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| from dataclasses import dataclass, make_dataclass | |
| from enum import Enum | |
| import json | |
| import pandas as pd | |
| 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(["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) | |
| 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, | |
| } | |
| 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}" | |
| 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"), | |
| } | |