update utils

src/display/utils.py

@@ -101,3 +101,102 @@ 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"
 ]]
+
+# For the queue columns in the submission tab
+@dataclass(frozen=True)
+class EvalQueueColumn:
+    name: str
+    type: Any
+    label: str
+    description: str
+
+# Define the queue columns
+EVAL_QUEUE_COLUMNS: List[EvalQueueColumn] = [
+    EvalQueueColumn(
+        name="model",
+        type=str,
+        label="Model",
+        description="Model name",
+    ),
+    EvalQueueColumn(
+        name="revision",
+        type=str,
+        label="Revision",
+        description="Model revision or commit hash",
+    ),
+    EvalQueueColumn(
+        name="private",
+        type=bool,
+        label="Private",
+        description="Is the model private?",
+    ),
+    EvalQueueColumn(
+        name="precision",
+        type=str,
+        label="Precision",
+        description="Precision of the model weights",
+    ),
+    EvalQueueColumn(
+        name="weight_type",
+        type=str,
+        label="Weight Type",
+        description="Type of model weights",
+    ),
+    EvalQueueColumn(
+        name="status",
+        type=str,
+        label="Status",
+        description="Evaluation status",
+    ),
+]
+
+# Create lists for evaluation columns and types
+EVAL_COLS = [col.name for col in EVAL_QUEUE_COLUMNS]
+EVAL_TYPES = [col.type for col in EVAL_QUEUE_COLUMNS]
+
+# Model information
+@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