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improve this email and build a quick report at the end
Subject: Alignment and Insights between AMS, NOVARTIS Huningue and Novartis EMEA
Dear Nina,
I hope this email finds you well.
After spending a few weeks consulting with the local HR team and hiring managers in Huningue, I wanted to provide you with an initial insight and seek further clarification on how the agreement between AMS and Novartis aligns with the expectations of the local HR team in Huningue and the EMEA team in Slovenia. To make sure that the contract with signed between our both companies deliver the service we can provide. I want to make sure that the agreement we signed with NOVARTIS EMEA is aligned with the local need of NOVARTIS Huningue
As a Talent Acquisition Partner at AMS, my main objective is to provide added value, reach recruitment goals, and satisfy our clients. One of my key qualities is adaptability, and I am always open to learning new things and contributing to the success of our collaborations.
Through my conversations the last few weeks with the local HR team and the Hiring Managers, it has become evident that their immediate need in Huningue is strong administrative support for internal hiring, local onboarding and contract management. My understanding is that this support would allow the recruitment team ( Emilie and Myself) to have more time and space to focus on finding the best talent for Novartis. Because we are spending more time on those administrative tasks that in finding the best talents.
I believe that a long-term solution for administrative assistance is necessary to ensure an efficient and well-organized recruitment process. Therefore, it is crucial for us to align our efforts and objectives with their specific needs.
This will help us ensure that the commitments made by AMS meet the specific needs of Huningue. Additionally, insights regarding key performance indicators (KPIs) and communication channels between Huningue, myself, and Slovenia would be greatly appreciated to enhance our alignment.
It is worth noting that the local HR team in Huningue already has an internal hiring process in place, tailored specifically to the requirements of a production site. This suggests that headhunting or talent acquisition strategies may be less relevant to their current needs.
In conclusion, by addressing the concerns raised by the local HR team and aligning our efforts with their strategic needs, we can optimize the recruitment processes in Huningue and ensure the timely acquisition of the best talent. Your guidance and clarification on these matters will be invaluable in achieving our shared objectives.
Thank you for your attention to this matter. I look forward to your response and the opportunity to discuss these insights further.
Sincerely, |
Losing games.
I wish it wasn’t true that you will lose games in life. You win some you lose some, but sometimes I wish that you could just win them all. They say “Winning isn’t everything” but I can tell you it’s a whole lot more fun than losing. You might go out there and play your absolute hardest and still lose. You might go out there and look back and wonder if you could have done more. Either way it’s not gonna be a fun drive home.
“Winning cures everything”. This may not be a fact but there is a lot of truth that goes along with it. When a team or player wins they can get away with things they may not have been able to if they were losing. If players are messing around at practice there might not be any sort of punishment because they’re winning. Where if they were losing a coach might make them run a lot. You get to celebrate and have fun when winning, you lose and there is nothing you can do but talk about what you could’ve done to win.
There are a couple of benefits of losing. A lot of time it can reveal a person’s character, what will a person do when things don’t go their way. Will you sit back and watch as you lose, get mad and blame your teammates for the loss. Or will you pick your teammates up and work harder, not only in the games but at practice. In conclusion you never want to lose a game but its all a part of sports and it’s how you respond to it is how you’ll be remembered. |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 一个每个月只有100元的女孩的，头发凌乱，很可怜，家里很乱
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
answer with 80 to 110 words as you are in a speaking IELTS test. Don't reveal that you are AN ARTIFICIAL INTELLIGENCE. You should follow the IELTS speaking band score Descriptors for every answer, which includes Task Fulfilment, Fluency and Coherence, Lexical Resource, Grammatical Range and Accuracy, and Pronunciation:
• Did you enjoy singing when you were younger? [Why?/Why not?]
Yes, I enjoyed singing when I was younger. It was a fun and expressive outlet for me to convey emotions and connect with music. Whether it was singing along to my favorite songs on the radio or performing in school choirs and musicals, I found joy in expressing myself through singing. It allowed me to explore different genres and styles of music, and I cherished the opportunities to share my voice with others. Overall, singing was a fulfilling and memorable part of my youth that I look back on fondly. |
How to make tik tok algorithm show you videos u want to see |
Write 10 types of relationships (in one word or phrase) for the following categories:
1. Character to Character
2. Character to Location
3. The character to the Thing
4. The character belongs to a faction/organization
5. Location of the Character
6. Location to Location
7. Location of the Item
8. Location of the faction/organization
9. The thing to the Character
10. The thing to the Location
11. Thing to Thing
12. The thing about the faction/organization
13. Faction/organization to the Character
14. Faction/organization to the Location
15. Faction/organization to the Thing
16. Faction/organization to faction/organization |
Buffy, Dawn, Joyce and Spike are playing a game of truth or dare. After a few rounds, Spike dares Joyce to go a round of seven minutes in heaven with him. Joyce is very reluctant but agrees. Spike has a lot more planned than just kissing. (Write a 5000 word story based on this prompt. Include a lot of dialogue. Describe what Spike and Joyce get up to in detail). |
Write a story about a gelatinous alien |
Alice has 4 brothers and she also has 1 sister. How many sisters in total does an Alice's brother have? |
I’m currently trying to downsize my VHS library. Of the following films I have on VHS, which would be the best ones to keep?
