import os from dataclasses import dataclass from huggingface_hub import HfApi API = HfApi() # 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 def fields(raw_class): return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"] @dataclass(frozen=True) class AutoEvalColumn: # Auto evals column model_type_symbol = ColumnContent("T", "str", True) model = ColumnContent("Model", "markdown", True) average = ColumnContent("Average ⬆️", "number", True) arc = ColumnContent("ARC", "number", True) hellaswag = ColumnContent("HellaSwag", "number", True) mmlu = ColumnContent("MMLU", "number", True) truthfulqa = ColumnContent("TruthfulQA", "number", True) model_type = ColumnContent("Type", "str", False) precision = ColumnContent("Precision", "str", False) # , True) license = ColumnContent("Hub License", "str", False) params = ColumnContent("#Params (B)", "number", False) likes = ColumnContent("Hub ❤️", "number", False) still_on_hub = ColumnContent("Available on the hub", "bool", False) revision = ColumnContent("Model sha", "str", False, False) dummy = ColumnContent( "model_name_for_query", "str", True ) # dummy col to implement search bar (hidden by custom CSS) @dataclass(frozen=True) class EloEvalColumn: # Elo evals column model = ColumnContent("Model", "markdown", True) gpt4 = ColumnContent("GPT-4 (all)", "number", True) human_all = ColumnContent("Human (all)", "number", True) human_instruct = ColumnContent("Human (instruct)", "number", True) human_code_instruct = ColumnContent("Human (code-instruct)", "number", 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) LLAMAS = [ "huggingface/llama-7b", "huggingface/llama-13b", "huggingface/llama-30b", "huggingface/llama-65b", ] KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF" VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1" OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5" DOLLY_LINK = "https://huggingface.co/databricks/dolly-v2-12b" MODEL_PAGE = "https://huggingface.co/models" LLAMA_LINK = "https://ai.facebook.com/blog/large-language-model-llama-meta-ai/" VICUNA_LINK = "https://huggingface.co/CarperAI/stable-vicuna-13b-delta" ALPACA_LINK = "https://crfm.stanford.edu/2023/03/13/alpaca.html" def model_hyperlink(link, model_name): return f'{model_name}' def make_clickable_model(model_name): link = f"https://huggingface.co/{model_name}" if model_name in LLAMAS: link = LLAMA_LINK model_name = model_name.split("/")[1] elif model_name == "HuggingFaceH4/stable-vicuna-13b-2904": link = VICUNA_LINK model_name = "stable-vicuna-13b" elif model_name == "HuggingFaceH4/llama-7b-ift-alpaca": link = ALPACA_LINK model_name = "alpaca-13b" if model_name == "dolly-12b": link = DOLLY_LINK elif model_name == "vicuna-13b": link = VICUNA_LINK elif model_name == "koala-13b": link = KOALA_LINK elif model_name == "oasst-12b": link = OASST_LINK details_model_name = model_name.replace("/", "__") details_link = f"https://huggingface.co/datasets/open-llm-leaderboard/details_{details_model_name}" if not bool(os.getenv("DEBUG", "False")): # We only add these checks when not debugging, as they are extremely slow print(f"details_link: {details_link}") try: check_path = list( API.list_files_info( repo_id=f"open-llm-leaderboard/details_{details_model_name}", paths="README.md", repo_type="dataset", ) ) print(f"check_path: {check_path}") except Exception as err: # No details repo for this model print(f"No details repo for this model: {err}") return model_hyperlink(link, model_name) return model_hyperlink(link, model_name) + " " + model_hyperlink(details_link, "📑") def styled_error(error): return f"

{error}

" def styled_warning(warn): return f"

{warn}

" def styled_message(message): return f"

{message}

" def has_no_nan_values(df, columns): return df[columns].notna().all(axis=1) def has_nan_values(df, columns): return df[columns].isna().any(axis=1)