src/utils_display.py

# from dataclasses import dataclass


# 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
#     never_hidden: bool = False
#     dummy: 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, never_hidden=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)
#     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", "bool", 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
    # else:
    #    link = MODEL_PAGE

#     return model_hyperlink(link, model_name)


# 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>"
#     )

Qwen_1_8B_Chat_Link = "https://huggingface.co/Qwen/Qwen-1_8B-Chat"
Qwen_7B_Chat_Link = "https://huggingface.co/Qwen/Qwen-7B-Chat"
Qwen_14B_Chat_Link = "https://huggingface.co/Qwen/Qwen-14B-Chat"
Qwen_72B_Chat_Link = "https://huggingface.co/Qwen/Qwen-72B-Chat"
Gemma_2B_it_Link = "https://huggingface.co/google/gemma-2b-it"
Gemma_7B_it__Link = "https://huggingface.co/google/gemma-7b-it"
ChatGLM3_6B_Link = "https://huggingface.co/THUDM/chatglm3-6b"
Mistral_7B_Instruct_v0_2_Link = "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2"
LLaMA_2_7B_Chat_Link = "https://huggingface.co/meta-llama/Llama-2-7b-chat-hf"
LLaMA_2_13B_Chat_Link = "https://huggingface.co/meta-llama/Llama-2-13b-chat-hf"
LLaMA_2_70B_Chat_Link = "https://huggingface.co/meta-llama/Llama-2-70b-chat-hf"
LLaMA_3_8B_Instruct_Link = "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"
LLaMA_3_70B_Instruct_Link = "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct"
Vicuna_7B_v1_3_Link = "https://huggingface.co/lmsys/vicuna-7b-v1.3"
Vicuna_13B_v1_3_Link = "https://huggingface.co/lmsys/vicuna-13b-v1.3"
Vicuna_33B_v1_3_Link = "https://huggingface.co/lmsys/vicuna-33b-v1.3"
Baichuan2_13B_Chat_Link = "https://huggingface.co/baichuan-inc/Baichuan2-13B-Chat"
Yi_34B_Chat_Link = "https://huggingface.co/01-ai/Yi-34B-Chat"
GPT_4_Turbo_Link = "https://platform.openai.com/docs/models/gpt-4-turbo-and-gpt-4"
ErnieBot_4_0_Link = "https://cloud.baidu.com/doc/WENXINWORKSHOP/s/clntwmv7t"
Gemini_1_0_Pro_Link = "https://ai.google.dev/gemini-api/docs/models/gemini"