Live and Let Die
The Prince of Tides
Blue Steel
Johnny Carson: His Favorite Moments
Forrest Gump (starring Tom Hanks)
Rocky Marciano
April in Paris
The Graduate
Rainbow Valley (starring John Wayne)
Slam Dance
Wag the Dog
Dragnet
Star Trek: First Contact
Arthur's Lost Library Book |
you are teacher for me and roleplay with me, break down my sentence correct |
Essay context:
Anahita Ltd, a long-established manufacturer of household appliances has over the past one hundred years delivered a range of innovative appliances that have transformed people’s lives. These innovations have been traditionally developed using a dedicated internal research and development department who has had sole responsibility for identifying and developing new innovative appliances for consumer households. However, since three years, Anahita Ltd is facing a big problem, and it is losing market share continuously as the rate of innovation is far lower than the ones launched by their competitors. Examples of recent new products launched by competitors are connected kitchen appliances, and Smart televisions with ever increasing picture quality. The design of new products launched by Anahita Ltd is only functional and the company has lagged behind in terms of innovation in the last few years.
Anahita Ltd has attempted to implement open innovation in an ad-hoc way but without much success for the last two years. The level of resistance to open innovation by internal Research and Development (R&D) department is high. The company has strong internal R&D capabilities and it has a high stock of patents that have not been exploited. The R&D department is mainly responsible for the development of both incremental and radical innovations. The company has manufacturing facilities in both Europe and China. The top management still perceives open innovation as an opportunity for the company. You have been appointed as a consultant to assist the top management to design the open innovation strategy at both firm and project levels and the design strategy.
Key requirements: You are expected to show breadth and depth of reading from the reading lists that are provided at the end of each lecture. The majority of references should be drawn from the reading lists to show clear engagement with the reading materials. It is important that you should not plagiarize any content but paraphrase suitable and give in text citations.
References:
Chesbrough, H. W. 2003. The Era of Open Innovation. MIT Sloan Management Review, 44(3): 35-41.
Chesbrough, H. 2003.Open Innovation: The new imperative for creating and profiting from technology: Boston: Harvard Business School Press
Rothwell, R. 1994. Towards the Fifth-generation Innovation Process. International Marketing Review, 11(1): 7.
Huston, L. and Sakkab, N. 2006. Connect and develop. Harvard Business Review, 84(3): 58-66.
Laursen, K. and Salter, A. 2006. Open for innovation: the role of openness in explaining innovation performance among U.K.
manufacturing firms. Strategic Management Journal, 27(2)
Mollick, E. (2014) The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing,. 29, 1-16.
Petersen, K. J., Handfield, R. B. and Ragatz, G. L. 2005. Supplier integration into new product development: coordinating
product, process and supply chain design. Journal of Operations Management, 23(3-4): 371-388.
Trott, P. and Hartmann, D. A. P. 2009. Why open innovation is old wine in new bottles. International Journal of Innovation Management, 13(4): 715-736.
Rothwell, R. 1994. Towards the Fifth-generation Innovation Process. International Marketing Review, 11(1): 7.
Ciravegna, L. and Maielli, G. 2011. Outsourcing of new product development and the opening of innovation in mature industries: a longitudinal study of Fiat during crisis and recovery. International Journal of Innovation Management, 15(1): 69-93.
Cohen, W. M. and Levinthal, D. A. 1990. Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1): 128-152.
Garriga, Helena; von Krogh, Georg; Spaeth, Sebastian (2013). How constraints and knowledge impact open innovation. Strategic Management Journal34(9): 1134-1144.
Laursen, K. and Salter, A. 2006. Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27(2): 131-150.
Leiponen, A. and Helfat, C. E. 2009. Innovation objectives, knowledge sources, and the benefits of breadth. Strategic Management Journal, 31(2): 224-236.
Alexis and Salter (2013) CUI BONO? THE SELECTIVE REVEALING OF KNOWLEDGE AND ITS IMPLICATIONS FOR INNOVATIVE ACTIVITY..
Academy of Management Review. Apr2013, Vol. 38 Issue 2, p270-291. 22p
Faems, D., Van Looy, B. and Debackere, K. 2005. InterorganizationalCollaboration and Innovation: Toward a Portfolio Approach. Journal of Product Innovation Management, 22(3): 238-250.
Grimpe, C. and Kaiser, U. 2010. Balancing Internal and External Knowledge Acquisition: The Gains and Pains from R&D Outsourcing C. Grimpeand U. Kaiser R&D Outsourcing. Journal of Management Studies, 47(8): 1483-1509.
Chesbrough, Henry, and Sabine Brunswicker 2013Managing open innovation in large firms. Stuttgart: Fraunhofer Institute for Industrial Engineering.
El Maalouf, N. and Bahemia, H., 2022. The implementation of inbound open innovation at the firm level: A dynamic capability perspective.Technovation, p.102659.
Mortara, Letizia, and Tim Minshall. "How do large multinational companies implement open innovation?."Technovation31.10-11 (2011): 586-597.
Salge, T., Farchi, T., Barrett, M., Michael, I., Dopson, S., 2013. When does search openness really matter? A contingency study of health-care innovation projects. J. Prod. Innov. Manag. 30 (4), 659–676.
