mirror of
https://github.com/ParisNeo/lollms-webui.git
synced 2024-12-20 04:47:55 +00:00
118 lines
4.0 KiB
Python
118 lines
4.0 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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# 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 the official llamacpp python bindings
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# Follow him on his github project : https://github.com/abetlen/llama-cpp-python
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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 llama_cpp import Llama
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from api.binding import LLMBinding
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import yaml
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import random
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__author__ = "parisneo"
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__github__ = "https://github.com/ParisNeo/gpt4all-ui"
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__copyright__ = "Copyright 2023, "
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__license__ = "Apache 2.0"
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binding_name = "LLAMACPP"
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class LLAMACPP(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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seed = config["seed"]
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# if seed <=0:
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# seed = random.randint(1, 2**31)
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if not "n_gpu_layers" in self.config:
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self.config["n_gpu_layers"] = 20
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self.model = Llama(model_path=f"./models/llama_cpp_official/{self.config['model']}", n_ctx=self.config["ctx_size"], n_gpu_layers=self.config["n_gpu_layers"], seed=seed)
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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).decode()
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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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self.model.reset()
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output = ""
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tokens = self.model.tokenize(prompt.encode())
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count = 0
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for tok in self.model.generate(tokens,
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temp=gpt_params["temperature"],
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top_k=gpt_params['top_k'],
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top_p=gpt_params['top_p'],
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repeat_penalty=gpt_params['repeat_penalty'],
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):
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if count >= n_predict or (tok == self.model.token_eos()):
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break
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word = self.model.detokenize([tok]).decode()
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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
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