from huggingface_hub import HfApi, Repository def restart_space(LLM_PERF_LEADERBOARD_REPO, OPTIMUM_TOKEN): HfApi().restart_space( repo_id=LLM_PERF_LEADERBOARD_REPO, token=OPTIMUM_TOKEN ) def load_dataset_repo(LLM_PERF_DATASET_REPO, OPTIMUM_TOKEN): llm_perf_repo = None if OPTIMUM_TOKEN: print("Loading LLM-Perf-Dataset from Hub...") llm_perf_repo = Repository( local_dir="./llm-perf/", clone_from=LLM_PERF_DATASET_REPO, token=OPTIMUM_TOKEN, repo_type="dataset", ) llm_perf_repo.git_pull() return llm_perf_repo 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'{model_name}' 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 return model_hyperlink(link, model_name)