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:] != "__"] @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(frozen=True) 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(["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(["fullname", ColumnContent, ColumnContent("fullname", "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: "

Baseline

", 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 ⬆️ 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: "

Human performance

", 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, } @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"), }