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| 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 | |
| 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:] != "__"] | |
| 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) | |
| 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) | |
| 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'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>' | |
| 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"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>" | |
| def styled_warning(warn): | |
| return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>" | |
| def styled_message(message): | |
| return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>" | |
| 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) | |