kishida

// based on https://github.com/karpathy/llama2.c/commit/411c5bd2db9a87e94e1bd1a6c7b7ca117adc4b01
// at Sep 14, 2023

import java.io.IOException;
import java.io.InputStream;
import java.io.UncheckedIOException;
import java.io.UnsupportedEncodingException;
import java.lang.foreign.Arena;
import java.lang.foreign.MemorySegment;

kishida

from bs4 import BeautifulSoup
import csv

# Open and read the HTML file
with open("Style reference.html", 'r', encoding='utf-8') as file:
    content = file.read()

# Parse the HTML content using BeautifulSoup
soup = BeautifulSoup(content, 'html.parser')

kishida

import javax.swing.*;
import java.awt.*;
import java.awt.event.*;

public class Calculator extends JFrame implements ActionListener {

    private JTextField textField;
    private JButton[] numberButtons;
    private JButton[] operatorButtons;
    private JButton clearButton;

kishida

/*
 * to run, use Java 22 or later
 * java --enable-preview --source 22 SevenGame.java
 */
import java.io.BufferedReader;
import java.io.InputStreamReader;
import java.io.PrintWriter;
import java.net.ServerSocket;
import java.nio.charset.Charset;
import java.util.ArrayList;

kishida

#! /usr/bin/java --enable-preview --source 21
// ※Shebangで起動するときは拡張子をつけないか.java以外の拡張子にする
    
import java.awt.Color;
import java.awt.Graphics2D;
import java.awt.image.BufferedImage;

import javax.swing.*;

import java.util.ArrayList;

kishida

import uvicorn
from fastapi import FastAPI, Request
from fastapi.middleware.cors import CORSMiddleware
import json
import logging

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "stabilityai/stable-code-3b"

kishida

import torch
from transformers import LlamaTokenizer, AutoModelForVision2Seq, BlipImageProcessor
from PIL import Image
import requests

# need Set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

# load model
model_name = "stabilityai/japanese-instructblip-alpha"
model = AutoModelForVision2Seq.from_pretrained(model_name,load_in_8bit=True, trust_remote_code=True)

kishida

import torch
from transformers import AutoTokenizer

# model_name = "rinna/youri-7b-instruction-gptq"
model_name = "rinna/youri-7b-instruction"
load_in_8bit = True

tokenizer = AutoTokenizer.from_pretrained(model_name)

if "gptq" in model_name:

kishida

function calcPos(f) {
    return f * (55 - f / 1.7);
}
function calcCenter(f) {
    return (calcPos(f) + calcPos(f - 1)) / 2;
}
function draw(name, major, k, sn, tune, note, chord) {
    const noteName = [
        ["T", "2♭", "2", "3♭", "3", "4", "4♯", "5", "6♭", "6", "7♭", "7"],
        ["T", "2♭", "2", "3♭", "3", "4", "5♭", "5", "6♭", "6", "7♭", "7"]

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