app.py

import os
import pandas as pd
import requests
import huggingface_hub
import gradio as gr

data = pd.read_csv("data.csv", dtype="str")
webhook_url = os.environ.get("WEBHOOK_URL")

archlinks = {
    "Mamba": "https://arxiv.org/abs/2312.00752",
    "RWKV-4": "https://arxiv.org/abs/2305.13048",
    "Based": "https://arxiv.org/abs/2402.18668",
    "RWKV-5": "https://substack.recursal.ai/p/rwkv-v5-15b-achieves-sota-multi-lingual", # paper soon...
    "StripedHyena": "https://www.together.ai/blog/stripedhyena-7b", # no paper?
}

def filter_table(cols, name, type, arch, size):
    tmp = data
    # filter
    tmp = tmp[tmp["Name"].str.contains(name)]
    tmp = tmp[tmp["Type"].isin(type)]
    tmp = tmp[tmp["Architecture"].isin(arch)]
    tmp = tmp[tmp["Model Size"].isin(size)]
    # prettify
    tmp["Type"] = tmp["Type"].apply(lambda x: x[0])
    tmp = tmp.rename({"Type": "T"}, axis=1)
    tmp["Name"] = tmp["Name"].apply(lambda x: f'<a target="_blank" href="https://huggingface.co/{x}" style="color:var(--link-text-color);text-decoration:underline;text-decoration-style:dotted">{x}</a>')
    tmp["Architecture"] = tmp["Architecture"].apply(lambda x: f'<a target="_blank" href="{archlinks[x]}" style="color:var(--link-text-color);text-decoration:underline;text-decoration-style:dotted">{x}</a>')
    tmp["Base Model"] = tmp["Base Model"].apply(lambda x: f'<a target="_blank" href="https://huggingface.co/{x}" style="color:var(--link-text-color);text-decoration:underline;text-decoration-style:dotted">{x}</a>' if x != "base" else "")
    # show/hide
    tmp = tmp.drop(cols, axis=1)
    # done!
    return tmp

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(css=".tab-buttons button{font-size:1.3em}") as demo:
    gr.HTML('<h1 style="text-align:center"><span style="font-size:1.3em">Subquadratic LLM Leaderboard</span></h1>')
    gr.Markdown("**REMEMBER:** If you don't see an eligible model here, make sure to submit it! We hope to incentivize subquadratic/attention-free LLM development through friendly competition.")

    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.Tab("🏅 LLM Benchmark"):
            with gr.Row():
                with gr.Column():
                    namefilter = gr.Textbox(max_lines=1, placeholder="Search by model name and hit Enter...", show_label=False)
                    typefilter = gr.CheckboxGroup(show_label=False, choices=list(data["Type"].unique()), value=[n for n in data["Type"].unique() if n not in ["⌛ Pending"]])
                
                with gr.Column():
                    archfilter = gr.CheckboxGroup(label="Filter by model architecture", choices=list(data["Architecture"].unique()), value=list(data["Architecture"].unique()))
                    sizefilter = gr.CheckboxGroup(label="Filter by model size", choices=list(data["Model Size"].unique()), value=list(data["Model Size"].unique()))

                with gr.Column():
                    colfilter = gr.CheckboxGroup(label="Hide columns", choices=list(data.columns)[2:], value=["MT-Bench (coming soon!)","Architecture","Model Size","Base Model"])
                
            table = gr.Dataframe(filter_table(["MT-Bench (coming soon!)","Architecture","Model Size","Base Model"],"",[n for n in data["Type"].unique() if n not in ["⌛ Pending"]],list(data["Architecture"].unique()),list(data["Model Size"].unique())), datatype="markdown")

            # actions
            
            namefilter.submit(filter_table, [colfilter,namefilter,typefilter,archfilter,sizefilter], table)
            
            for filter in [colfilter,typefilter,archfilter,sizefilter]:
                filter.input(filter_table, [colfilter,namefilter,typefilter,archfilter,sizefilter], table)
        
        with gr.Tab("📝 About"):
            gr.Markdown("""
                The **Subquadratic LLM Leaderboard** evaluates LLMs with subquadratic/attention-free architectures (i.e. RWKV & Mamba) with the goal of providing open
                evaluation results while the architectures themselves are pending inclusion/release in the 🤗 Transformers library.  
                
                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 perpetually under construction, check back regularly for further improvements!  
                
                Compute for evaluating RWKV models is generously provided by [Recursal AI](https://recursal.ai).
                """)
        
        with gr.Tab("🚀 Submit here!"):
            with gr.Group():
                with gr.Row():
                    model_name = gr.Textbox(max_lines=1, placeholder="Enter model name...", show_label=False, 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(show_api=False, allowed_paths=["data.csv"])