File size: 7,579 Bytes
2a5f9fb
df66f6e
2a5f9fb
e0f9194
2a5f9fb
df66f6e
 
 
6e56e0d
0c7ef71
ead4c96
6e56e0d
49a5f27
df66f6e
6e56e0d
 
 
b4ba8b7
6e56e0d
 
 
 
 
f04f90e
6e56e0d
 
 
 
0a3530a
 
 
 
 
 
 
 
6e56e0d
 
 
f04f90e
6e56e0d
f04f90e
6e56e0d
 
0a3530a
 
 
6e56e0d
0a3530a
ca686d6
7302987
ead4c96
0a3530a
 
 
ead4c96
0a3530a
 
7302987
 
0a3530a
 
7302987
3dfaf22
6e56e0d
0a3530a
6e56e0d
 
 
0a3530a
6e56e0d
 
7302987
a4c11b8
 
9f4d66f
6e56e0d
0a3530a
e0f9194
fbbefcc
0c7ef71
e0f9194
2a5f9fb
0c7ef71
 
e0f9194
0c7ef71
 
 
 
2a5f9fb
0c7ef71
e0f9194
 
 
 
 
0a3530a
e0f9194
 
2a5f9fb
0c7ef71
2a5f9fb
 
e0f9194
0a3530a
fc1e99b
 
2a5f9fb
0a3530a
9d22eee
49a5f27
 
 
 
2671d62
 
 
 
6e56e0d
 
2671d62
6e56e0d
2671d62
6e56e0d
2671d62
 
 
 
6e56e0d
 
2671d62
 
 
 
 
6e56e0d
2671d62
6e56e0d
 
2a5f9fb
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f04f90e
0a3530a
f04f90e
 
 
 
 
 
 
 
0a3530a
 
 
51b829f
 
0a3530a
 
 
f04f90e
 
51b829f
df0b79f
2119dda
df0b79f
f04f90e
 
 
 
 
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
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
import json
import os
import re
import logging
from collections import defaultdict
from datetime import datetime, timedelta, timezone

import huggingface_hub
from huggingface_hub import ModelCard
from huggingface_hub.hf_api import ModelInfo, get_safetensors_metadata
from transformers import AutoConfig, AutoTokenizer

from src.display.utils import parse_iso8601_datetime, curated_authors
from src.envs import HAS_HIGHER_RATE_LIMIT


# ht to @Wauplin, thank you for the snippet!
# See https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard/discussions/317
def check_model_card(repo_id: str) -> tuple[bool, str]:
    # Returns operation status, and error message
    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.", None

    # 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."
                ),
                None,
            )

    # Enforce card content
    if len(card.text) < 200:
        return False, "Please add a description to your model card, it is too short.", None

    return True, "", card


def is_model_on_hub(
    model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False
) -> tuple[bool, str, AutoConfig]:
    try:
        config = AutoConfig.from_pretrained(
            model_name, revision=revision, trust_remote_code=trust_remote_code, token=token, force_download=True)
        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:
                return (
                    False,
                    "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?",
                    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:
        if "You are trying to access a gated repo." in str(e):
            return True, "uses a gated model.", None
        return False, f"was not found or misconfigured on the hub! Error raised was {e.args[0]}", None


def get_model_size(model_info: ModelInfo, precision: str) -> float:
    size_pattern = re.compile(r"(\d+\.)?\d+(b|m)")
    safetensors = None

    try:
        safetensors = get_safetensors_metadata(model_info.id)
    except Exception as e:
        logging.error(f"Failed to get safetensors metadata for model {model_info.id}: {str(e)}")

    if safetensors is not None:
        model_size = round(sum(safetensors.parameter_count.values()) / 1e9, 3)
    else:
        try:
            size_match = re.search(size_pattern, model_info.id.lower())
            if size_match:
                model_size = size_match.group(0)
                model_size = round(float(model_size[:-1]) if model_size[-1] == "b" else float(model_size[:-1]) / 1e3, 3)
            else:
                return -1  # Unknown model size
        except AttributeError:
            logging.warning(f"Unable to parse model size from ID: {model_info.id}")
            return -1  # Unknown model size

    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.id.lower()) else 1
    model_size = size_factor * model_size

    return model_size

def get_model_arch(model_info: ModelInfo):
    return model_info.config.get("architectures", "Unknown")


def user_submission_permission(org_or_user, users_to_submission_dates, rate_limit_period, rate_limit_quota):
    # No limit for curated authors
    if org_or_user in curated_authors:
        return True, ""
    
    # Increase quota first if user has higher limits
    if org_or_user in HAS_HIGHER_RATE_LIMIT:
        rate_limit_quota *= 2

    if org_or_user not in users_to_submission_dates:
        return True, ""

    submission_dates = sorted(users_to_submission_dates[org_or_user])
    time_limit = datetime.now(timezone.utc) - timedelta(days=rate_limit_period)

    submissions_after_timelimit = [
        parse_iso8601_datetime(d) for d in submission_dates
        if parse_iso8601_datetime(d) > time_limit
    ]

    num_models_submitted_in_period = len(submissions_after_timelimit)

    # Use >= to correctly enforce the rate limit
    if num_models_submitted_in_period >= rate_limit_quota:
        error_msg = f"Organisation or user `{org_or_user}` already has {num_models_submitted_in_period} model requests submitted in the last {rate_limit_period} days.\n"
        error_msg += "Please wait a couple of days before resubmitting, so that everybody can enjoy using the leaderboard 🤗"
        return False, error_msg

    return True, ""


def already_submitted_models(requested_models_dir: str) -> set[str]:
    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
                with open(os.path.join(root, file), "r") as f:
                    info = json.load(f)
                    file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")

                    # Select organisation
                    if info["model"].count("/") == 0 or "submitted_time" not in info:
                        continue
                    organisation, _ = info["model"].split("/")
                    users_to_submission_dates[organisation].append(info["submitted_time"])

    return set(file_names), users_to_submission_dates


def get_model_tags(model_card, model: str):
    is_merge_from_metadata = False
    is_moe_from_metadata = False

    tags = []
    if model_card is None:
        return tags
    if model_card.data.tags:
        is_merge_from_metadata = any(
            [tag in model_card.data.tags for tag in ["merge", "moerge", "mergekit", "lazymergekit"]]
        )
        is_moe_from_metadata = any([tag in model_card.data.tags for tag in ["moe", "moerge"]])

    is_merge_from_model_card = any(
        keyword in model_card.text.lower() for keyword in ["merged model", "merge model", "moerge"]
    )
    if is_merge_from_model_card or is_merge_from_metadata:
        tags.append("merge")
    is_moe_from_model_card = any(keyword in model_card.text.lower() for keyword in ["moe", "mixtral"])
    # Hardcoding because of gating problem
    if "Qwen/Qwen1.5-32B" in model:
        is_moe_from_model_card = False
    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")

    return tags