mirror of
https://github.com/ParisNeo/lollms-webui.git
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120 lines
4.3 KiB
Python
120 lines
4.3 KiB
Python
######
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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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# license : 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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from transformers import AutoTokenizer, TextGenerationPipeline
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
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from gpt4all_api.backend import GPTBackend
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import torch
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import yaml
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__author__ = "parisneo"
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__github__ = "https://github.com/ParisNeo/GPTQ_backend"
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__copyright__ = "Copyright 2023, "
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__license__ = "Apache 2.0"
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backend_name = "GPTQ"
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class GPTQ(GPTBackend):
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file_extension='*'
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def __init__(self, config:dict) -> None:
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"""Builds a GPTQ backend
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Args:
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config (dict): The configuration file
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"""
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super().__init__(config, False)
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self.model_dir = f'{config["model"]}'
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# load quantized model, currently only support cpu or single gpu
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self.model = AutoGPTQForCausalLM.from_pretrained(self.model_dir, BaseQuantizeConfig())
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self.tokenizer = AutoTokenizer.from_pretrained(self.model_dir, use_fast=True )
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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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return None
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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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return None
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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] = bool,
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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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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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try:
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tok = self.tokenizer.decode(self.model.generate(**self.tokenizer(prompt, return_tensors="pt").to("cuda:0"))[0])
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if new_text_callback is not None:
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new_text_callback(tok)
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output = tok
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"""
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self.model.reset()
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for tok in self.model.generate(prompt,
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n_predict=n_predict,
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temp=self.config['temp'],
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top_k=self.config['top_k'],
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top_p=self.config['top_p'],
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repeat_penalty=self.config['repeat_penalty'],
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repeat_last_n = self.config['repeat_last_n'],
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n_threads=self.config['n_threads'],
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):
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if not new_text_callback(tok):
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return
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"""
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except Exception as ex:
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print(ex)
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return output
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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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return [
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"EleutherAI/gpt-j-6b",
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"opt-125m-4bit"
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"TheBloke/medalpaca-13B-GPTQ-4bit",
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"TheBloke/stable-vicuna-13B-GPTQ",
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]
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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 |