diff --git a/machine-learning/app/config.py b/machine-learning/app/config.py
index 08eeae7d5f..c48b3278d6 100644
--- a/machine-learning/app/config.py
+++ b/machine-learning/app/config.py
@@ -70,6 +70,8 @@ LOG_LEVELS: dict[str, int] = {
 settings = Settings()
 log_settings = LogSettings()
 
+LOG_LEVEL = LOG_LEVELS.get(log_settings.log_level.lower(), logging.INFO)
+
 
 class CustomRichHandler(RichHandler):
     def __init__(self) -> None:
@@ -81,6 +83,7 @@ class CustomRichHandler(RichHandler):
             console=console,
             rich_tracebacks=True,
             tracebacks_suppress=[*self.excluded, concurrent.futures],
+            tracebacks_show_locals=LOG_LEVEL == logging.DEBUG,
         )
 
     # hack to exclude certain modules from rich tracebacks
@@ -96,7 +99,7 @@ class CustomRichHandler(RichHandler):
 
 
 log = logging.getLogger("ml.log")
-log.setLevel(LOG_LEVELS.get(log_settings.log_level.lower(), logging.INFO))
+log.setLevel(LOG_LEVEL)
 
 
 # patches this issue https://github.com/encode/uvicorn/discussions/1803
diff --git a/machine-learning/app/models/clip.py b/machine-learning/app/models/clip.py
index 608a5fbf03..895786cde1 100644
--- a/machine-learning/app/models/clip.py
+++ b/machine-learning/app/models/clip.py
@@ -144,11 +144,11 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
 
     def _load(self) -> None:
         super()._load()
+        text_cfg: dict[str, Any] = self.model_cfg["text_cfg"]
+        context_length: int = text_cfg.get("context_length", 77)
+        pad_token: int = self.tokenizer_cfg["pad_token"]
 
-        context_length = self.model_cfg["text_cfg"]["context_length"]
-        pad_token = self.tokenizer_cfg["pad_token"]
-
-        size = self.preprocess_cfg["size"]
+        size: list[int] | int = self.preprocess_cfg["size"]
         self.size = size[0] if isinstance(size, list) else size
 
         self.resampling = get_pil_resampling(self.preprocess_cfg["interpolation"])
@@ -157,7 +157,7 @@ class OpenCLIPEncoder(BaseCLIPEncoder):
 
         log.debug(f"Loading tokenizer for CLIP model '{self.model_name}'")
         self.tokenizer: Tokenizer = Tokenizer.from_file(self.tokenizer_file_path.as_posix())
-        pad_id = self.tokenizer.token_to_id(pad_token)
+        pad_id: int = self.tokenizer.token_to_id(pad_token)
         self.tokenizer.enable_padding(length=context_length, pad_token=pad_token, pad_id=pad_id)
         self.tokenizer.enable_truncation(max_length=context_length)
         log.debug(f"Loaded tokenizer for CLIP model '{self.model_name}'")
diff --git a/machine-learning/app/models/constants.py b/machine-learning/app/models/constants.py
index a07c9ed57c..edfe68aa70 100644
--- a/machine-learning/app/models/constants.py
+++ b/machine-learning/app/models/constants.py
@@ -29,6 +29,9 @@ _OPENCLIP_MODELS = {
     "ViT-L-14-quickgelu__dfn2b",
     "ViT-H-14-quickgelu__dfn5b",
     "ViT-H-14-378-quickgelu__dfn5b",
+    "XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k",
+    "nllb-clip-base-siglip__v1",
+    "nllb-clip-large-siglip__v1",
 }
 
 
@@ -37,9 +40,6 @@ _MCLIP_MODELS = {
     "XLM-Roberta-Large-Vit-B-32",
     "XLM-Roberta-Large-Vit-B-16Plus",
     "XLM-Roberta-Large-Vit-L-14",
-    "XLM-Roberta-Large-ViT-H-14__frozen_laion5b_s13b_b90k",
-    "nllb-clip-base-siglip__v1",
-    "nllb-clip-large-siglip__v1",
 }