First pass at model submission feature
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app.py
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import gradio as gr
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with gr.Blocks() as demo:
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gr.HTML('<h1 style="text-align:center">Subquadratic LLM Leaderboard</h1>')
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with gr.
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gr.
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gr.
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demo.launch()
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import os
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import requests
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from huggingface_hub import hf_hub_download
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import gradio as gr
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webhook_url = os.environ.get("WEBHOOK_URL")
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def submit_model(name):
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try:
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hf_hub_download(repo_id=name, filename="config.json") # sanity check input
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result = requests.post(webhook_url, json={"content":name})
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result.raise_for_status()
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except requests.exceptions.HTTPError:
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return "# ERROR: Could not contact queue. Please try again in a few minutes."
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except EntryNotFoundError:
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return "# ERROR: Model does not have a config.json file!"
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except RepositoryNotFoundError:
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return "# ERROR: Model could not be found on the Hugging Face Hub!"
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except:
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return "# ERROR: Unexpected error. Please try again later."
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return "# SUCCESS: Please wait up to 24 hours for your model to be added to the queue."
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with gr.Blocks() as demo:
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gr.HTML('<style>.tab-buttons button{font-size:1.3em}</style><h1 style="text-align:center">Subquadratic LLM Leaderboard</h1>')
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with gr.Tabs(elem_classes="tab-buttons") as tabs:
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with gr.Tab("π
LLM Benchmark"):
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gr.Markdown("Table filters under construction")
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gr.Dataframe("data.csv")
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with gr.Tab("π About"):
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gr.Markdown("""
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The **Subquadratic LLM Leaderboard** evaluates LLMs with subquadratic architectures (ie RWKV & Mamba) on the same benchmarks as the Open LLM Leaderboard, with the goal of providing open evaluation results while the architectures themselves are pending inclusion in π€ Transformers.
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This leaderboard is maintained by Devin Gulliver and is still under construction, check back regularly for further improvements!
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""")
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with gr.Tab("π Submit here!"):
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with gr.Group():
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with gr.Row():
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model_name = gr.Textbox(max_lines=1, label="Model Name", scale=4)
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submit = gr.Button("Submit", variant="primary", scale=0)
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output = gr.Markdown("Enter a public HF repo id, then hit Submit to add it to the evaluation queue.")
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submit.click(fn=submit_model, inputs=model_name, outputs=output)
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demo.launch()
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