from huggingface_hub import HfApi, Repository import gradio as gr import json def change_tab(query_param): query_param = query_param.replace("'", '"') query_param = json.loads(query_param) if ( isinstance(query_param, dict) and "tab" in query_param and query_param["tab"] == "plot" ): return gr.Tabs.update(selected=1) else: return gr.Tabs.update(selected=0) 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_dataset_repo = None if OPTIMUM_TOKEN: print("Loading LLM-Perf-Dataset from Hub...") llm_perf_dataset_repo = Repository( local_dir="./llm-perf-dataset", clone_from=LLM_PERF_DATASET_REPO, token=OPTIMUM_TOKEN, repo_type="dataset", ) llm_perf_dataset_repo.git_pull() return llm_perf_dataset_repo LLM_MODEL_TYPES = { "gpt_bigcode": "GPT-BigCode 🌸", "RefinedWebModel": "Falcon 🦅", "RefinedWeb": "Falcon 🦅", "baichuan": "Baichuan 🌊", "bloom": "Bloom 🌸", "llama": "LLaMA 🦙", "gpt_neox": "GPT-NeoX", "gpt_neo": "GPT-Neo", "codegen": "CodeGen", "chatglm": "ChatGLM", "gpt2": "GPT-2", "gptj": "GPT-J", "xglm": "XGLM", "opt": "OPT", "mpt": "MPT", } def model_hyperlink(link, model_name): return f'{model_name}' def process_model_name(model_name): link = f"https://huggingface.co/{model_name}" return model_hyperlink(link, model_name) def process_model_type(model_type): if model_type in LLM_MODEL_TYPES: return LLM_MODEL_TYPES[model_type] else: return model_type