File size: 7,508 Bytes
bba9a09 ff205eb bba9a09 919911e bba9a09 919911e bba9a09 919911e e9f2e5e 919911e ff205eb bba9a09 ff205eb bba9a09 ff205eb bba9a09 ff205eb bba9a09 ff205eb bba9a09 ff205eb bba9a09 ff205eb bba9a09 ff205eb 8545ff9 c368a70 d7938a7 8559e9f 8545ff9 774cfc7 d7938a7 8559e9f 8545ff9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 |
from dataclasses import dataclass, make_dataclass
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
dummy: bool = False
## Leaderboard columns
auto_eval_column_dict = []
# Init
auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
#Scores
auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
for task in Tasks:
auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
# Model information
auto_eval_column_dict.append(["model_type", ColumnContent, ColumnContent("Type", "str", False)])
#auto_eval_column_dict.append(["architecture", ColumnContent, ColumnContent("Architecture", "str", False)])
auto_eval_column_dict.append(["weight_type", ColumnContent, ColumnContent("Weight type", "str", False, True)])
auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precision", "str", False)])
#auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
#auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
#auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
#auto_eval_column_dict.append(["merged", ColumnContent, ColumnContent("Merged", "bool", False)])
auto_eval_column_dict.append(["flagged", ColumnContent, ColumnContent("Flagged", "bool", False, hidden=True)])
auto_eval_column_dict.append(["moe", ColumnContent, ColumnContent("MoE", "bool", False, hidden=True)])
# Dummy column for the search bar (hidden by the custom CSS)
auto_eval_column_dict.append(["dummy", ColumnContent, ColumnContent("model_name_for_query", "str", False, dummy=True)])
# We use make dataclass to dynamically fill the scores from Tasks
AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=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):
PT = ModelDetails(name="pretrained", symbol="🟢")
FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="🔶")
chat = ModelDetails(name="chat models (RLHF, DPO, IFT, ...)", symbol="💬")
merges = ModelDetails(name="base merges and moerges", symbol="🤝")
Unknown = ModelDetails(name="", symbol="?")
def to_str(self, separator=" "):
return f"{self.value.symbol}{separator}{self.value.name}"
@staticmethod
def from_str(type):
if "fine-tuned" in type or "🔶" in type:
return ModelType.FT
if "pretrained" in type or "🟢" in type:
return ModelType.PT
if any([k in type for k in ["instruction-tuned", "RL-tuned", "chat", "🟦", "⭕", "💬"]]):
return ModelType.chat
if "merge" in type or "🤝" in type:
return ModelType.merges
return ModelType.Unknown
class WeightType(Enum):
Adapter = ModelDetails("Adapter")
Original = ModelDetails("Original")
Delta = ModelDetails("Delta")
class Precision(Enum):
float16 = ModelDetails("float16")
bfloat16 = ModelDetails("bfloat16")
qt_8bit = ModelDetails("8bit")
qt_4bit = ModelDetails("4bit")
qt_GPTQ = ModelDetails("GPTQ")
Unknown = ModelDetails("?")
def from_str(precision):
if precision in ["torch.float16", "float16"]:
return Precision.float16
if precision in ["torch.bfloat16", "bfloat16"]:
return Precision.bfloat16
if precision in ["8bit"]:
return Precision.qt_8bit
if precision in ["4bit"]:
return Precision.qt_4bit
if precision in ["GPTQ", "None"]:
return Precision.qt_GPTQ
return Precision.Unknown
# Column selection
COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
TYPES = [c.type 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]
NUMERIC_INTERVALS = {
"?": pd.Interval(-1, 0, closed="right"),
"~1.5": pd.Interval(0, 2, closed="right"),
"~3": pd.Interval(2, 4, closed="right"),
"~7": pd.Interval(4, 9, closed="right"),
"~13": pd.Interval(9, 20, closed="right"),
"~35": pd.Interval(20, 45, closed="right"),
"~60": pd.Interval(45, 70, closed="right"),
"70+": pd.Interval(70, 10000, closed="right"),
}
# Define the baselines
baseline_row = {
AutoEvalColumn.model.name: "<p>Baseline</p>",
AutoEvalColumn.revision.name: "N/A",
AutoEvalColumn.precision.name: None,
AutoEvalColumn.average.name: 92.75,
#AutoEvalColumn.merged.name: False,
AutoEvalColumn.CMMMU.name: 100,
AutoEvalColumn.MMMU.name: 100,
AutoEvalColumn.MMMU_Pro_standard.name: 100,
AutoEvalColumn.MMMU_Pro_vision.name: 100,
AutoEvalColumn.MathVision.name: 100,
AutoEvalColumn.CII_Bench.name: 100,
AutoEvalColumn.Blink.name: 100,
AutoEvalColumn.CharXiv.name: 100,
AutoEvalColumn.MathVerse.name: 100,
AutoEvalColumn.MmvetV2.name: 100,
AutoEvalColumn.Ocrlite.name: 100,
AutoEvalColumn.OcrliteZh.name: 100,
AutoEvalColumn.dummy.name: "baseline",
AutoEvalColumn.model_type.name: "",
AutoEvalColumn.flagged.name: False,
}
# Define the human baselines
human_baseline_row = {
AutoEvalColumn.model.name: "<p>Human performance</p>",
AutoEvalColumn.revision.name: "N/A",
AutoEvalColumn.precision.name: None,
AutoEvalColumn.average.name: 92.75,
#AutoEvalColumn.merged.name: False,
AutoEvalColumn.CMMMU.name: 100,
AutoEvalColumn.MMMU.name: 100,
AutoEvalColumn.MMMU_Pro_standard.name: 100,
AutoEvalColumn.MMMU_Pro_vision.name: 100,
AutoEvalColumn.MathVision.name: 100,
AutoEvalColumn.CII_Bench.name: 100,
AutoEvalColumn.Blink.name: 100,
AutoEvalColumn.CharXiv.name: 100,
AutoEvalColumn.MathVerse.name: 100,
AutoEvalColumn.MmvetV2.name: 100,
AutoEvalColumn.Ocrlite.name: 100,
AutoEvalColumn.OcrliteZh.name: 100,
AutoEvalColumn.dummy.name: "human_baseline",
AutoEvalColumn.model_type.name: "",
AutoEvalColumn.flagged.name: False,
}
|