import os import pandas as pd from pandas import DataFrame from huggingface_hub import get_collection, add_collection_item, delete_collection_item from huggingface_hub.utils._errors import HfHubHTTPError from src.display_models.model_metadata_type import ModelType from src.display_models.utils import AutoEvalColumn H4_TOKEN = os.environ.get("H4_TOKEN", None) path_to_collection = "HuggingFaceH4/current-best-models-of-the-open-llm-leaderboard-652d64cf619fc62beef5c2a3" intervals = { "1B": pd.Interval(0, 1.5, closed="right"), "3B": pd.Interval(2.5, 3.5, closed="neither"), "7B": pd.Interval(6, 8, closed="neither"), "13B": pd.Interval(10, 14, closed="neither"), "30B":pd.Interval(25, 35, closed="neither"), "65B": pd.Interval(60, 70, closed="neither"), } def update_collections(df: DataFrame): """This function updates the Open LLM Leaderboard model collection with the latest best models for each size category and type. """ params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce") cur_best_models = [] for type in ModelType: if type.value.name == "": continue for size in intervals: # We filter the df to gather the relevant models type_emoji = [t[0] for t in type.value.symbol] filtered_df = df[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)] numeric_interval = pd.IntervalIndex([intervals[size]]) mask = params_column.apply(lambda x: any(numeric_interval.contains(x))) filtered_df = filtered_df.loc[mask] best_models = list(filtered_df.sort_values(AutoEvalColumn.average.name, ascending=False)[AutoEvalColumn.dummy.name]) print(type.value.symbol, size, best_models[:10]) # We add them one by one to the leaderboard for model in best_models: # We can use collection = get_collection to grab the id of the last item, then place it where we want using update_collection but it's costly... # We could also remove exists_ok to update the note to include the date of apparition of the model for ex. try: add_collection_item( path_to_collection, item_id=model, item_type="model", exists_ok=True, note=f"Best {type.to_str(' ')} model of around {size} on the leaderboard today!", token=H4_TOKEN ) cur_best_models.append(model) break except HfHubHTTPError: continue collection = get_collection(path_to_collection, token=H4_TOKEN) for item in collection.items: if item.item_id not in cur_best_models: delete_collection_item(collection_slug=path_to_collection, item_object_id=item.item_object_id, token=H4_TOKEN)