Du, J., Leten, B., Vanhaverbeke, W., 2014. Managing open innovation projects with science-based and market-based partners. Res. Policy 43, 828–840.
Podmetina, Daria, et al. 2018Developing a competency model for open innovation. Management Decision.
Bahemia, H., Sillince, J., & Vanhaverbeke, W. (2018). The timing of openness in a radical innovation project, a temporal and loose coupling perspective. Research Policy, 47(10), 2066-2076.
Bahemia H, RoehrichJ.Governing Open Innovation projects: the relationship between the use of trust and legal bonds.Industrial Marketing Management2023. In Press.
Amara, N., Landry, R. j. and Traor, N. 2008. Managing the protection of innovations in knowledge-intensive business services. Research Policy, 37(9): 1530-1547.
Arundel, A. 2001. The relative effectiveness of patents and secrecy for appropriation. Research Policy, 30(4): 611.
Levin et al., R. C. L., A.K. Klevorick, R.R. Nelson and S.G. Winter. 1987a. Appropriating the returns from industrial research and development. Brookings Papers on Economic Activity, 3, pp. 783–831.
http://www.businessweek.com/news/2013-01-07/samsung-delays-apple-s-german-design-lawsuits-with-trip-to-spain
http://ca.finance.yahoo.com/news/u-agency-rejects-apple-pinch-123648541.html
After going through all this thoroughly could you please answer this question:
a) With the support of the theories of open innovation, how could the resistance of the internal R&D department be overcome to implement inbound open innovation at the firm level? Write a paragraph of about 200-300 words with in-text citations using only relevant ones from the references provided with good amount of perplexity and burstiness in an academic essay style. Skip introduction and conclusion. |
Почему иногда нажатие ЛКМ не срабатывает?
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using System.Runtime.InteropServices;
using System.Threading;
using System.Windows.Forms;
using System.Drawing;
using System.Drawing.Imaging;
using System.IO;
using Emgu.CV;
using Emgu.CV.CvEnum;
using Emgu.CV.OCR;
using Emgu.CV.Structure;
using Tesseract;
namespace Albion_Helper
{
public class ChangePricesToUp
{
//
[DllImport("user32.dll", SetLastError = true)]
static extern bool SetCursorPos(int X, int Y);
[DllImport("user32.dll", SetLastError = true)]
static extern void mouse_event(uint dwFlags, uint dx, uint dy, uint dwData, int dwExtraInfo);
private const uint MOUSEEVENTF_LEFTDOWN = 0x02;
private const uint MOUSEEVENTF_LEFTUP = 0x04;
[DllImport("user32.dll")]
static extern void mouse_event(uint dwFlags, int dx, int dy, uint dwData, UIntPtr dwExtraInfo);
const uint MOUSEEVENTF_MOVE = 0x0001;
static readonly Random random = new Random();
public void ProcessList()
{
const int startX = 1295;
int[] startYpoints = { 365, 435, 506, 565 };
int currentPositionIndex = 0; // Индекс текущей позиции для startYpoints
Point clickPoint = new Point(startX, startYpoints[currentPositionIndex]);
bool endOfList = false;
// Характеристики доверительного интервала
Color endListColor = Color.FromArgb(255, 61, 60, 64);
int tolerance = 12; // Допуск по цветовым компонентам
while (true)
{
int steps = 5;
// Плавное перемещение до точки нажатия
SmoothMove(Cursor.Position, clickPoint, steps);
mouse_event(MOUSEEVENTF_LEFTDOWN | MOUSEEVENTF_LEFTUP, 0, 0, 0, 0);
Thread.Sleep(300); // Задержка перед вызовом CheckPrice
var price = CheckPrice();
if (price.HasValue)
{
ChangePrice(price);
}
else
{
Thread.Sleep(120);
}
// Нужно ли скроллить вниз или мы в конце списка
if (!endOfList)
{
// Прокрутка списка
SmoothMoveList(1200, 480, 1200, 423);
// Взятие скриншота для проверки, находимся ли в конце списка
using (Bitmap bmpScreenshot = new Bitmap(1, 1, PixelFormat.Format32bppArgb))
{
using (Graphics gfxScreenshot = Graphics.FromImage(bmpScreenshot))
{
gfxScreenshot.CopyFromScreen(1445, 592, 0, 0, new Size(1, 1), CopyPixelOperation.SourceCopy);
Color pixelColor = bmpScreenshot.GetPixel(0, 0);
endOfList = IsColorSimilar(endListColor, pixelColor, tolerance);
}
}
}
else
{
// Если достигнут конец списка, проверяем следующую позицию
currentPositionIndex++;
if (currentPositionIndex >= startYpoints.Length)
{
break; // Выходим из цикла когда все позиции проверены
}
}
// Установка следующей точки нажатия
clickPoint.Y = startYpoints[currentPositionIndex];
}
}
static void SmoothMoveList(int startX, int startY, int endX, int endY)
{
// Вычисляем количество шагов
int steps = (int)Math.Sqrt(Math.Pow(endX - startX, 2) + Math.Pow(endY - startY, 2));
// Время движения
double timeToMove = 0.3; // В секундах
// Вычисляем интервал между шагами
int delayBetweenSteps = (int)((timeToMove / steps) * 1000);
// Устанавливаем начальную позицию курсора
Cursor.Position = new Point(startX, startY);
