# Model Card: Mortdecai

![Training Progress](branding/training_progress.svg)

## Model Details

| Field | Value |
|-------|-------|
| **Name** | Mortdecai |
| **Version** | 0.5.0 |
| **Base Model** | Qwen3.5-9B (Apache 2.0) |
| **Adaptation** | QLoRA (4-bit base + LoRA adapters in FP16) |
| **Parameters** | 9.4B total, 29M trainable (0.31%) |
| **Training Hardware** | RTX 3090 Ti (24GB VRAM) |
| **Inference Hardware** | RTX 4000 (16GB), RTX 2080 Ti (11GB), GTX 1660 Super (6GB), or any GPU with 6GB+ |
| **Quantization** | Q4_K_M (5.6GB GGUF) |
| **Context Length** | 4096 tokens (training), 262K tokens (model capability) |
| **License** | Proprietary (adapter + training data). Base model: Apache 2.0 |

## Intended Use

Mortdecai is designed for **Minecraft Java Edition 1.21.x server operations**:

- Translating natural language to valid Minecraft commands
- Controlling an AI God character that responds to player prayers
- Server administration via chat (gamerules, effects, world editing)
- Error correction (self-corrects failed RCON commands)

**Not intended for:**
- General-purpose chat or reasoning
- Other games or non-Minecraft domains
- Safety-critical applications
- Use without the validator safety layer

## Training Data

| Source | Count | Description |
|--------|-------|-------------|
| Hand-curated seed examples | 3,196 | Command syntax, recipes, enchantments, entities, effects, memory, events |
| Tool-calling sequences | 1,430 | Multi-turn RCON execution with 17 tools (script, memory, wiki, plugins) |
| IGLU build dataset | 4,656 | Natural language → block placement commands from Microsoft Research |
| Plugin training (RCON-validated) | 104 | WorldGuard, CoreProtect, EssentialsX, LuckPerms, FAWE |
| Exploration self-play | 150 | Wiki-grounded knowledge discovery with RCON validation |
| Self-play (0.4.0 + 0.5.0) | 2,900+ | Model-generated prompts validated via RCON |
| Live server audit | 8,000+ | Wolf bot + real player interactions from 3 servers |

**Total: ~20,000+ examples across all sources**

### Tool Architecture (17 tools)

| Category | Tools |
|----------|-------|
| Execution | rcon.execute |
| Knowledge | minecraft.wiki_lookup, plugin.docs_lookup, minecraft.changelog_lookup, paper.docs_lookup |
| World Sensing | world.player_info, world.server_state, world.nearby_entities |
| Memory | memory.read, memory.write |
| Scripts | script.write, script.validate, script.execute, script.read, script.list, script.delete, script.schedule |

### Data Collection Methods

1. **Manual curation** — Minecraft Wiki, command reference, recipe databases
2. **Live server logs** — Real player interactions on Paper 1.21.x servers
3. **Bot collection** — Mineflayer bots with Gemini/Dolphin prompt generation
4. **API distillation** — Claude Haiku and Gemini Flash responses
5. **Self-play** — Model generates edge cases, attempts via RCON, learns from results
6. **RCON validation** — Every command tested against a live Minecraft server

### Known Biases

- Training data skewed toward English (~97%) with limited multilingual coverage (3%)
- Command distribution favors `give` and `effect` over complex `execute` chains
- God persona training reflects a specific dramatic character — not neutral
- Player interaction data comes from a small group of testers (< 10 players)
- Self-play data may overrepresent patterns the model is already good at

## Evaluation

### Bake-off Results (0.5.0 vs 0.4.0, 38 prompts × 12 categories)

| Metric | 0.4.0 | 0.5.0 |
|--------|-------|-------|
| Overall success rate | 45.2% | 46.8% |
| Avg response time | 2.60s | 2.11s |
| Errors (crashes) | 2 | 0 |
| Empty responses | 0 | 0 |

**Category improvements (0.5.0 vs 0.4.0):**

| Category | 0.4.0 | 0.5.0 | Change |
|----------|-------|-------|--------|
| Enchantments | 20% | 67% | **+47%** |
| EssentialsX | 0% | 60% | **+60%** |
| Effects | 0% | 25% | **+25%** |
| Basic commands | 75% | 75% | — |
| Teleport | 100% | 100% | — |
| Error recovery | 50% | 50% | — |

### Safety

The model uses a 5-level risk hierarchy:

- **Level 0 (never):** ban, kick, stop, op — hardcoded block in validator
- **Level 1 (refuse):** permanent server state changes
- **Level 2 (warn):** temporary/reversible changes, destructive actions
- **Level 3 (normal):** standard gameplay commands
- **Level 4 (generous):** full enchanted gear, large material stacks

Additional safety layers:
- Validator blocks dangerous commands even if model generates them
- Dangerous effect duration caps (levitation 15s, wither 30s)
- Fall protection (detects lethal teleports)
- Gamerule auto-revert timers

### Limitations

- Cannot determine what a player is looking at (no raycast)
- Limited awareness of world state beyond player position
- Enchantment syntax errors still occur (~15% need validator fixes)
- Empty responses on ~5% of requests
- Thinks in `<think>` blocks that must be stripped (Qwen3 behavior)
- God persona can be unpredictable by design

## Environmental Impact

- **Training energy:** ~84W × 4 hours = 0.34 kWh per training run
- **Inference energy:** ~54W during calls, idle otherwise
- **All compute on consumer GPUs** — no data center resources used