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from dataclasses import dataclass, field, make_dataclass
from typing import ClassVar
from enum import Enum
import pandas as pd
from src.about import Tasks
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
# These classes are for user facing column names,
# to avoid having to change them all around the code
# when a modif is needed
@dataclass
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
never_hidden: bool = False
## Leaderboard columns
# auto_eval_column_fields = []
# # Init
# auto_eval_column_fields.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
# auto_eval_column_fields.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
# # Scores
# auto_eval_column_fields.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
# for task in Tasks:
# auto_eval_column_fields.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# # Model information
# auto_eval_column_fields.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
# auto_eval_column_fields.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
# auto_eval_column_fields.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
# auto_eval_column_fields.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
# auto_eval_column_fields.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
# auto_eval_column_fields.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
# auto_eval_column_fields.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
# auto_eval_column_fields.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
# auto_eval_column_fields.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
#
#
#
# def make_classvar_dataclass(name: str, spec: list):
# ns = {"__annotations__": {}}
# for field_name, field_type, default in spec:
# # Mark as ClassVar so dataclass doesn't treat it as an instance field
# ns["__annotations__"][field_name] = ClassVar[field_type]
# ns[field_name] = default
# # No instance fields; just class-level descriptors
# return make_dataclass(name, [], frozen=True, namespace=ns)
#
# # We use make dataclass to dynamically fill the scores from Tasks
# AutoEvalColumn = make_classvar_dataclass("AutoEvalColumn", auto_eval_column_fields)
@dataclass(frozen=True)
class AutoEvalColumn:
system = ColumnContent("System Name", "markdown", True, never_hidden=True)
system_type = ColumnContent("System Type", "str", True)
organization = ColumnContent("Organization", "str", True, never_hidden=True)
success_rate = ColumnContent("Success Rate (%)", "number", True)
problems_solved = ColumnContent("Problems Solved", "number", True)
submitted_on = ColumnContent("Submitted On", "datetime", True)
## For the queue columns in the submission tab
@dataclass(frozen=True)
class EvalQueueColumn: # Queue column
model = ColumnContent("model", "markdown", True)
revision = ColumnContent("revision", "str", True)
private = ColumnContent("private", "bool", True)
precision = ColumnContent("precision", "str", True)
weight_type = ColumnContent("weight_type", "str", "Original")
status = ColumnContent("status", "str", True)
## All the model information that we might need
@dataclass
class ModelDetails:
name: str
display_name: str = ""
symbol: str = "" # emoji
class ModelType(Enum):
LLM = ModelDetails(name="LLM", symbol="🟢")
AgenticLLM = ModelDetails(name="AgenticLLM", symbol="🔶")
# IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
# RL = ModelDetails(name="RL-tuned", symbol="🟦")
Other = ModelDetails(name="Other", symbol="?")
def to_str(self, separator=" "):
return f"{self.value.symbol}{separator}{self.value.name}"
@staticmethod
def from_str(type):
if "AgenticLLM" in type or "🔶" in type:
return ModelType.AgenticLLM
if "LLM" in type or "🟢" in type:
return ModelType.LLM
# if "RL-tuned" in type or "🟦" in type:
# return ModelType.RL
# if "instruction-tuned" in type or "⭕" in type:
# return ModelType.IFT
return ModelType.Other
class WeightType(Enum):
Adapter = ModelDetails("Adapter")
Original = ModelDetails("Original")
Delta = ModelDetails("Delta")
class Precision(Enum):
float16 = ModelDetails("float16")
bfloat16 = ModelDetails("bfloat16")
Unknown = ModelDetails("?")
def from_str(precision):
if precision in ["torch.float16", "float16"]:
return Precision.float16
if precision in ["torch.bfloat16", "bfloat16"]:
return Precision.bfloat16
return Precision.Unknown
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
# BENCHMARK_COLS = [t.value.col_name for t in Tasks]
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