mirror of
https://github.com/ParisNeo/lollms-webui.git
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144 lines
5.2 KiB
Python
144 lines
5.2 KiB
Python
######
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# Project : GPT4ALL-UI
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# File : binding.py
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# Author : ParisNeo with the help of the community
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# Underlying binding : Abdeladim's pygptj binding
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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 bindings.
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# This binding is a wrapper to marella's binding
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# Follow him on his github project : https://github.com/marella/ctransformers
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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 api.binding import LLMBinding
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import yaml
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from ctransformers import AutoModelForCausalLM
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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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binding_name = "CTRansformers"
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class CTRansformers(LLMBinding):
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file_extension='*.bin'
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def __init__(self, config:dict) -> None:
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"""Builds a LLAMACPP binding
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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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if 'gpt2' in self.config['model']:
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model_type='gpt2'
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elif 'gptj' in self.config['model']:
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model_type='gptj'
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elif 'gpt_neox' in self.config['model']:
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model_type='gpt_neox'
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elif 'dolly-v2' in self.config['model']:
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model_type='dolly-v2'
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elif 'starcoder' in self.config['model']:
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model_type='starcoder'
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elif 'llama' in self.config['model'] or 'wizardLM' in self.config['model']:
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model_type='llama'
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elif 'mpt' in self.config['model']:
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model_type='mpt'
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else:
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print("The model you are using is not supported by this binding")
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return
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if self.config["use_avx2"]:
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self.model = AutoModelForCausalLM.from_pretrained(
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f"./models/c_transformers/{self.config['model']}", model_type=model_type
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)
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else:
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self.model = AutoModelForCausalLM.from_pretrained(
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f"./models/c_transformers/{self.config['model']}", model_type=model_type, lib = "avx"
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)
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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 self.model.tokenize(prompt.encode())
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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 self.model.detokenize(tokens_list)
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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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output = ""
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self.model.reset()
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tokens = self.model.tokenize(prompt)
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count = 0
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for tok in self.model.generate(
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tokens,
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top_k=self.config['top_k'],
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top_p=self.config['top_p'],
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temperature=self.config['temperature'],
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repetition_penalty=self.config['repeat_penalty'],
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seed=self.config['seed'],
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batch_size=1,
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threads = self.config['n_threads'],
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reset=True,
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):
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if count >= n_predict or self.model.is_eos_token(tok):
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break
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word = self.model.detokenize(tok)
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if new_text_callback is not None:
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if not new_text_callback(word):
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break
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output += word
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count += 1
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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 get_available_models():
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# Create the file path relative to the child class's directory
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binding_path = Path(__file__).parent
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file_path = binding_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 |