import os import requests import huggingface_hub import gradio as gr webhook_url = os.environ.get("WEBHOOK_URL") def submit_model(name): try: huggingface_hub.hf_hub_download(repo_id=name, filename="config.json") # sanity check input except huggingface_hub.utils._errors.EntryNotFoundError: return "# ERROR: Model does not have a config.json file!" except huggingface_hub.utils._errors.RepositoryNotFoundError: return "# ERROR: Model could not be found on the Hugging Face Hub!" except requests.exceptions.HTTPError: return "# ERROR: Network error while validating model. Please try again later." except Exception as e: print(e) return "ERROR: Unexpected error. Please try again later." try: result = requests.post(webhook_url, json={"content":name}) except requests.exceptions.HTTPError: return "# ERROR: Network error while contacting queue. Please try again in a few minutes." except Exception as e: print(e) return "ERROR: Unexpected error. Please try again later." return "# SUCCESS: Please wait up to 24 hours for your model to be added to the queue." with gr.Blocks() as demo: gr.HTML('

Subquadratic LLM Leaderboard

') with gr.Tabs(elem_classes="tab-buttons") as tabs: with gr.Tab("🏅 LLM Benchmark"): gr.Markdown("Table filters under construction") gr.Dataframe("data.csv") with gr.Tab("📝 About"): gr.Markdown(""" The **Subquadratic LLM Leaderboard** evaluates LLMs with subquadratic architectures (ie RWKV & Mamba) with the goal of providing open evaluation results while the architectures themselves are pending inclusion in 🤗 Transformers. The metrics are the same as the Open LLM Leaderboard: ARC 25-shot, HellaSwag 10-shot, MMLU 5-shot, TruthfulQA zeroshot, Winogrande 5-shot, and GSM8K 5-shot. This leaderboard is maintained by Devin Gulliver and is still under construction, check back regularly for further improvements! """) with gr.Tab("🚀 Submit here!"): with gr.Group(): with gr.Row(): model_name = gr.Textbox(max_lines=1, label="Model Name", scale=4) submit = gr.Button("Submit", variant="primary", scale=0) output = gr.Markdown("Enter a public HF repo id, then hit Submit to add it to the evaluation queue.") submit.click(fn=submit_model, inputs=model_name, outputs=output) demo.launch()