2023-04-20 17:30:03 +00:00
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######
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# Project : GPT4ALL-UI
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# File : backend.py
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# Author : ParisNeo with the help of the community
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# Supported by Nomic-AI
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# Licence : Apache 2.0
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# Description :
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# This is an interface class for GPT4All-ui backends.
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######
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from pathlib import Path
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from typing import Callable
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2023-05-13 12:19:56 +00:00
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import inspect
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import yaml
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import sys
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2023-04-20 17:30:03 +00:00
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__author__ = "parisneo"
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__github__ = "https://github.com/nomic-ai/gpt4all-ui"
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__copyright__ = "Copyright 2023, "
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__license__ = "Apache 2.0"
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class GPTBackend:
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file_extension='*.bin'
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backend_path = Path(__file__).parent
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2023-04-23 22:19:15 +00:00
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def __init__(self, config:dict, inline:bool) -> None:
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self.config = config
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self.inline = inline
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2023-04-20 17:30:03 +00:00
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def generate(self,
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prompt:str,
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n_predict: int = 128,
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new_text_callback: Callable[[str], None] = None,
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verbose: bool = False,
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**gpt_params ):
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"""Generates text out of a prompt
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This should ber implemented by child class
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Args:
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prompt (str): The prompt to use for generation
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n_predict (int, optional): Number of tokens to prodict. Defaults to 128.
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new_text_callback (Callable[[str], None], optional): A callback function that is called everytime a new text element is generated. Defaults to None.
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verbose (bool, optional): If true, the code will spit many informations about the generation process. Defaults to False.
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"""
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2023-05-02 14:49:13 +00:00
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pass
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def tokenize(self, prompt):
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"""
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Tokenizes the given prompt using the model's tokenizer.
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Args:
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prompt (str): The input prompt to be tokenized.
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Returns:
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list: A list of tokens representing the tokenized prompt.
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"""
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pass
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def detokenize(self, tokens_list):
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"""
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Detokenizes the given list of tokens using the model's tokenizer.
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Args:
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tokens_list (list): A list of tokens to be detokenized.
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Returns:
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str: The detokenized text as a string.
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"""
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pass
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2023-05-02 14:49:13 +00:00
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@staticmethod
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def list_models(config:dict):
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"""Lists the models for this backend
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"""
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models_dir = Path('./models')/config["backend"] # replace with the actual path to the models folder
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return [f.name for f in models_dir.glob(GPTBackend.file_extension)]
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@staticmethod
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def get_available_models():
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# Create the file path relative to the child class's directory
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backend_path = Path(__file__).parent
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file_path = backend_path/"models.yaml"
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with open(file_path, 'r') as file:
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yaml_data = yaml.safe_load(file)
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return yaml_data
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