Mortdecai
da8f557219
GPU Scheduler (gpu.sethpc.xyz): - Live dashboard with 4 GPUs, training monitor, loss sparklines - Preset-based job scheduler with 3 triggers (time, finish_training, cost) - Model selection per GPU, pipeline configuration - Tool self-play and training pipeline types - Behind Google OAuth, live-refresh without page reload Tool Architecture (14 tools): - 3 new tools: world.nearby_entities, memory.read, memory.write - 7 script.* tools: write, validate, execute, read, list, delete, schedule - ScriptManager: full mcfunction datapack CRUD with RCON validation - Training data: 1,430 tool examples (up from 1,159) Plugin Deployment (paper-ai-25567): - WorldGuard 7.0.12, CoreProtect CE 23.1, EssentialsX 2.21.2, Vault 1.7.3 - Fresh greenfield world reset - 104 RCON-validated plugin training examples Event Dispatcher: - Watches server log for deaths, joins, advancements, PvP kills - Configurable trigger probability and cooldowns per event type - Deployed to dev server, fires god_system prompts on events - 21 event-response training examples Training Infrastructure: - train_lora.py: --save-steps 50, --resume from checkpoint - run_training.sh: stops Ollama, activates conda, restarts after - Passwordless sudo for ollama services on steel141 - Dev server added to MCSManager with autoStart Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
59 lines
1.3 KiB
JSON
59 lines
1.3 KiB
JSON
{
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"a78c48c1": {
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"id": "a78c48c1",
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"name": "Everyone Test",
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"pipeline": "self_play",
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"gpus": [
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"3090ti",
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"2080ti",
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"rtx4000"
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],
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"params": {
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"model": "mortdecai:0.4.0",
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"tiers": "1,2,3",
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"rounds_per_tier": "50",
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"rcon_host": "192.168.0.244",
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"rcon_port": "25578",
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"rcon_pass": "REDACTED_RCON"
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},
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"created_at": "2026-03-21T01:18:14Z"
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},
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"06356764": {
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"id": "06356764",
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"name": "Infer during training",
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"pipeline": "self_play",
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"gpus": [
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"rtx4000"
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],
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"params": {
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"model": "mortdecai:0.4.0",
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"tiers": "1,2,3",
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"rounds_per_tier": "50",
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"rcon_host": "192.168.0.244",
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"rcon_port": "25578",
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"rcon_pass": "REDACTED_RCON"
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},
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"created_at": "2026-03-21T01:37:31Z"
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},
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"9cc95c0a": {
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"id": "9cc95c0a",
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"name": "Train",
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"pipeline": "training",
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"gpus": [
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"3090ti",
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"2080ti"
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],
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"params": {
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"base_model": "Qwen/Qwen3.5-9B",
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"dataset": "auto",
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"output_name": "mortdecai-0.5.0",
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"epochs": "1",
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"lr": "0.0001",
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"batch_size": "2",
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"grad_accum": "4",
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"max_seq_len": "2048",
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"save_steps": "50"
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},
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"created_at": "2026-03-21T01:37:57Z"
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}
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} |