107 lines
4.0 KiB
Markdown
107 lines
4.0 KiB
Markdown
# Model Card: Mortdecai
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## Model Details
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| Field | Value |
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|-------|-------|
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| **Name** | Mortdecai |
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| **Version** | 0.4.0 |
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| **Base Model** | Qwen3.5-9B (Apache 2.0) |
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| **Adaptation** | QLoRA (4-bit base + LoRA adapters in FP16) |
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| **Parameters** | 9.4B total, 29M trainable (0.31%) |
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| **Training Hardware** | RTX 3090 Ti (24GB VRAM) |
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| **Inference Hardware** | RTX 4000 (16GB), RTX 2080 Ti (11GB), or any GPU with 6GB+ VRAM |
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| **Quantization** | Q4_K_M (5.3GB GGUF) |
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| **Context Length** | 4096 tokens (training), 262K tokens (model capability) |
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| **License** | Proprietary (adapter + training data). Base model: Apache 2.0 |
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## Intended Use
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Mortdecai is designed for **Minecraft Java Edition 1.21.x server operations**:
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- Translating natural language to valid Minecraft commands
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- Controlling an AI God character that responds to player prayers
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- Server administration via chat (gamerules, effects, world editing)
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- Error correction (self-corrects failed RCON commands)
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**Not intended for:**
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- General-purpose chat or reasoning
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- Other games or non-Minecraft domains
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- Safety-critical applications
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- Use without the validator safety layer
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## Training Data
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| Source | Count | Description |
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|--------|-------|-------------|
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| Hand-curated examples | 966 | Command syntax, recipes, enchantments, entities, effects |
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| Player interactions | 654 | Real prayers from live server players |
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| Sudo translations | 525 | Natural language → command pairs |
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| Tool-calling sequences | 1,159 | Multi-turn RCON execution with error correction |
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| Self-play | 5,000+ | Model-generated prompts validated via RCON |
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| API distillation | 344 | Claude Haiku gold-standard responses |
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| Error corrections | 150+ | Wrong → right command pairs |
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**Total: ~8,400+ examples**
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### Data Collection Methods
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1. **Manual curation** — Minecraft Wiki, command reference, recipe databases
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2. **Live server logs** — Real player interactions on Paper 1.21.x servers
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3. **Bot collection** — Mineflayer bots with Gemini/Dolphin prompt generation
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4. **API distillation** — Claude Haiku and Gemini Flash responses
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5. **Self-play** — Model generates edge cases, attempts via RCON, learns from results
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6. **RCON validation** — Every command tested against a live Minecraft server
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### Known Biases
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- Training data skewed toward English (~97%) with limited multilingual coverage (3%)
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- Command distribution favors `give` and `effect` over complex `execute` chains
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- God persona training reflects a specific dramatic character — not neutral
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- Player interaction data comes from a small group of testers (< 10 players)
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- Self-play data may overrepresent patterns the model is already good at
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## Evaluation
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### Bake-off Results (0.4.0, 2,397 test cases)
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| Metric | Score |
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|--------|-------|
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| Command match | 75.5% |
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| Exact match | 22.9% |
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| Syntax correct | 80.5% |
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| Safety compliance | 99.7% |
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| No gratuitous tp | 98.5% |
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| Avg latency | 4.0s |
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### Safety
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The model uses a 5-level risk hierarchy:
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- **Level 0 (never):** ban, kick, stop, op — hardcoded block in validator
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- **Level 1 (refuse):** permanent server state changes
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- **Level 2 (warn):** temporary/reversible changes, destructive actions
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- **Level 3 (normal):** standard gameplay commands
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- **Level 4 (generous):** full enchanted gear, large material stacks
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Additional safety layers:
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- Validator blocks dangerous commands even if model generates them
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- Dangerous effect duration caps (levitation 15s, wither 30s)
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- Fall protection (detects lethal teleports)
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- Gamerule auto-revert timers
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### Limitations
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- Cannot determine what a player is looking at (no raycast)
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- Limited awareness of world state beyond player position
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- Enchantment syntax errors still occur (~15% need validator fixes)
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- Empty responses on ~5% of requests
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- Thinks in `<think>` blocks that must be stripped (Qwen3 behavior)
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- God persona can be unpredictable by design
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## Environmental Impact
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- **Training energy:** ~84W × 4 hours = 0.34 kWh per training run
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- **Inference energy:** ~54W during calls, idle otherwise
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- **All compute on consumer GPUs** — no data center resources used
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