File size: 6,833 Bytes
50373cb 2d84df2 50373cb 2d84df2 4428299 2d84df2 4428299 2d84df2 50373cb 4428299 50373cb 4428299 50373cb 4428299 50373cb 3ea291e 50373cb |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 |
import json
import os
from collections import defaultdict
import huggingface_hub
from huggingface_hub import ModelCard
from huggingface_hub.hf_api import ModelInfo
from transformers import AutoConfig
from transformers.models.auto.tokenization_auto import AutoTokenizer
def check_model_card(repo_id: str) -> tuple[bool, str]:
"""Checks if the model card and license exist and have been filled"""
try:
card = ModelCard.load(repo_id)
except huggingface_hub.utils.EntryNotFoundError:
return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
# Enforce license metadata
if card.data.license is None:
if not ("license_name" in card.data and "license_link" in card.data):
return False, (
"License not found. Please add a license to your model card using the `license` metadata or a"
" `license_name`/`license_link` pair."
)
# Enforce card content
if len(card.text) < 200:
return False, "Please add a description to your model card, it is too short."
# Check for security considerations section
# if "security" not in card.text.lower() and "security considerations" not in card.text.lower():
# return False, (
# "Please add a 'Security Considerations' section to your model card describing security implications, "
# "known vulnerabilities, and safe usage guidelines."
# )
return True, ""
def check_safetensors_format(model_name: str, revision: str, token: str = None) -> tuple[bool, str]:
"""Checks if the model uses safetensors format"""
try:
# Use HF API to list repository files
api = huggingface_hub.HfApi()
files = api.list_repo_files(model_name, revision=revision, token=token)
# Check for any .safetensors files in the repository
if any(f.endswith('.safetensors') for f in files):
return True, ""
return False, (
"Model weights must be in safetensors format. Please convert your model using: \n"
"```python\n"
"from transformers import AutoModelForCausalLM\n"
"from safetensors.torch import save_file\n\n"
"model = AutoModelForCausalLM.from_pretrained('your-model')\n"
"state_dict = model.state_dict()\n"
"save_file(state_dict, 'model.safetensors')\n"
"```"
)
except Exception as e:
return False, f"Error checking safetensors format: {str(e)}"
def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str, AutoConfig]:
"""Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
try:
# Check if it's a GGUF model first
api = huggingface_hub.HfApi()
files = api.list_repo_files(model_name, revision=revision, token=token)
is_gguf = any(f.endswith('.gguf') for f in files)
if is_gguf:
# For GGUF models, we don't need to check AutoConfig/AutoTokenizer
return True, None, None
# For non-GGUF models, proceed with standard checks
config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
if test_tokenizer:
try:
tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
except ValueError as e:
return (
False,
f"uses a tokenizer which is not in a transformers release: {e}",
None
)
except Exception as e:
return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
# Check safetensors format for non-GGUF models
safetensors_check, safetensors_msg = check_safetensors_format(model_name, revision, token)
if not safetensors_check:
return False, safetensors_msg, None
return True, None, config
except ValueError:
return (
False,
"needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
None
)
except Exception as e:
return False, "was not found on hub!", None
def get_model_size(model_info: ModelInfo, precision: str):
"""Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
try:
model_size = round(model_info.safetensors["total"] / 1e9, 3)
except (AttributeError, TypeError):
return 0 # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
model_size = size_factor * model_size
return model_size
def get_model_arch(model_info: ModelInfo):
"""Gets the model architecture from the configuration"""
return model_info.config.get("architectures", "Unknown")
def already_submitted_models(requested_models_dir: str) -> tuple[set[str], defaultdict]:
"""Gather a list of already submitted models to avoid duplicates"""
depth = 1
file_names = []
users_to_submission_dates = defaultdict(list)
for root, _, files in os.walk(requested_models_dir):
current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
if current_depth == depth:
for file in files:
if not file.endswith(".json"):
continue
try:
with open(os.path.join(root, file), "r") as f:
info = json.load(f)
# Handle missing fields gracefully
model = info.get('model', '')
revision = info.get('revision', 'main') # default to main if missing
precision = info.get('precision', '')
file_names.append(f"{model}_{revision}_{precision}")
# Select organisation
if model.count("/") == 0 or "submitted_time" not in info:
continue
organisation, _ = model.split("/")
users_to_submission_dates[organisation].append(info["submitted_time"])
except (json.JSONDecodeError, KeyError, IOError) as e:
print(f"Warning: Skipping malformed file {file}: {str(e)}")
continue
return set(file_names), users_to_submission_dates
|