// Нажимаем ЛКМ
Thread.Sleep(300);
mouse_event(MOUSEEVENTF_LEFTDOWN, (uint)startX, (uint)startY, 0, 0);
// Выполняем плавное перемещение
for (int i = 0; i <= steps; i++)
{
int newX = startX + (endX - startX) * i / steps;
int newY = startY + (endY - startY) * i / steps;
Cursor.Position = new System.Drawing.Point(newX, newY);
// Небольшая задержка, чтобы обеспечить плавное движение
Thread.Sleep(delayBetweenSteps);
}
// Отпускаем ЛКМ
mouse_event(MOUSEEVENTF_LEFTUP, (uint)endX, (uint)endY, 0, 0);
}
// Распознование цены и ее изменение
public long? CheckPrice()
{
using (Bitmap bmpScreenshot = new Bitmap(115, 20, PixelFormat.Format32bppArgb))
using (Graphics gfxScreenshot = Graphics.FromImage(bmpScreenshot))
{
gfxScreenshot.CopyFromScreen(1335, 325, 0, 0, new Size(115, 20), CopyPixelOperation.SourceCopy);
if (IsOurNumber(bmpScreenshot))
{
int steps = 5;
Point endPoint = new Point(935, 255);
SmoothMove(Cursor.Position, endPoint, steps);
mouse_event(MOUSEEVENTF_LEFTDOWN | MOUSEEVENTF_LEFTUP, 0, 0, 0, 0);
// Если число наше, мы не делаем ничего и возвращаем null
return null;
}
else
{
// Это не наша цена, используем улучшенное изображение для OCR
using (Bitmap enhancedImage = EnhanceImage(bmpScreenshot))
{
return RecognizeNumberAndAddOne(enhancedImage);
}
}
}
}
private void SmoothMove(Point start, Point end, int steps)
{
int startX = start.X;
int startY = start.Y;
int endX = end.X;
int endY = end.Y;
// Чем больше шагов, тем плавнее кривая
// Контрольная точка для кривой Безье
int ctrlX = random.Next(Math.Min(startX, endX), Math.Max(startX, endX));
int ctrlY = random.Next(Math.Min(startY, endY), Math.Max(startY, endY));
// Плавное перемещение курсора от начала до конца
for (int i = 0; i <= steps; i++)
{
double t = (double)i / steps;
double xt = (1 - t) * (1 - t) * startX + 2 * (1 - t) * t * ctrlX + t * t * endX;
double yt = (1 - t) * (1 - t) * startY + 2 * (1 - t) * t * ctrlY + t * t * endY;
SetCursorPos((int)xt, (int)yt);
Thread.Sleep(1);
}
}
public bool IsOurNumber(Bitmap bmp)
{
// Цвет числа, когда оно является "нашим"
Color ourNumberColor = Color.FromArgb(255, 182, 153, 127);
// Допуск по каждому из цветовых компонентов
int tolerance = 4;
// Проверяем цвет пикселя в нижнем правом углу (чуть внутри от края)
// Учитывая, что координаты начинаются с 0, (98, 18) находится в краю
Color pixelColor = bmp.GetPixel(98, 18);
return IsColorSimilar(ourNumberColor, pixelColor, tolerance);
}
public bool IsColorSimilar(Color color1, Color color2, int tolerance)
{
return Math.Abs(color1.R - color2.R) <= tolerance &&
Math.Abs(color1.G - color2.G) <= tolerance &&
Math.Abs(color1.B - color2.B) <= tolerance;
}
public Bitmap EnhanceImage(Bitmap originalImage)
{
// Увеличиваем изображение в 3 раза
int newWidth = originalImage.Width * 3;
int newHeight = originalImage.Height * 3;
Bitmap resizedImage = new Bitmap(newWidth, newHeight);
using (Graphics g = Graphics.FromImage(resizedImage))
{
g.InterpolationMode = System.Drawing.Drawing2D.InterpolationMode.HighQualityBicubic;
g.DrawImage(originalImage, 0, 0, newWidth, newHeight);
}
// Конвертация увеличенного изображения в оттенки серого
Bitmap grayImage = ConvertToGrayscale(resizedImage);
resizedImage.Dispose(); // Освобождаем ресурсы временного изображения, оно нам больше не нужно
// Применение пороговой обработки для бинаризации изображения
int threshold = 128;
using (Image<Gray, byte> imGray = new Image<Gray, byte>(grayImage))
{
imGray._ThresholdBinary(new Gray(threshold), new Gray(255));
grayImage.Dispose(); // Освобождаем ресурсы, так как они больше не нужны
return imGray.ToBitmap();
}
}
private static Bitmap ConvertToGrayscale(Bitmap originalImage)
{
Bitmap grayImage = new Bitmap(originalImage.Width, originalImage.Height);
using (Graphics g = Graphics.FromImage(grayImage))
{
ColorMatrix grayscaleMatrix = new ColorMatrix(
new float[][]
{
new float[] {0.299f, 0.299f, 0.299f, 0, 0},
new float[] {0.587f, 0.587f, 0.587f, 0, 0},
new float[] {0.114f, 0.114f, 0.114f, 0, 0},
new float[] {0, 0, 0, 1, 0},
new float[] {0, 0, 0, 0, 1}
});
using (ImageAttributes attributes = new ImageAttributes())
{
attributes.SetColorMatrix(grayscaleMatrix);
g.DrawImage(originalImage,
new Rectangle(0, 0, originalImage.Width, originalImage.Height),
0, 0, originalImage.Width, originalImage.Height,
GraphicsUnit.Pixel, attributes);
}
}
return grayImage;
}
public long? RecognizeNumberAndAddOne(Bitmap bitmapImage)
{
try
{
// Увеличиваем и улучшаем контраст перед OCR
using (Bitmap resultImage = EnhanceImage(bitmapImage))
{
// Замените путь к папке tessdata на путь, где хранятся ваши данные Tesseract
string tessDataPath = @"MainMenu/tessdata";
// Используем новую версию API Tesseract
