Spaces:
Sleeping
Sleeping
File size: 3,421 Bytes
e295ac3 c212cb7 e295ac3 6e56e0d e295ac3 36bf18d e295ac3 06acefd e295ac3 c212cb7 e295ac3 c212cb7 e295ac3 06acefd e295ac3 c212cb7 e295ac3 c212cb7 e295ac3 c212cb7 06acefd e295ac3 c212cb7 e295ac3 c212cb7 e295ac3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 |
import os
import pandas as pd
from pandas import DataFrame
from requests.exceptions import HTTPError
from huggingface_hub import get_collection, add_collection_item, update_collection_item, delete_collection_item
from huggingface_hub.utils._errors import HfHubHTTPError
from src.get_model_info.hardocded_metadata.types import ModelType
from src.get_model_info.utils import AutoEvalColumn
H4_TOKEN = os.environ.get("H4_TOKEN", None)
path_to_collection = "open-llm-leaderboard/llm-leaderboard-best-models-652d6c7965a4619fb5c27a03"
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
|