| import json |
| import os |
| from datetime import datetime |
|
|
| import pandas as pd |
|
|
|
|
| def generate_request(model_id, precision, model_type, params): |
| model_type = ( |
| f"\ud83d\udfe2 : {model_type}" if model_type == "pretrained" else model_type |
| ) |
| data = { |
| "model": model_id, |
| "base_model": "", |
| "revision": "main", |
| "private": False, |
| "precision": precision, |
| "weight_type": "Original", |
| "status": "PENDING", |
| "submitted_time": (datetime.now()).strftime("%Y-%m-%dT%H:%M:%SZ"), |
| "model_type": 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[["status", "model_id", "precision", "model_type", "params"]] |
|
|
| if selection == "pretrained": |
| df = df[df["model_type"] == "pretrained"] |
| elif selection == "instructed": |
| df = df[df["model_type"] == "instruction-tuned"] |
| elif selection == "todo": |
| df = df[df["status"] == "To do"] |
|
|
| for _, row in df.iterrows(): |
| status, model_id, precision, model_type, params = row |
| generate_request( |
| model_id=model_id, |
| precision=precision, |
| model_type=model_type, |
| params=params, |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| import argparse |
|
|
| parser = argparse.ArgumentParser(description="Generate model requests.") |
| parser.add_argument("--pretrained", action="store_true") |
| parser.add_argument("--instructed", action="store_true") |
| parser.add_argument("--todo", action="store_true") |
| args = parser.parse_args() |
|
|
| if args.pretrained: |
| generate_requests("pretrained") |
| elif args.instructed: |
| generate_requests("instructed") |
| elif args.todo: |
| generate_requests("todo") |
| else: |
| print("Please select a valid option between: pretrained, instructed, todo, all") |
|
|