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#!/usr/bin/env python
import json
import os
import time
from datetime import datetime, timezone
from src.envs import API, EVAL_REQUESTS_PATH, H4_TOKEN, QUEUE_REPO
from src.submission.check_validity import already_submitted_models, get_model_size, is_model_on_hub
from huggingface_hub import snapshot_download
from src.backend.envs import EVAL_REQUESTS_PATH_BACKEND
from src.backend.manage_requests import get_eval_requests
from src.backend.manage_requests import EvalRequest
def add_new_eval(model: str, base_model: str, revision: str, precision: str, private: bool, weight_type: str, model_type: str):
REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
user_name = ""
model_path = model
if "/" in model:
tokens = model.split("/")
user_name = tokens[0]
model_path = tokens[1]
precision = precision.split(" ")[0]
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
if model_type is None or model_type == "":
return print("Please select a model type.")
# Does the model actually exist?
if revision == "":
revision = "main"
# Is the model on the hub?
if weight_type in ["Delta", "Adapter"]:
base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=H4_TOKEN, test_tokenizer=True)
if not base_model_on_hub:
print(f'Base model "{base_model}" {error}')
return
if not weight_type == "Adapter":
model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True)
if not model_on_hub:
print(f'Model "{model}" {error}')
return
# Is the model info correctly filled?
try:
model_info = API.model_info(repo_id=model, revision=revision)
except Exception:
print("Could not get your model information. Please fill it up properly.")
return
model_size = get_model_size(model_info=model_info, precision=precision)
license = 'none'
try:
license = model_info.cardData["license"]
except Exception:
print("Please select a license for your model")
# return
# modelcard_OK, error_msg = check_model_card(model)
# if not modelcard_OK:
# print(error_msg)
# return
# Seems good, creating the eval
print("Adding new eval")
eval_entry = {
"model": model,
"base_model": base_model,
"revision": revision,
"private": private,
"precision": precision,
"weight_type": weight_type,
"status": "PENDING",
"submitted_time": current_time,
"model_type": model_type,
"likes": model_info.likes,
"params": model_size,
"license": license,
}
# Check for duplicate submission
if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
print("This model has been already submitted.")
return
print("Creating eval file")
OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
os.makedirs(OUT_DIR, exist_ok=True)
out_path = f"{OUT_DIR}/{model_path}_eval_request_{private}_{precision}_{weight_type}.json"
with open(out_path, "w") as f:
f.write(json.dumps(eval_entry))
print("Uploading eval file")
API.upload_file(path_or_fileobj=out_path, path_in_repo=out_path.split("eval-queue/")[1],
repo_id=QUEUE_REPO, repo_type="dataset", commit_message=f"Add {model} to eval queue")
# Remove the local file
os.remove(out_path)
print("Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list.")
return
def main():
from huggingface_hub import HfApi
api = HfApi()
model_lst = api.list_models()
model_lst = [m for m in model_lst]
def custom_filter(m) -> bool:
# res = m.pipeline_tag in {'text-generation'} and 'en' in m.tags and m.private is False
# res = m.pipeline_tag in {'text-generation'} and 'en' in m.tags and m.private is False and 'mistralai/' in m.id
res = 'mistralai/' in m.id
return res
filtered_model_lst = sorted([m for m in model_lst if custom_filter(m)], key=lambda m: m.downloads, reverse=True)
snapshot_download(repo_id=QUEUE_REPO, revision="main", local_dir=EVAL_REQUESTS_PATH_BACKEND, repo_type="dataset", max_workers=60)
PENDING_STATUS = "PENDING"
RUNNING_STATUS = "RUNNING"
FINISHED_STATUS = "FINISHED"
FAILED_STATUS = "FAILED"
status = [PENDING_STATUS, RUNNING_STATUS, FINISHED_STATUS, FAILED_STATUS]
# Get all eval requests
eval_requests: list[EvalRequest] = get_eval_requests(job_status=status, hf_repo=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH_BACKEND)
requested_model_names = {e.model for e in eval_requests}
breakpoint()
for i in range(min(200, len(filtered_model_lst))):
model = filtered_model_lst[i]
print(f'Considering {model.id} ..')
is_finetuned = any(tag.startswith('base_model:') for tag in model.tags)
model_type = 'pretrained'
if is_finetuned:
model_type = "fine-tuned"
is_instruction_tuned = 'nstruct' in model.id
if is_instruction_tuned:
model_type = "instruction-tuned"
if model.id not in requested_model_names:
if 'mage' not in model.id:
add_new_eval(model=model.id, base_model='', revision='main', precision='float32', private=False, weight_type='Original', model_type=model_type)
time.sleep(10)
else:
print(f'Model {model.id} already added, not adding it to the queue again.')
if __name__ == "__main__":
main()
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