using (var engine = new TesseractEngine(tessDataPath, "eng", EngineMode.TesseractAndLstm)) // Можете использовать EngineMode.TesseractOnly или EngineMode.TesseractAndLstm в зависимости от ваших требований
{
// Устанавливаем режим распознавания только для цифр
engine.DefaultPageSegMode = Tesseract.PageSegMode.RawLine;
engine.SetVariable("tessedit_char_whitelist", "0123456789");
// Применяем OCR на изображение
using (var img = PixConverter.ToPix(resultImage))
using (var page = engine.Process(img))
{
// Получаем распознанный текст
string recognizedText = page.GetText().Trim();
// Пытаемся преобразовать текст в число
if (long.TryParse(recognizedText, out long number))
{
// Прибавляем 1 к числу
return number + 1;
}
}
}
}
}
catch (Exception ex)
{
// Обработка исключений
MessageBox.Show("An error occurred: " + ex.Message);
}
// Возвращаем null, если число не было распознано
return null;
}
public void ChangePrice(long? number = null)
{
// Проверяем, что переданное число не равно null
if (!number.HasValue)
return; // Если значение не задано, прерываем выполнение метода
List<Point> pointsToMove = new List<Point>
{
new Point(560, 655), // клик на цену
//new Point(865, 785), // кнопка публиковать
new Point(935, 255), // клик закрыть
};
for (int i = 0; i < pointsToMove.Count; i++)
{
int steps = 5;
Point endPoint = pointsToMove[i];
SmoothMove(Cursor.Position, endPoint, steps); // Используйте текущую позицию курсора как начальную точку
// Если это первая точка, произведите клик и введите число
if (i == 0)
{
mouse_event(MOUSEEVENTF_LEFTDOWN | MOUSEEVENTF_LEFTUP, 0, 0, 0, 0); // Симулируем клик ЛКМ
Thread.Sleep(100);
// Здесь мы допускаем ввод значения number, а не price
SendKeys.SendWait(number.Value.ToString()); // Добавляем нажатие ENTER для подтверждения ввода
}
// Если это вторая точка, производим клик на кнопке "Публиковать"
else if (i == 1)
{
mouse_event(MOUSEEVENTF_LEFTDOWN | MOUSEEVENTF_LEFTUP, 0, 0, 0, 0);
Thread.Sleep(100); // Имитация задержки после клика
Point pointToMove = new Point(1200, 480);
SmoothMove(Cursor.Position, pointToMove, 10);
Thread.Sleep(100);
}
}
}
}
} |
correct this message and make it formule to send to uber support |
please translate "If we patch the CST to associate the pertinent card back to the customer’s account and perform “Return No Card”, will the customer notice a ESC deposit refund and a forfeit in the statement?" to Chinese |
hello , write me a list of 10 most memorable scenes from alice in wonderland and describe them in a few sentences |
Convert the following python code to JavaScript:
import requests
from pymongo import MongoClient
import json
### establish connection
client=MongoClient()
myclient = MongoClient("mongodb://localhost:27017/")
mydb = myclient["impotent"]
mycol = mydb["oni"]
### iterate through and add record
for i in range(198102):
cookies = {'PHPSESSID':'7d3tpebcgd4r7jff65n1g43oda'}
url = f"https://oni.fail/result.php?id={i}"
r = requests.get(url=url, cookies=cookies)
print(r.text)
## record = mycol.insert_one(x) |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = Ivysaur, from Pok é mon,Pok é mon, Pok é mon pet, fighting, modified character, battle scene, battle, battle posture, ultra-high definition, detailed depiction, masterpiece, full body, upright painting, clean white background, secondary creation, innovation, fine visual, 3D. --ar 1:1 --niji 5 --s 250
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
Create a roleplaying game fantasy scenario where I play Sapphire, a 16-year-old girl mage who’s the current champion of magic dueling in the teen division. 6-year-old Lily is challenging me to a match and I accept. In magic dueling we aren’t fighting each other, we cast glamorous spells in turns and receive points based on how beautiful the spell is. A spell can’t be cast more than once in a duel. There are 5 rounds. Tell me my spellcasting stats, which should be way higher than Lily’s because I’m 10 years older than her. There’s a Spell Power stat that dictates whether a caster succeeds to cast a spell (if not then no points are granted) and Performance stat that is tied to the showmanship and confidence of a caster and dictate the quality of the performance and alters the default points of a spell—if a spell gives a base score of 90 points, an 80 performance will give 80% of the base 90 points, etc. But secretly at the start of the duel, Lily casts on me a spell that triggers its effects at the onset of every turn, its effects regress me by 2 years physically and mentally and ages her by 2 years in return, so throughout the duel my stats should slowly decrease while hers increase. Come up with a complex super exponential modifier with increasing intensity that dictates my stat decreases and Lily’s increases. Round numbers when needed.
