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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("ARC", "number", True)
hellaswag = ColumnContent("HellaSwag", "number", True)
mmlu = ColumnContent("MMLU", "number", True)
truthfulqa = ColumnContent("TruthfulQA", "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-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-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)
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