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SivilTaram
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Update app.py
Browse files
app.py
CHANGED
@@ -41,7 +41,7 @@ with gr.Blocks() as demo:
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This repo is the offical demo space for [Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies](https://huggingface.co/papers/2407.13623). In summary, we show that when scaling up model size, increase vocabulary size too, but at a slower rate than other parameters.
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![Vocabulary Demo](https://huggingface.co/spaces/sail/scaling-with-vocab-demo/
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This tool is used to predict the optimal vocabulary size given the non-vocabulary parameters. We provide 3 ways for prediction:
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@@ -68,17 +68,3 @@ with gr.Blocks() as demo:
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outputs=output_text
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demo.launch()
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# import gradio as gr
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# def update(name):
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# return f"Welcome to Gradio, {name}!"
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# with gr.Blocks() as demo:
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# gr.Markdown("Start typing below and then click **Run** to see the output.")
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# with gr.Row():
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# inp = gr.Textbox(placeholder="What is your name?")
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# out = gr.Textbox()
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# btn = gr.Button("Run")
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# btn.click(fn=update, inputs=inp, outputs=out)
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# demo.launch()
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This repo is the offical demo space for [Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies](https://huggingface.co/papers/2407.13623). In summary, we show that when scaling up model size, increase vocabulary size too, but at a slower rate than other parameters.
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![Vocabulary Demo](https://huggingface.co/spaces/sail/scaling-with-vocab-demo/resolve/main/figures/vocabulary_demo.png)
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This tool is used to predict the optimal vocabulary size given the non-vocabulary parameters. We provide 3 ways for prediction:
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outputs=output_text
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)
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demo.launch()
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