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import json
import os
from datetime import datetime
import pandas as pd
def generate_request(model_id, precision, model_type, params, index):
data = {
"model": model_id,
"base_model": "",
"revision": "main",
"private": False,
"precision": precision,
"weight_type": "Original",
"status": "FINISHED",
"submitted_time": (datetime.now() + pd.Timedelta(hours=index)).strftime(
"%Y-%m-%dT%H:%M:%SZ"
),
"model_type": f"\ud83d\udfe2 : {model_type} if model_type == 'pretrained' else model_type",
"likes": 0,
"params": params,
"license": "custom",
"architecture": "",
"sender": "mariagrandury",
}
os.makedirs(f"{model_id}", exist_ok=True)
with open(f"{model_id}_eval_request_False_{precision}_Original.json", "w") as f:
json.dump(data, f)
def generate_requests(selection: str):
df = pd.read_csv("scripts/models.csv")
df = df[["model_id", "precision", "model_type", "params", "iberobench"]]
if selection == "pretrained":
df = df[df["model_type"] == "pretrained"]
elif selection == "pretrained_new":
df = df[df["model_type"] == "pretrained"]
df = df[df["iberobench"] == False]
elif selection == "instruction":
df = df[df["model_type"] == "instruction-tuned"]
for index, row in df.iterrows():
model_id, precision, model_type, params, iberobench = row
generate_request(
model_id=model_id,
precision=precision,
model_type=model_type,
params=params,
index=index,
)
if __name__ == "__main__":
import argparse
parser = argparse.ArgumentParser(description="Generate model requests.")
parser.add_argument("--pretrained", action="store_true")
parser.add_argument("--pretrained_new", action="store_true")
parser.add_argument("--instruction", action="store_true")
args = parser.parse_args()
if args.pretrained:
generate_requests("pretrained")
elif args.pretrained_new:
generate_requests("pretrained_new")
elif args.instruction:
generate_requests("instruction")
else:
generate_requests()
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