import json import os from datetime import datetime, timezone from huggingface_hub import ModelCard, snapshot_download from src.display.formatting import styled_error, styled_message, styled_warning from src.envs import API, EVAL_REQUESTS_PATH, DYNAMIC_INFO_PATH, DYNAMIC_INFO_FILE_PATH, DYNAMIC_INFO_REPO, H4_TOKEN, QUEUE_REPO, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA from src.leaderboard.filter_models import DO_NOT_SUBMIT_MODELS from src.submission.check_validity import ( already_submitted_models, check_model_card, get_model_size, is_model_on_hub, user_submission_permission, ) REQUESTED_MODELS = None USERS_TO_SUBMISSION_DATES = None def add_new_eval( model: str, base_model: str, revision: str, precision: str, private: bool, weight_type: str, model_type: str, ): global REQUESTED_MODELS global USERS_TO_SUBMISSION_DATES if not REQUESTED_MODELS: REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH) user_name = "" model_path = model if "/" in model: user_name = model.split("/")[0] model_path = model.split("/")[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 styled_error("Please select a model type.") # Is the user rate limited? if user_name != "": user_can_submit, error_msg = user_submission_permission( user_name, USERS_TO_SUBMISSION_DATES, RATE_LIMIT_PERIOD, RATE_LIMIT_QUOTA ) if not user_can_submit: return styled_error(error_msg) # Did the model authors forbid its submission to the leaderboard? if model in DO_NOT_SUBMIT_MODELS or base_model in DO_NOT_SUBMIT_MODELS: return styled_warning("Model authors have requested that their model be not submitted on the leaderboard.") # 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: return styled_error(f'Base model "{base_model}" {error}') if not weight_type == "Adapter": model_on_hub, error, model_config = is_model_on_hub(model_name=model, revision=revision, test_tokenizer=True) if not model_on_hub: return styled_error(f'Model "{model}" {error}') architecture = "?" if model_config is not None: architectures = getattr(model_config, "architectures", None) if architectures: architecture = ";".join(architectures) downloads = getattr(model_config, 'downloads', 0) created_at = getattr(model_config, 'created_at', '') # Is the model info correctly filled? try: model_info = API.model_info(repo_id=model, revision=revision) except Exception: return styled_error("Could not get your model information. Please fill it up properly.") model_size = get_model_size(model_info=model_info, precision=precision) # Were the model card and license filled? try: license = model_info.cardData["license"] except Exception: return styled_error("Please select a license for your model") modelcard_OK, error_msg = check_model_card(model) if not modelcard_OK: return styled_error(error_msg) is_merge_from_metadata = False is_moe_from_metadata = False model_card = ModelCard.load(model) # Storing the model tags tags = [] if model_card.data.tags: is_merge_from_metadata = "merge" in model_card.data.tags is_moe_from_metadata = "moe" in model_card.data.tags merge_keywords = ["mergekit", "merged model", "merge model", "merging"] # If the model is a merge but not saying it in the metadata, we flag it is_merge_from_model_card = any(keyword in model_card.text.lower() for keyword in merge_keywords) if is_merge_from_model_card or is_merge_from_metadata: tags.append("merge") if not is_merge_from_metadata: tags.append("flagged:undisclosed_merge") moe_keywords = ["moe", "mixture of experts"] is_moe_from_model_card = any(keyword in model_card.text.lower() for keyword in moe_keywords) is_moe_from_name = "moe" in model.lower().replace("/", "-").replace("_", "-").split("-") if is_moe_from_model_card or is_moe_from_name or is_moe_from_metadata: tags.append("moe") if not is_moe_from_metadata: tags.append("flagged:undisclosed_moe") # Seems good, creating the eval print("Adding new eval") eval_entry = { "model": model, "base_model": base_model, "revision": revision, "private": private, "precision": precision, "params": model_size, "architectures": architecture, "weight_type": weight_type, "status": "PENDING", "submitted_time": current_time, "model_type": model_type, "job_id": -1, "job_start_time": None, } supplementary_info = { "likes": model_info.likes, "license": license, "still_on_hub": True, "tags": tags, "downloads": downloads, "created_at": created_at } # Check for duplicate submission if f"{model}_{revision}_{precision}" in REQUESTED_MODELS: return styled_warning("This model has been already submitted.") 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", ) # We want to grab the latest version of the submission file to not accidentally overwrite it snapshot_download( repo_id=DYNAMIC_INFO_REPO, local_dir=DYNAMIC_INFO_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30 ) with open(DYNAMIC_INFO_FILE_PATH) as f: all_supplementary_info = json.load(f) all_supplementary_info[model] = supplementary_info with open(DYNAMIC_INFO_FILE_PATH, "w") as f: json.dump(all_supplementary_info, f, indent=2) API.upload_file( path_or_fileobj=DYNAMIC_INFO_FILE_PATH, path_in_repo=DYNAMIC_INFO_FILE_PATH.split("/")[-1], repo_id=DYNAMIC_INFO_REPO, repo_type="dataset", commit_message=f"Add {model} to dynamic info queue", ) # Remove the local file os.remove(out_path) return styled_message( "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." )