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app.py
CHANGED
@@ -30,7 +30,7 @@ with gr.Blocks() as demo:
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The model checkpoint is the 'nano_gpt_ckpts' dir. The hyper params used are the exact same shown in the nano-gpt video by Karpathy, and the dataset size is just 1MB, so the text generated could be gibberish.
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Keep in mind the output is limited to 400 tokens so the inference runs within reasonable time (10s) on CPU. (Huggingface free tier)
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GPU inference can output much much longer sequences.
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Click on the "Generate text" button to see the generated text.
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""")
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@@ -38,4 +38,4 @@ with gr.Blocks() as demo:
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output = gr.Textbox(label="Generated text from nano-gpt")
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generate_button.click(fn=generate_nanogpt_text, inputs=None, outputs=output, api_name='nano-gpt text generation sample')
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demo.launch(
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The model checkpoint is the 'nano_gpt_ckpts' dir. The hyper params used are the exact same shown in the nano-gpt video by Karpathy, and the dataset size is just 1MB, so the text generated could be gibberish.
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Keep in mind the output is limited to 400 tokens so the inference runs within reasonable time (10s) on CPU. (Huggingface free tier)
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+
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GPU inference can output much much longer sequences.
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Click on the "Generate text" button to see the generated text.
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""")
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output = gr.Textbox(label="Generated text from nano-gpt")
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generate_button.click(fn=generate_nanogpt_text, inputs=None, outputs=output, api_name='nano-gpt text generation sample')
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demo.launch()
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