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from dataclasses import dataclass, make_dataclass | |
from enum import Enum | |
import json | |
import logging | |
from datetime import datetime | |
import pandas as pd | |
# Configure logging | |
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') | |
def parse_datetime(datetime_str): | |
formats = [ | |
"%Y-%m-%dT%H-%M-%S.%f", # Format with dashes | |
"%Y-%m-%dT%H:%M:%S.%f", # Standard format with colons | |
"%Y-%m-%dT%H %M %S.%f", # Spaces as separator | |
] | |
for fmt in formats: | |
try: | |
return datetime.strptime(datetime_str, fmt) | |
except ValueError: | |
continue | |
# in rare cases set unix start time for files with incorrect time (legacy files) | |
logging.error(f"No valid date format found for: {datetime_str}") | |
return datetime(1970, 1, 1) | |
def load_json_data(file_path): | |
"""Safely load JSON data from a file.""" | |
try: | |
with open(file_path, "r") as file: | |
return json.load(file) | |
except json.JSONDecodeError: | |
print(f"Error reading JSON from {file_path}") | |
return None # Or raise an exception | |
def fields(raw_class): | |
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] | |
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 | |
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(["score", ColumnContent, ColumnContent("score", "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 β€οΈ", "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(["not_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(["tokens", ColumnContent, ColumnContent("avg_tokens", "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) | |
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.fullname.name: "baseline", | |
# AutoEvalColumn.model_type.name: "", | |
# AutoEvalColumn.not_flagged.name: False, | |
} | |
# Average β¬οΈ 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.fullname.name: "human_baseline", | |
# AutoEvalColumn.model_type.name: "", | |
# AutoEvalColumn.not_flagged.name: False, | |
} | |
class ModelDetails: | |
name: str | |
symbol: str = "" # emoji, only for the model type | |
class ModelType(Enum): | |
PT = ModelDetails(name="pretrained", symbol="π’") | |
CPT = ModelDetails(name="continuously pretrained", symbol="π©") | |
FT = ModelDetails(name="fine-tuned on domain-specific datasets", symbol="πΆ") | |
chat = ModelDetails(name="chat models (RLHF, DPO, IFT, ...)", symbol="π¬") | |
merges = ModelDetails(name="base merges and moerges", symbol="π€") | |
Unknown = ModelDetails(name="", symbol="?") | |
def to_str(self, separator=" "): | |
return f"{self.value.symbol}{separator}{self.value.name}" | |
def from_str(type): | |
if "fine-tuned" in type or "πΆ" in type: | |
return ModelType.FT | |
if "continously pretrained" in type or "π©" in type: | |
return ModelType.CPT | |
if "pretrained" in type or "π’" in type: | |
return ModelType.PT | |
if any([k in type for k in ["instruction-tuned", "RL-tuned", "chat", "π¦", "β", "π¬"]]): | |
return ModelType.chat | |
if "merge" in type or "π€" 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"), | |
} | |