Fix OOM: skip AutoConfig.from_pretrained hub check at startup

Calling is_model_on_hub (AutoConfig.from_pretrained) for every open-source
model at startup caused the Space to be OOM-killed (exit code 137).
Since result files only exist for valid models, set still_on_hub=True
unconditionally and read architecture from JSON config instead.

src/leaderboard/read_evals.py

@@ -9,7 +9,6 @@ import numpy as np
 
 from src.display.formatting import make_clickable_model
 from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
-from src.submission.check_validity import is_model_on_hub
 
 
 @dataclass
@@ -56,19 +55,13 @@ class EvalResult:
             result_key = f"{org}_{model}_{precision.value.name}"
         full_model = "/".join(org_and_model)
 
-        # Proprietary API models are not on HF Hub; mark them as available directly
+        # If we have result files for a model it is valid; skip expensive hub check
+        # (AutoConfig.from_pretrained for many models causes OOM on HF Spaces free tier)
+        still_on_hub = True
         if config.get("api_model", False):
-            still_on_hub = True
             architecture = "API"
         else:
-            still_on_hub, _, model_config = is_model_on_hub(
-                full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
-            )
-            architecture = "?"
-            if model_config is not None:
-                architectures = getattr(model_config, "architectures", None)
-                if architectures:
-                    architecture = ";".join(architectures)
+            architecture = config.get("architecture", "?")
 
         # Extract results available in this file (some results are split in several files)
         results = {}