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Update app.py
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app.py
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@@ -68,7 +68,6 @@ if __name__ == "__main__":
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gr.Markdown(
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"""
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An Open Bilingual Pre-Trained Model. [Visit our github repo](https://github.com/THUDM/GLM-130B)
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GLM-130B uses two different mask tokens: `[MASK]` for short blank filling and `[gMASK]` for left-to-right long text generation. When the input does not contain any MASK token, `[gMASK]` will be automatically appended to the end of the text. We recommend that you use `[MASK]` to try text fill-in-the-blank to reduce wait time (ideally within seconds without queuing).
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""")
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@@ -120,8 +119,7 @@ if __name__ == "__main__":
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gr.Markdown(
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"""
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Try this! Disclaimer inspired from [BLOOM](https://huggingface.co/spaces/bigscience/bloom-book)
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As with all language models, it is hard to predict how GLM-130B will respond to particular prompts; harmful or otherwise offensive content may occur without warning. We prohibit users from knowingly generating or allowing others to knowingly generate harmful content, including Hateful, Harassment, Violence, Adult, Political, Deception, etc.
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""")
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gr_examples = gr.Examples(examples=examples, inputs=model_input)
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gr.Markdown(
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"""
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An Open Bilingual Pre-Trained Model. [Visit our github repo](https://github.com/THUDM/GLM-130B)
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GLM-130B uses two different mask tokens: `[MASK]` for short blank filling and `[gMASK]` for left-to-right long text generation. When the input does not contain any MASK token, `[gMASK]` will be automatically appended to the end of the text. We recommend that you use `[MASK]` to try text fill-in-the-blank to reduce wait time (ideally within seconds without queuing).
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""")
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gr.Markdown(
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"""
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Try this! Disclaimer inspired from [BLOOM](https://huggingface.co/spaces/bigscience/bloom-book)
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As with all language models, it is hard to predict how GLM-130B will respond to particular prompts; harmful or otherwise offensive content may occur without warning. We prohibit users from knowingly generating or allowing others to knowingly generate harmful content, including Hateful, Harassment, Violence, Adult, Political, Deception, etc.
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""")
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gr_examples = gr.Examples(examples=examples, inputs=model_input)
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