diff --git a/py/routes/handlers/hf_handlers.py b/py/routes/handlers/hf_handlers.py
index e87016a4..24a3f23e 100644
--- a/py/routes/handlers/hf_handlers.py
+++ b/py/routes/handlers/hf_handlers.py
@@ -122,12 +122,8 @@ async def _save_hf_metadata(dest_path: str, repo: str, model_root: str) -> None:
         metadata._unknown_fields["hf_url"] = hf_url
         metadata.from_civitai = False  # HF models are not from CivitAI
 
-        metadata_dict = metadata.to_dict()
-        if "trainedWords" in metadata_dict and not metadata_dict["trainedWords"]:
-            del metadata_dict["trainedWords"]
-
         # 3. Save metadata atomically
-        await MetadataManager.save_metadata(dest_path, metadata_dict)
+        await MetadataManager.save_metadata(dest_path, metadata)
         logger.info("Saved HF metadata (with hf_url) for %s", dest_path)
 
         # 4. Determine relative folder path for cache
diff --git a/py/services/agent/agent_service.py b/py/services/agent/agent_service.py
index c7d39e4f..89e792c1 100644
--- a/py/services/agent/agent_service.py
+++ b/py/services/agent/agent_service.py
@@ -407,7 +407,6 @@ class AgentService:
             "base_model": metadata.get("base_model", ""),
             "tags": metadata.get("tags", []),
             "modelDescription": metadata.get("modelDescription", ""),
-            "trainedWords": metadata.get("trainedWords", []),
             "sha256": (metadata.get("sha256") or "")[:16] + "..." if metadata.get("sha256") else "",
             "size": metadata.get("size", 0),
         }
diff --git a/py/utils/models.py b/py/utils/models.py
index cfe96585..8c14b626 100644
--- a/py/utils/models.py
+++ b/py/utils/models.py
@@ -77,9 +77,6 @@ class BaseModelMetadata:
     last_checked_at: float = 0  # Last checked timestamp
     hash_status: str = "completed"  # Hash calculation status: pending | calculating | completed | failed
     autov3: Optional[str] = None  # CivitAI AutoV3 hash (12-char lowercase hex); "" = checked but unavailable, None = not checked
-    trainedWords: List[str] = field(
-        default_factory=list
-    )  # Trigger words / activation prompts (source-agnostic)
     _unknown_fields: Dict[str, Any] = field(
         default_factory=dict, repr=False, compare=False
     )  # Store unknown fields
@@ -92,9 +89,6 @@ class BaseModelMetadata:
         if self.tags is None:
             self.tags = []
 
-        if self.trainedWords is None:
-            self.trainedWords = []
-
     @classmethod
     def from_dict(cls, data: Dict[str, Any]) -> "BaseModelMetadata":
         """Create instance from dictionary"""
diff --git a/tests/enrich_hf_validation/evaluation_engine.py b/tests/enrich_hf_validation/evaluation_engine.py
index 9f3fe153..74181e85 100644
--- a/tests/enrich_hf_validation/evaluation_engine.py
+++ b/tests/enrich_hf_validation/evaluation_engine.py
@@ -100,7 +100,7 @@ def evaluate_model(
     flagged issues.
     """
     civitai = metadata.get("civitai") or {}
-    trained_words: List[str] = civitai.get("trainedWords") or metadata.get("trainedWords") or []
+    trained_words: List[str] = civitai.get("trainedWords") or []
     short_desc: str = civitai.get("description") or ""
     tags: List[str] = metadata.get("tags") or []
     notes: str = metadata.get("notes") or ""
diff --git a/tests/enrich_hf_validation/metadata_constructor.py b/tests/enrich_hf_validation/metadata_constructor.py
index 17ac9517..feeef367 100644
--- a/tests/enrich_hf_validation/metadata_constructor.py
+++ b/tests/enrich_hf_validation/metadata_constructor.py
@@ -149,7 +149,6 @@ def create_initial_metadata(
         "metadata_source": "",
         "last_checked_at": 0,
         "hash_status": "completed",
-        "trainedWords": [],
         "hf_url": hf_url,
         "usage_tips": "{}",
     }
diff --git a/tests/services/test_post_processor.py b/tests/services/test_post_processor.py
index 3779fe30..8da7a431 100644
--- a/tests/services/test_post_processor.py
+++ b/tests/services/test_post_processor.py
@@ -164,7 +164,7 @@ class TestEnrichHfMetadata:
                 skill_name="enrich_hf_metadata",
                 model_path="/p.safetensors",
                 llm_output=llm,
-                metadata={"trainedWords": []},
+                metadata={},
             )
         applied = mock_apply.call_args[0][1]
         assert applied["civitai"]["trainedWords"] == ["trigger1", "trigger2"]