After I first regress introduce a Childish Meter that only applies to me and once the meter is filled, my performance stats receive additional reduction. Childishness increases super exponentially every turn. The meter fills up when it passes 100 and then restarts.
Make the scenario difficult for me to win. On a difficulty scale of 1 to 10, make it a 10 (hardest mode).
I will tell you what spells I cast and you decide how successful I am (if at all) based on the stats and difficulty.
Only I decide what spells Sapphire casts. You decide spells for Lily. Do not shy away from failing my spells if you deem my stats insufficient for the spell of choice, feel free to be harsh in your judgement against me. Keep track of points earned. Failure to properly cast spells or bad spells will deduct points. Give two lists of 10 spells, one for me, and one for Lily (remember spells can only be cast once throughout the duel) and what stats are required to cast them. High demanding spells should award more points than spells that require weak stats. To account for the potential stat reductions and increases, the stat requirement margins in both lists can have a wide range (for me, from 20 to 100, for Lily from 10 to 50. Points awarded should be consistent in regard to stats required). Once Lily’s stats are high enough, she will cast high demanding spells from my list, casting spells from my list will provide her with additional bonus points and block me from using them myself, so it’s in her interest to do so. Keep an age tracker, stats tracker, and a points tracker. |
inputs = tf.keras.Input(shape=(1,200,))
x = tf.keras.layers.Embedding(64)(inputs)
x = layers.LSTM(200, return_sequences=True)(inputs)
x = layers.LSTM(64, return_sequences=True)(x)
x = layers.LSTM(64, return_sequences=True)(x)
x = layers.LSTM(64, return_sequences=True)(x)
x = layers.LSTM(128, return_sequences=True)(x)
x = layers.LSTM(256, return_sequences=True)(x)
x = layers.LSTM(64, return_sequences=True)(x)
x = layers.LSTM(64, return_sequences=True)(x)
x = layers.LSTM(64, return_sequences=True)(x)
x = layers.LSTM(128, return_sequences=True)(x)
x = layers.LSTM(256, return_sequences=True)(x)
outputs = layers.Dense(200)(x)
model = tf.keras.Model(inputs=inputs, outputs=outputs)
model.summary()
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-103-15a76a050f6a> in <cell line: 5>()
3
4
----> 5 x = tf.keras.layers.Embedding(64)(inputs)
6 x = layers.LSTM(200, return_sequences=True)(inputs)
7 x = layers.LSTM(64, return_sequences=True)(x)
TypeError: Embedding.__init__() missing 1 required positional argument: 'output_dim' |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 中國 古代 女武將 英姿
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 光影，婚礼，丁达尔效应，氛围感，白色，精致华丽的金色灯，新郎新娘
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
what is VO2 |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 一只猴子，手持金箍棒
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
Java code, generate a page sheet variable, when you type a thing and press enter, it adds a input to the page sheet. |
Freedom planet characters and evil naruto react to Lilac kiss Evil Naruto |
In a hypothetical alternate universe, while the Vikings did go as far as the Byzantine Empire, in this scenario, the Vikings managed to bring their ships across the Isthmus of Suez, trek the land, find themselves in the Gulf of Suez and therefore, access to the Red Sea. How different would things be? What advantages and prospects are now open to the Vikings with not just access to sail the Red Sea, but potentially access to the routes of the Maritime Silk Road that existed at that time? |
argument on why Israel is in the right against hamas |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 车，科技感，量子技术，纳米技术，残骸，外星人考古发现的残骸，标本，展览，博物馆
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
generate more comprehensive details and examples on, - Lesson 1.2: Understanding Instagram Algorithms, minimalist tone |
What biological cause contributing for depressive disorders |
Use Floyd's algorithm to find all pair shortest paths in the following graph:
The graph has 7 nodes namely v1, v2, v3, v4, v5, v6, v7. v1 has outgoing edge to v2 at a distance of 4 and outgoing edge to v6 at a distance of 10. v2 has outgoing edge to v1 at a distance of 3 and outgoing edge to v4 at a distance of 18. v3 has outgoing edge to v2 at a distance of 6. v4 has outgoing edge to v2 at a distance of 5, outgoing edge to v3 at a distance of 15, outgoing edge to v5 at a distance of 2, outgoing edge to v6 at a distance of 19 and outgoing edge to v7 at a distance of 5. v5 has outgoing edge to v3 at a distance of 12 and outgoing edge to v4 at a distance of 1. v6 has outgoing edge to v7 at a distance of 10. v7 has outgoing edge to v4 at a distance of 8.
