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Update app.py
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app.py
CHANGED
@@ -37,7 +37,7 @@ kn_max = 2
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kns = np.linspace(0.05, 2, 100)
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overheads = []
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for kn in
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kd = compute_kd(kn)
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overheads.append(compute_overhead(kn, kd)*100)
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@@ -74,14 +74,15 @@ Optimal model size:\t\t {N_opt/Bn:.2f}B
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Optimal datset size (tokens):\t {D_opt/Bn:.2f}
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## Your setting trade-off:
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Training compute overhead
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Inference cost fraction
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return text, fig
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with gr.Blocks() as demo:
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button = gr.Button("Compute!")
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plot = gr.Plot(value=plt)
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kns = np.linspace(0.05, 2, 100)
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overheads = []
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for kn in kns:
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kd = compute_kd(kn)
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overheads.append(compute_overhead(kn, kd)*100)
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Optimal datset size (tokens):\t {D_opt/Bn:.2f}
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## Your setting trade-off:
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Training compute overhead:\t {100*compute_overhead(kn, kd):.2f}%
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Inference cost fraction:\t {kn*100:.2f}%"""
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return text, fig
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with gr.Blocks() as demo:
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gr.Markdown("# Harm's law")
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N = gr.Number(value=1, label="Model size (in B parameters):")
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D = gr.Number(value=100, label="Dataset size (in B tokens):")
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button = gr.Button("Compute!")
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plot = gr.Plot(value=plt)
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