Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
File size: 5,529 Bytes
ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e 9afcf15 a507ee8 6313532 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 d50e87a ce5c604 4f0083e ce5c604 4f0083e ce5c604 4f0083e ce5c604 |
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 |
import os
from dataclasses import dataclass
from huggingface_hub import HfApi
API = HfApi()
# These classes are for user facing column names, to avoid having to change them
# all around the code when a modif is needed
@dataclass
class ColumnContent:
name: str
type: str
displayed_by_default: bool
hidden: bool = False
def fields(raw_class):
return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
@dataclass(frozen=True)
class AutoEvalColumn: # Auto evals column
model_type_symbol = ColumnContent("T", "str", True)
model = ColumnContent("Model", "markdown", True)
average = ColumnContent("Average ⬆️", "number", True)
arc = ColumnContent("Ko-ARC", "number", True)
hellaswag = ColumnContent("Ko-HellaSwag", "number", True)
mmlu = ColumnContent("Ko-MMLU", "number", True)
truthfulqa = ColumnContent("Ko-TruthfulQA", "number", True)
commongen_v2 = ColumnContent("Ko-CommonGen V2", "number", True)
# TODO: Uncomment when we have results for these
# ethicalverification = ColumnContent("EthicalVerification", "number", True)
model_type = ColumnContent("Type", "str", False)
precision = ColumnContent("Precision", "str", False) # , True)
license = ColumnContent("Hub License", "str", False)
params = ColumnContent("#Params (B)", "number", False)
likes = ColumnContent("Hub ❤️", "number", False)
still_on_hub = ColumnContent("Available on the hub", "bool", False)
revision = ColumnContent("Model sha", "str", False, False)
dummy = ColumnContent(
"model_name_for_query", "str", True
) # dummy col to implement search bar (hidden by custom CSS)
@dataclass(frozen=True)
class EloEvalColumn: # Elo evals column
model = ColumnContent("Model", "markdown", True)
gpt4 = ColumnContent("GPT-4 (all)", "number", True)
human_all = ColumnContent("Human (all)", "number", True)
human_instruct = ColumnContent("Human (instruct)", "number", True)
human_code_instruct = ColumnContent("Human (code-instruct)", "number", True)
@dataclass(frozen=True)
class EvalQueueColumn: # Queue column
model = ColumnContent("model", "markdown", True)
revision = ColumnContent("revision", "str", True)
private = ColumnContent("private", "bool", True)
precision = ColumnContent("precision", "str", True)
weight_type = ColumnContent("weight_type", "str", "Original")
status = ColumnContent("status", "str", True)
LLAMAS = [
"huggingface/llama-7b",
"huggingface/llama-13b",
"huggingface/llama-30b",
"huggingface/llama-65b",
]
KOALA_LINK = "https://huggingface.co/TheBloke/koala-13B-HF"
VICUNA_LINK = "https://huggingface.co/lmsys/vicuna-13b-delta-v1.1"
OASST_LINK = "https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5"
DOLLY_LINK = "https://huggingface.co/databricks/dolly-v2-12b"
MODEL_PAGE = "https://huggingface.co/models"
LLAMA_LINK = "https://ai.facebook.com/blog/large-language-model-llama-meta-ai/"
VICUNA_LINK = "https://huggingface.co/CarperAI/stable-vicuna-13b-delta"
ALPACA_LINK = "https://crfm.stanford.edu/2023/03/13/alpaca.html"
def model_hyperlink(link, model_name):
return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def make_clickable_model(model_name):
link = f"https://huggingface.co/{model_name}"
if model_name in LLAMAS:
link = LLAMA_LINK
model_name = model_name.split("/")[1]
elif model_name == "HuggingFaceH4/stable-vicuna-13b-2904":
link = VICUNA_LINK
model_name = "stable-vicuna-13b"
elif model_name == "HuggingFaceH4/llama-7b-ift-alpaca":
link = ALPACA_LINK
model_name = "alpaca-13b"
if model_name == "dolly-12b":
link = DOLLY_LINK
elif model_name == "vicuna-13b":
link = VICUNA_LINK
elif model_name == "koala-13b":
link = KOALA_LINK
elif model_name == "oasst-12b":
link = OASST_LINK
details_model_name = model_name.replace("/", "__")
# details_link = f"https://huggingface.co/datasets/open-ko-llm-leaderboard/details_{details_model_name}"
# if not bool(os.getenv("DEBUG", "False")):
# # We only add these checks when not debugging, as they are extremely slow
# print(f"details_link: {details_link}")
# try:
# check_path = list(
# API.list_files_info(
# repo_id=f"open-ko-llm-leaderboard/details_{details_model_name}",
# paths="README.md",
# repo_type="dataset",
# )
# )
# print(f"check_path: {check_path}")
# except Exception as err:
# # No details repo for this model
# print(f"No details repo for this model: {err}")
# return model_hyperlink(link, model_name)
return model_hyperlink(link, model_name) # + " " + model_hyperlink(details_link, "📑")
def styled_error(error):
return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
def styled_warning(warn):
return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
def styled_message(message):
return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
def has_no_nan_values(df, columns):
return df[columns].notna().all(axis=1)
def has_nan_values(df, columns):
return df[columns].isna().any(axis=1)
|