledge/build_index.py: TF-IDF indexer + search function (19 docs, 725 terms)
- All command syntax validated live on dev server via RCON (12/13 passed)
- PLAN.md: mark Phase 1.3 complete
- IDEA.md: project scope (Minecraft ops AI assistant via qwen3-coder LoRA/SFT)
- PLAN.md: complete roadmap with prior art analysis, architecture, phased plan, dev server docs
- data/schema.json: training example JSON Schema with negative_output support
- data/processed/seed_dataset.jsonl: 31 validated examples from repair code, prayer logs, session history
- data/validate_dataset.py: schema validator with summary statistics
- ingame/: Mineflayer bot framework (test_connect, spawn_bots, aware_bots with full event logging)
- Directory structure for knowledge/, eval/, training/, agent/ (Phase 1.3+ work)
Seth