Fix training script: bf16 for Ampere GPU, add system prompts to training data

- 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>

training/scripts/train_lora.py

@@ -18,8 +18,39 @@ import os
 from pathlib import Path
 
 
+def determine_mode(example: dict) -> str:
+    """Determine prompt mode from the example."""
+    query = example["input"]["user_message"]
+    eid = example.get("id", "")
+    if query.lower().startswith("pray "):
+        return "god"
+    elif eid.startswith("negative-") and "god" in query.lower():
+        return "god_system"
+    elif example.get("source") == "prayer_log":
+        return "god"
+    return "sudo"
+
+
+def get_system_prompt(mode: str) -> str:
+    """Get the system prompt for training. Import from project if available, fallback to inline."""
+    try:
+        import sys
+        script_dir = Path(__file__).resolve().parent
+        project_root = script_dir.parent.parent
+        sys.path.insert(0, str(project_root))
+        from agent.prompts.system_prompts import get_prompt
+        return get_prompt(mode)
+    except ImportError:
+        # Minimal fallback prompts
+        if mode == "god":
+            return "You are God in a Minecraft server. Return JSON: {\"message\": \"...\", \"commands\": [...], \"reasoning\": \"...\"}"
+        elif mode == "god_system":
+            return "You are God performing an unprompted intervention. Return JSON: {\"message\": \"...\", \"commands\": [...]}"
+        return "You are a Minecraft 1.21 command translator. Return JSON: {\"commands\": [...], \"reasoning\": \"...\"}"
+
+
 def load_dataset(path: str) -> list:
-    """Load seed dataset and format for SFT training."""
+    """Load seed dataset and format for SFT training with system prompts and mode awareness."""
     examples = []
     with open(path) as f:
         for line in f:
@@ -27,6 +58,10 @@ def load_dataset(path: str) -> list:
                 continue
             ex = json.loads(line)
 
+            # Determine mode and get system prompt
+            mode = determine_mode(ex)
+            system_prompt = get_system_prompt(mode)
+
             # Build the training conversation
             inp = ex["input"]
             out = ex["output"]
@@ -48,11 +83,14 @@ def load_dataset(path: str) -> list:
             response = {
                 "reasoning": out.get("reasoning", ""),
                 "commands": out.get("commands", []),
-                "message": out.get("message"),
             }
+            # Include message field for god modes
+            if mode in ("god", "god_system"):
+                response["message"] = out.get("message") or ""
 
             examples.append({
                 "conversations": [
+                    {"role": "system", "content": system_prompt},
                     {"role": "user", "content": user_msg},
                     {"role": "assistant", "content": json.dumps(response)},
                 ]
@@ -158,7 +196,7 @@ def main():
         lr_scheduler_type="cosine",
         warmup_ratio=0.1,
         weight_decay=0.01,
-        fp16=True,
+        bf16=True,
         logging_steps=1,
         save_strategy="epoch",
         seed=42,