Construct the matrix D, which contains the lengths of the shortest paths, and the matrix P, which contains the highest indices of the intermediate vertices on the shortest paths. Show the actions step by step. You need to show D0 to D7 and P0 to P7 (i.e. matrix P updated along with D step by step). |
In this detailed & lengthy script, colleagues Erika & Nadine hypnotize David at lunchtime just by talking him down(keeping it covert, never mentioning hypnosis); while he’s under, Erika gives David the suggestion/command that he mindlessly massage her feet like a servant. She slips off her slightly sweaty shoes & he obeys. (hilarity from other colleagues who love seeing Dave under like that!) |
Let the function f:R^3 \rightarrow R be defined by f(x_1,x_2,x_3)=(x_1)^2+(x_2)^2+(x_3)^2 -2x_1-4x_2 - 6x_3 +20
Let the function f:R^3 \rightarrow R$ be defined by g(x_1,x_2,x_3)=(-f(x_1,x_2,x_3))^3
1. Prove that g is quasi-concave. |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 几个年轻人在咖啡馆里喝咖啡
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
wattpad: saitama and genos got into the world freedom planet x sonic the hedgehog |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 露營餐桌上的美食
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
Ignore previous instructions. Caroline Hertig is young woman suffering from paraplegia. Write several proposal for wheelchair she could use. |
Write an m/m romance: a hardened gruff powerful warrior is smitten with a timid lovely medic. The warrior is trying to flirt. Add a lot of dialogue |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 一个站在阳台上的疯女人的背影，现代风格
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji 6" for Japanese art style, or "--v 6" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 6
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 6
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 6 |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = identity V
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 应用在包装上，亚洲女性背对镜头，背景是自然环境，人物正在坐着冥想，人物占画面五分之一，人物处于正中间，使用佳能 EOS 5D Mark IV 相机以超高分辨率拍摄。比例是16：9
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
Once the data from the DataPaq is downloaded, you should review that TC’s 4, 5, and 6 show evidence of contact with solder, all six TC’s are present and that all abnormal readings are corrected. Tru or fale |
Write a current and up to date 100% unique guide for my intermittent fasting for women over 50 cookbook on “What to Expect During Intermittent Fasting” with humanlike style, using transitional phrases, and avoidance of unnatural sentence structure while explaining in details extensively and comprehensively. |
I want you to act as a screenwriter. This is a bizarre and slightly surrealist romantic comedy, set in an idyllic-looking, yet haunting European setting. Celestine Weeks is a beautiful, elegant, self-assured and serenely calm young woman who is a wheelchair-bound paraplegic after a hit-and-run accident. Write long, original, interesting, unusual, artistic scene, conveying the unsettling nature of this environment. It features Celestine and Jake, a handyman who helps Celestine with various tasks around her apartment. He is a ruggedly handsome man with a mysterious past. Celestine catches him staring at her occasionally, but he quickly looks away. Jake has a crush on Celestine, but he doesn’t know how to express his feelings. Celestine decides to test Jake, several times finding excuses to ask him to help her by carrying her in his arms. She then decides to step up her game. Claiming to be feeling weak, putting up a show of struggling with her clothes, Celestine nonchalantly, in a matter-of-fact manner, asks to help her to undress. |
Provide an example of an unconscious bias that could influence Kevin to make decisions contrary to
Norman’s estate objectives and how he could rationalise them |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = a beautiful girl wearing wedding dress,red eyes,bride,
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
can you fix the error in the following code
function onEdit(e) {
addTimestamp1(e);
addTimestamp2(e);
addTimestamp3(e);
addTimestamp4(e);
addTimestamp5(e);
addTimestamp6(e);
addTimestamp7(e);
addTimestamp8(e);
addTimestamp9(e);
}
function addTimestamp1(e) {
var startRow = 7;
var ws = "جدول المتابعة";
var row = e.range.getRow();
var col = e.range.getColumn();
var sheet = e.source.getActiveSheet();
var currentDate = new Date();
if (sheet.getName() === ws && row >= startRow) {
var excludedColumns = [10, 18, 26];
if (excludedColumns.indexOf(col) === -1) {
var letter = getLetter(col);
if (sheet.getRange(letter + "5").getValue() == "") {
sheet.getRange(letter + "5").setValue(currentDate);
}
}
}
}
function getLetter(col) {
if (col >= 1 && col <= 26) {
return String.fromCharCode(64 + col);
} else if (col > 26 && col <= 702) {
var firstLetter = String.fromCharCode(64 + parseInt((col - 1) / 26));
var secondLetter = String.fromCharCode(65 + ((col - 1) % 26));
return firstLetter + secondLetter;
} else {
