lucasjinreal

import torch
import sys
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoImageProcessor


def load_model_auto(model_path):
    dtype = torch.float16
    model = AutoModelForCausalLM.from_pretrained(
        model_path, torch_dtype=dtype, trust_remote_code=True, device_map="cuda"

lucasjinreal

// Copyright 2019 The Flutter Authors. All rights reserved.
// Use of this source code is governed by a BSD-style license that can be
// found in the LICENSE file.

import 'package:flutter/material.dart';
import 'package:animations/animations.dart';

const String _loremIpsumParagraph =
    'Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod '
    'tempor incididunt ut labore et dolore magna aliqua. Vulputate dignissim '

lucasjinreal

# -*- coding: utf-8 -*-
# file: models.py
# author: JinTian
# time: 11/05/2017 3:03 PM
# Copyright 2017 JinTian. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#

lucasjinreal

import os
import sys
import numpy as np
import tensorflow as tf
import logging


logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s %(filename)s line:%(lineno)d %(levelname)s %(message)s',
                    datefmt='%Y-%m-%d %H:%M:%S')

lucasjinreal

logging.basicConfig(level=logging.DEBUG,
                    format='%(asctime)s %(filename)s line:%(lineno)d %(levelname)s %(message)s',
                    datefmt='%a, %d %b %Y %H:%M:%S')

lucasjinreal

import torch


def load_previous_model(encoder, decoder, checkpoint_dir, model_prefix):
    """
    this can generally used in PyTorch to load previous model,
    this function will find max epoch from checkpoints dir, for other models
    just change model load format.
    :param encoder:
    :param decoder:

lucasjinreal

# -*- coding: utf-8 -*-
# file: notebook_to_python.py
# author: JinTian
# time: 09/05/2017 3:28 PM
# Copyright 2017 JinTian. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#