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import time |
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import random |
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import streamlit as st |
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from example_prompts import EXAMPLE_PROMPTS |
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HEADER = """ |
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# <span style="color:red"><b>WARNING:</b> This app uses BLOOM-6b3 as a backend generation . We are currently working on making it work with BLOOM-176 </span> |
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""" |
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SIDE_BAR_TEXT = """ |
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# *PETALS: A Collaborative Inference of Large Models* |
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A BigScience initiative. |
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- [Introduction](#introduction) |
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* [What is *PETALS* ?](#what-is--petals---) |
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* [Generation parameters](#generation-parameters) |
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# Introduction |
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This Space is an interactive Space of *PETALS* paper (Submitted in EMNLP 2022) that aims to run BLOOM-176 in a distributed manner for efficient and cost-effective inference and fine-tuning. |
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## What is *PETALS* ? |
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With the release of BLOOM-176B and OPT-175B, everyone can download pretrained models of this scale. Still, using these models requires supercomputer-grade hardware, which is unavailable to many researchers. |
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PETALS proposes to run BLOOM-176 in a distributed manner. The model is run on multiple computers from different users. Each user can benefit from the large model's inference by running a script similar to the one on this Space or from this Google Colab link: [](https://colab.research.google.com/drive/1FEu0Dt_MjiwvdIz1SmIr9QfDDvNAJdZ-#scrollTo=O0WwC_IqofNH) |
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## Generation parameters |
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""" |
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def write_incremental(text, place_holder, delay=0.05): |
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""" |
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Write a text in a streamlit widget, one character at a time. |
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Adapted from: https://discuss.streamlit.io/t/display-several-pieces-of-strings-incrementally-on-the-same-line/9279 |
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""" |
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for i in range(len(text) + 1): |
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place_holder.markdown("### <span style='color:grey' class='font'> %s </span>" % text[0:i].replace("\n", "<br>"), unsafe_allow_html=True) |
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time.sleep(delay) |
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def i_am_feeling_lucky(): |
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""" |
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Return a random prompt from EXAMPLE_PROMPT |
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""" |
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return EXAMPLE_PROMPTS[random.randint(0, len(EXAMPLE_PROMPTS) - 1)] |
