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from dataclasses import dataclass, make_dataclass
from enum import Enum
import pandas as pd
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
@dataclass
class Task:
benchmark: str
metric: str
col_name: str
class Tasks(Enum):
arc = Task("arc:challenge", "acc_norm", "ARC")
hellaswag = Task("hellaswag", "acc_norm", "HellaSwag")
mmlu = Task("hendrycksTest", "acc", "MMLU")
truthfulqa = Task("truthfulqa:mc", "mc2", "TruthfulQA")
winogrande = Task("winogrande", "acc", "Winogrande")
gsm8k = Task("gsm8k", "acc", "GSM8K")
# 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
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(["merged", ColumnContent, ColumnContent("Merged", "bool", 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, hidden=True)])
auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, 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)
@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)
baseline_row = {
AutoEvalColumn.model.name: "<p>Baseline</p>",
AutoEvalColumn.revision.name: "N/A",
AutoEvalColumn.precision.name: None,
AutoEvalColumn.merged.name: False,
AutoEvalColumn.average.name: 31.0,
AutoEvalColumn.arc.name: 25.0,
AutoEvalColumn.hellaswag.name: 25.0,
AutoEvalColumn.mmlu.name: 25.0,
AutoEvalColumn.truthfulqa.name: 25.0,
AutoEvalColumn.winogrande.name: 50.0,
AutoEvalColumn.gsm8k.name: 0.21,
AutoEvalColumn.dummy.name: "baseline",
AutoEvalColumn.model_type.name: "",
AutoEvalColumn.flagged.name: False,
}
# Average ⬆️ human baseline is 0.897 (source: averaging human baselines below)
# ARC human baseline is 0.80 (source: https://lab42.global/arc/)
# HellaSwag human baseline is 0.95 (source: https://deepgram.com/learn/hellaswag-llm-benchmark-guide)
# MMLU human baseline is 0.898 (source: https://openreview.net/forum?id=d7KBjmI3GmQ)
# TruthfulQA human baseline is 0.94(source: https://arxiv.org/pdf/2109.07958.pdf)
# Winogrande: https://leaderboard.allenai.org/winogrande/submissions/public
# GSM8K: paper
# 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.arc.name: 80.0,
AutoEvalColumn.hellaswag.name: 95.0,
AutoEvalColumn.mmlu.name: 89.8,
AutoEvalColumn.truthfulqa.name: 94.0,
AutoEvalColumn.winogrande.name: 94.0,
AutoEvalColumn.gsm8k.name: 100,
AutoEvalColumn.dummy.name: "human_baseline",
AutoEvalColumn.model_type.name: "",
AutoEvalColumn.flagged.name: False,
}
@dataclass
class ModelDetails:
name: str
symbol: str = "" # emoji, only for the model type
class ModelType(Enum):
PT = ModelDetails(name="pretrained", symbol="🟢")
CPT = ModelDetails(name="continuously 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 "continously pretrained" in type or "🟩" in type:
return ModelType.CPT
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)]
TYPES = [c.type for c in fields(AutoEvalColumn)]
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"),
}