update utils.py

src/display/utils.py

@@ -39,30 +39,16 @@ COLUMNS.append(
 )
 
 # Include per-subject accuracy columns based on your subjects
-# Remove unwanted subjects from here
 for task in Tasks:
-    # Exclude unwanted tasks
-    if task.name not in [
-        "History",
-        "Mathematics",
-        "Science",
-        "Geography",
-        "Literature",
-        "Art",
-        "Physics",
-        "Chemistry",
-        "Biology",
-        "Computer_Science",
-    ]:
-        COLUMNS.append(
-            ColumnContent(
-                name=task.value.benchmark,
-                type=float,
-                label=f"{task.value.col_name} (%)",
-                description=f"Accuracy on {task.value.col_name}",
-                displayed_by_default=True,  # Set to True to display by default
-            )
+    COLUMNS.append(
+        ColumnContent(
+            name=task.value.benchmark,
+            type=float,
+            label=f"{task.value.col_name} (%)",
+            description=f"Accuracy on {task.value.col_name}",
+            displayed_by_default=False,
         )
+    )
 
 # Additional columns
 COLUMNS.extend([
@@ -71,47 +57,126 @@ COLUMNS.extend([
         type=str,
         label="Model Type",
         description="Type of the model (e.g., Transformer, RNN, etc.)",
-        displayed_by_default=True,  # Set to True to display by default
+        displayed_by_default=False,
+    ),
+    ColumnContent(
+        name="architecture",
+        type=str,
+        label="Architecture",
+        description="Model architecture",
+        displayed_by_default=False,
     ),
     ColumnContent(
         name="weight_type",
         type=str,
         label="Weight Type",
         description="Type of model weights (e.g., Original, Delta, Adapter)",
-        displayed_by_default=True,  # Set to True to display by default
+        displayed_by_default=False,
     ),
     ColumnContent(
         name="precision",
         type=str,
         label="Precision",
         description="Precision of the model weights (e.g., float16)",
-        displayed_by_default=True,  # Set to True to display by default
+        displayed_by_default=False,
     ),
     ColumnContent(
         name="license",
         type=str,
         label="License",
         description="License of the model",
-        displayed_by_default=True,  # Set to True to display by default
+        displayed_by_default=False,
+    ),
+    ColumnContent(
+        name="params",
+        type=float,
+        label="Parameters (B)",
+        description="Number of model parameters in billions",
+        displayed_by_default=False,
     ),
     ColumnContent(
         name="likes",
         type=int,
         label="Likes",
         description="Number of likes on the Hugging Face Hub",
-        displayed_by_default=True,  # Set to True to display by default
+        displayed_by_default=False,
     ),
     ColumnContent(
         name="still_on_hub",
         type=bool,
         label="Available on the Hub",
         description="Whether the model is still available on the Hugging Face Hub",
-        displayed_by_default=True,  # Set to True to display by default
+        displayed_by_default=False,
+    ),
+    ColumnContent(
+        name="revision",
+        type=str,
+        label="Model Revision",
+        description="Model revision or commit hash",
+        displayed_by_default=False,
     ),
 ])
 
 # Now we can create lists of column names for use in the application
 COLS = [col.name for col in COLUMNS]
-BENCHMARK_COLS = [col.name for col in COLUMNS if col.name not in [
-    "model", "average", "model_type", "weight_type", "precision", "license", "likes", "still_on_hub"
-]]
+BENCHMARK_COLS = [col.name for col in COLUMNS if col.name not in ["model", "average", "model_type", "architecture", "weight_type", "precision", "license", "params", "likes", "still_on_hub", "revision"]]
+
+# For the queue columns in the submission tab
+@dataclass(frozen=True)
+class EvalQueueColumn:
+    model: str
+    revision: str
+    private: bool
+    precision: str
+    weight_type: str
+    status: str
+
+EVAL_COLS = ["model", "revision", "private", "precision", "weight_type", "status"]
+EVAL_TYPES = [str, str, bool, str, str, str]
+
+## All the model information that we might need
+@dataclass
+class ModelDetails:
+    name: str
+    display_name: str = ""
+    symbol: str = ""  # emoji
+
+class ModelType(Enum):
+    PT = ModelDetails(name="pretrained", symbol="🟢")
+    FT = ModelDetails(name="fine-tuned", symbol="🔶")
+    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
+    RL = ModelDetails(name="RL-tuned", symbol="🟦")
+    Unknown = ModelDetails(name="", symbol="?")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+    @staticmethod
+    def from_str(type_str):
+        if "fine-tuned" in type_str or "🔶" in type_str:
+            return ModelType.FT
+        if "pretrained" in type_str or "🟢" in type_str:
+            return ModelType.PT
+        if "RL-tuned" in type_str or "🟦" in type_str:
+            return ModelType.RL
+        if "instruction-tuned" in type_str or "⭕" in type_str:
+            return ModelType.IFT
+        return ModelType.Unknown
+
+class WeightType(Enum):
+    Adapter = "Adapter"
+    Original = "Original"
+    Delta = "Delta"
+
+class Precision(Enum):
+    float16 = "float16"
+    bfloat16 = "bfloat16"
+    Unknown = "Unknown"
+
+    @staticmethod
+    def from_str(precision_str):
+        if precision_str in ["torch.float16", "float16"]:
+            return Precision.float16
+        if precision_str in ["torch.bfloat16", "bfloat16"]:
+            return Precision.bfloat16
+        return Precision.Unknown