File size: 4,192 Bytes
03696ac | 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 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | import json
import os
import re
from datetime import datetime, timezone
from src.envs import API, EVAL_REQUESTS_PATH, QUEUE_REPO, TOKEN
from src.submission.check_validity import (
already_submitted_models,
check_model_card,
get_model_size,
is_model_on_hub,
)
REQUESTED_MODELS = None
USERS_TO_SUBMISSION_DATES = None
MODEL_ID_PATTERN = re.compile(r"^[\w.-]+/[\w.-]+$")
def response(status: str, message: str) -> dict[str, str]:
return {"status": status, "message": message}
def add_new_eval(
model: str,
base_model: str,
revision: str,
precision: str,
weight_type: str,
model_type: str,
):
global REQUESTED_MODELS
global USERS_TO_SUBMISSION_DATES
if REQUESTED_MODELS is None:
REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
model = model.strip()
base_model = base_model.strip()
revision = revision.strip() or "main"
if not MODEL_ID_PATTERN.fullmatch(model):
return response("error", "Enter a model ID in the form organization/model-name.")
user_name, model_path = model.split("/", 1)
precision = precision.split(" ")[0]
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
if not model_type:
return response("error", "Please select a model type.")
if weight_type in ["Delta", "Adapter"]:
if not MODEL_ID_PATTERN.fullmatch(base_model):
return response("error", "Enter a base model ID in the form organization/model-name.")
base_model_on_hub, error, _ = is_model_on_hub(
model_name=base_model,
revision=revision,
token=TOKEN,
test_tokenizer=True,
)
if not base_model_on_hub:
return response("error", f'Base model "{base_model}" {error}')
if weight_type != "Adapter":
model_on_hub, error, _ = is_model_on_hub(
model_name=model,
revision=revision,
token=TOKEN,
test_tokenizer=True,
)
if not model_on_hub:
return response("error", f'Model "{model}" {error}')
try:
model_info = API.model_info(repo_id=model, revision=revision)
except Exception:
return response("error", "Could not read this model. Check its ID and revision, then try again.")
model_size = get_model_size(model_info=model_info, precision=precision)
try:
license_name = model_info.cardData["license"]
except Exception:
return response("error", "Please select a license in your model card.")
modelcard_ok, error_msg = check_model_card(model)
if not modelcard_ok:
return response("error", error_msg)
eval_entry = {
"model": model,
"base_model": base_model,
"revision": revision,
"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_name,
"private": False,
}
request_key = f"{model}_{revision}_{precision}"
if request_key in REQUESTED_MODELS:
return response("warning", "This model has already been submitted.")
out_dir = os.path.join(EVAL_REQUESTS_PATH, user_name)
os.makedirs(out_dir, exist_ok=True)
out_path = os.path.join(out_dir, f"{model_path}_eval_request_False_{precision}_{weight_type}.json")
with open(out_path, "w") as file:
json.dump(eval_entry, file)
try:
API.upload_file(
path_or_fileobj=out_path,
path_in_repo=os.path.relpath(out_path, EVAL_REQUESTS_PATH),
repo_id=QUEUE_REPO,
repo_type="dataset",
commit_message=f"Add {model} to eval queue",
)
except Exception:
os.remove(out_path)
return response("error", "The model passed validation, but the queue could not be updated. Please try again.")
REQUESTED_MODELS.add(request_key)
return response(
"success",
"Your model is in the evaluation queue. It should appear in the pending list shortly.",
)
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