import pandas as pd from huggingface_hub import add_collection_item, delete_collection_item, get_collection, update_collection_item from huggingface_hub.utils._errors import HfHubHTTPError from pandas import DataFrame from src.display.utils import AutoEvalColumn, ModelType from src.envs import H4_TOKEN, PATH_TO_COLLECTION # Specific intervals for the collections 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. """ collection = get_collection(collection_slug=PATH_TO_COLLECTION, token=H4_TOKEN) params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce") cur_best_models = [] ix = 0 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: ix += 1 cur_len_collection = len(collection.items) try: collection = 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, ) if ( len(collection.items) > cur_len_collection ): # we added an item - we make sure its position is correct item_object_id = collection.items[-1].item_object_id update_collection_item( collection_slug=PATH_TO_COLLECTION, item_object_id=item_object_id, position=ix ) cur_len_collection = len(collection.items) 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: try: delete_collection_item( collection_slug=PATH_TO_COLLECTION, item_object_id=item.item_object_id, token=H4_TOKEN ) except HfHubHTTPError: continue