File size: 5,728 Bytes
9d22eee
2a5f9fb
c848631
2a5f9fb
c848631
df66f6e
efeee6d
 
9d22eee
 
 
314f91a
2a5f9fb
 
 
 
 
 
 
 
 
 
 
 
efeee6d
c848631
9d22eee
c848631
 
 
 
9d22eee
c848631
 
65323e6
9d22eee
c848631
 
 
 
 
 
 
 
 
9d22eee
c848631
9d22eee
 
 
2a5f9fb
efeee6d
2a5f9fb
 
 
 
 
 
c848631
2a5f9fb
 
efeee6d
2a5f9fb
9d22eee
2a5f9fb
9833cdb
 
2a5f9fb
 
 
9d22eee
 
 
 
 
2a5f9fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9d22eee
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2a5f9fb
 
 
 
 
 
 
 
 
 
b1a1395
2a5f9fb
 
 
 
 
 
 
 
 
 
 
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
from dataclasses import dataclass, make_dataclass
from enum import Enum
from typing import Any

import pandas as pd # type: ignore

from src.display.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: list[tuple[str, type, Any]] = []
# 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)))
# Dashboard
auto_eval_column_dict.append(("dashboard_link", ColumnContent, ColumnContent("Dashboard", "markdown", 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)))
# 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", True)
    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", 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):
        if "fine-tuned" in type or "🔶" in type:
            return ModelType.FT
        if "pretrained" in type or "🟢" in type:
            return ModelType.PT
        if "RL-tuned" in type or "🟦" in type:
            return ModelType.RL
        if "instruction-tuned" in type or "⭕" in type:
            return ModelType.IFT
        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]
COLS_LITE = [c.name for c in fields(AutoEvalColumn) if c.displayed_by_default and not c.hidden]
TYPES_LITE = [c.type for c in fields(AutoEvalColumn) if c.displayed_by_default and 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"),
}