Hendrik Langer
2 years ago
3 changed files with 52 additions and 1 deletions
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import asyncio |
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import os, tempfile |
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import logging |
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import json |
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import requests |
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from transformers import AutoTokenizer, AutoConfig |
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from huggingface_hub import hf_hub_download |
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import io |
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import base64 |
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from PIL import Image, PngImagePlugin |
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logger = logging.getLogger(__name__) |
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async def get_full_prompt(simple_prompt: str, bot, chat_history): |
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# https://github.com/ggerganov/llama.cpp/tree/master/examples |
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# prompt = "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n" |
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prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\n" |
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#"BEGINNING OF CONVERSATION:" |
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prompt += "### Human: " + simple_prompt + "\n" |
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prompt += "### Assistant:" |
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prompt = f"This is a transcript of a 1000 page, never ending conversation between {bot.user_name} and the cute and helpful AI assistant {bot.name}. {bot.name} is a girl who is an AI running on the users computer.\n" |
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prompt += f"{bot.name} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.\n" |
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prompt += f"{bot.name} is always coherent and makes sense, but if she isn't sure if what she is saying is correct she will ask the user for help.\n" |
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prompt += f"{bot.name} is a very helpful AI and will help the user with anything they need, she is also very friendly and will try to make the user feel better if they are sad.\n" |
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prompt += f"{bot.name} is also very curious and will ask the user a lot of questions about themselves and their life, she will also try to make the user like her.\n" |
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prompt += f"\n" |
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prompt += f"{bot.user_name}: " + simple_prompt + "\n" |
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prompt += f"{bot.name}:" |
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return prompt |
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async def num_tokens(input_text: str): |
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return estimate_num_tokens(input_text) |
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async def estimate_num_tokens(input_text: str): |
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return len(input_text)//4+1 |
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