kishida

from transformers import AutoProcessor, MusicgenForConditionalGeneration
# import scipy

# model_name = "facebook/musicgen-small"
model_name = "facebook/musicgen-medium"
# model_name = "facebook/musicgen-large"

processor = AutoProcessor.from_pretrained(model_name)
model = MusicgenForConditionalGeneration.from_pretrained(model_name).to("cuda")

kishida

import torch
import requests
from PIL import Image
from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
from customized_mini_gpt4 import CustomizedMiniGPT4

ckpt_path = "./checkpoint.pth"

model = CustomizedMiniGPT4(gpt_neox_model="rinna/bilingual-gpt-neox-4b")
tokenizer = model.gpt_neox_tokenizer

kishida

import com.atilika.kuromoji.ipadic.Tokenizer;

import java.awt.*;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

public class CaseFrameGrammer {
    enum Degree {
        NONE, LITTLE;

kishida

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.util.Arrays;
import java.util.List;
import java.util.NoSuchElementException;
import java.util.concurrent.CompletableFuture;

public class EnglishGenerator {

kishida

import com.fasterxml.jackson.core.JsonProcessingException;
import com.fasterxml.jackson.databind.ObjectMapper;
import lombok.AllArgsConstructor;
import lombok.Data;

import javax.swing.*;
import java.awt.*;
import java.awt.image.BufferedImage;
import java.net.URI;
import java.net.http.HttpClient;

kishida

# based on StableLM chat
# https://huggingface.co/spaces/stabilityai/stablelm-tuned-alpha-chat
import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer
import time
import numpy as np
from torch.nn import functional as F
import os
from threading import Thread

kishida

import java.awt.*;
import java.awt.image.BufferedImage;
import java.util.stream.IntStream;

import javax.swing.*;

public class ParticleParallel {
    enum Type{
        WATER(new Color(0, 0, 255, 96)), OUTWALL(Color.BLACK), INWALL(Color.GREEN);

kishida

import java.awt.*;
import java.awt.geom.Path2D;
import java.awt.image.BufferedImage;
import javax.swing.*;

public class WaveAnim {
    public static void main(String[] args){
        final int TDIV = 1000; //時間の分割数
        final int XDIV = 40; //座標の分割数
        final int VIEWSTEP = 25;//何コマごとに描画するか

kishida

# Dependencies: transformers, colorama, torch, matplotlib, numpy
# pip install transformers colorama torch matplotlib numpy
# Models are stored at C:\Users\%username%\.cache\huggingface\hub
import time
import random
import torch
import array
import numpy as np
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, Conversation, set_seed
from colorama import Fore, Back, Style, init

kishida

package naoki.slm;

import javax.imageio.ImageIO;
import javax.swing.*;
import java.awt.*;
import java.awt.datatransfer.DataFlavor;
import java.awt.dnd.DnDConstants;
import java.awt.dnd.DropTarget;
import java.awt.dnd.DropTargetDropEvent;
import java.awt.image.BufferedImage;