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| import os | |
| 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 HF_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=HF_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=HF_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=HF_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=HF_TOKEN | |
| ) | |
| except HfHubHTTPError: | |
| continue | |