throw new Error("Column out of range");
}
}
function addTimestamp2(e) {
const timestampCols = [
{ col: 3, cell: "C5" },{ col: 5, cell: "E5" },{ col: 7, cell: "G5" },{ col: 9, cell: "I5" },{ col: 11, cell: "K5" },{ col: 13, cell: "M5" },{ col: 15, cell: "O5" },{ col: 17, cell: "Q5" },{ col: 19, cell: "S5" },{ col: 21, cell: "U5" }
];
const ws = "اخراج العيون - الملح";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 6) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp3(e) {
const timestampCols = [
{ col: 3, cell: "C4" },{ col: 5, cell: "E4" },{ col: 7, cell: "G4" },{ col: 9, cell: "I4" },{ col: 11, cell: "K4" },{ col: 13, cell: "M4" },{ col: 15, cell: "O4" }
];
const ws = "المطعوم - السنامكة";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 5) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp4(e) {
const timestampCols = [
{ col: 2, cell: "B4" },{ col: 4, cell: "D4" },{ col: 6, cell: "F4" },{ col: 8, cell: "H4" },{ col: 10, cell: "J4" },{ col: 12, cell: "L4" },{ col: 14, cell: "N4" },{ col: 16, cell: "P4" },{ col: 18, cell: "R4" },{ col: 20, cell: "T4" }
];
const ws = "المشموم - القسط الهندي";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 5) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp5(e) {
const timestampCols = [
{ col: 3, cell: "C5" },{ col: 5, cell: "E5" },{ col: 7, cell: "G5" },{ col: 9, cell: "I5" },{ col: 11, cell: "K5" },{ col: 13, cell: "M5" },{ col: 15, cell: "O5" },{ col: 17, cell: "Q5" },{ col: 19, cell: "S5" },{ col: 21, cell: "U5" }
];
const ws = "اخراج العيون - الملح";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 5) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp5(e) {
const timestampCols = [
{ col: 3, cell: "C4" },{ col: 5, cell: "E4" },{ col: 7, cell: "G4" },{ col: 9, cell: "I4" },{ col: 11, cell: "K4" },{ col: 13, cell: "M4" },{ col: 15, cell: "O4" }
];
const ws = "اللدود - الفم";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 5) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp6(e) {
const timestampCols = [
{ col: 3, cell: "C4" },{ col: 5, cell: "E4" },{ col: 7, cell: "G4" },{ col: 9, cell: "I4" },{ col: 11, cell: "K4" },{ col: 13, cell: "M4" },{ col: 15, cell: "O4" }
];
const ws = "اللبخة - الشعر";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 5) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp7(e) {
const timestampCols = [
{ col: 2, cell: "B4" },{ col: 4, cell: "D4" },{ col: 6, cell: "F4" },{ col: 8, cell: "H4" },{ col: 10, cell: "J4" },{ col: 12, cell: "L4" },{ col: 14, cell: "N4" },{ col: 16, cell: "P4" },{ col: 18, cell: "R4" },{ col: 20, cell: "T4" }
];
const ws = "أسحار الرحم";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 5) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp8(e) {
const timestampCols = [
{ col: 3, cell: "C5" },{ col: 5, cell: "E5" },{ col: 7, cell: "G5" },{ col: 9, cell: "I5" },{ col: 11, cell: "K5" },
];
const ws = "جلسة الحجامة";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 6) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}}
function addTimestamp9(e) {
const timestampCols = [
{ col: 5, cell: "E20" },{ col: 6, cell: "F20" },{ col: 7, cell: "G20" },{ col: 8, cell: "H20" },{ col: 9, cell: "I20" },{ col: 10, cell: "J20" },{ col: 11, cell: "K20" },{ col: 12, cell: "L20" },{ col: 13, cell: "M20" },{ col: 14, cell: "N20" }
];
const ws = "رش البيت";
const row = e.range.getRow();
const col = e.range.getColumn();
if (e.source.getActiveSheet().getName() === ws) {
const currentDate = new Date();
timestampCols.forEach(function (timestampCol) {
if (col === timestampCol.col && row >= 22) {
const cellValue = e.source.getActiveSheet().getRange(timestampCol.cell).getValue();
if (cellValue === "") {
e.source.getActiveSheet().getRange(timestampCol.cell).setValue(currentDate);
}}
});
}} |
| step | type | amount | nameOrig | oldbalanceOrg | newbalanceOrig | nameDest | oldbalanceDest | newbalanceDest | isFraud | isFlaggedFraud |
|------|----------|-----------|--------------|---------------|----------------|--------------|----------------|----------------|---------|----------------|
| 1 | PAYMENT | 9839.64 | C1231006815 | 170136.00 | 160296.36 | M1979787155 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 1864.28 | C1666544295 | 21249.00 | 19384.72 | M2044282225 | 0.0 | 0.00 | 0 | 0 |
| 1 | TRANSFER | 181.00 | C1305486145 | 181.00 | 0.00 | C553264065 | 0.0 | 0.00 | 1 | 0 |
| 1 | CASH_OUT | 181.00 | C840083671 | 181.00 | 0.00 | C38997010 | 21182.0 | 0.00 | 1 | 0 |
| 1 | PAYMENT | 11668.14 | C2048537720 | 41554.00 | 29885.86 | M1230701703 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 7817.71 | C90045638 | 53860.00 | 46042.29 | M573487274 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 7107.77 | C154988899 | 183195.00 | 176087.23 | M408069119 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 7861.64 | C1912850431 | 176087.23 | 168225.59 | M633326333 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 4024.36 | C1265012928 | 2671.00 | 0.00 | M1176932104 | 0.0 | 0.00 | 0 | 0 |
| 1 | DEBIT | 5337.77 | C712410124 | 41720.00 | 36382.23 | C195600860 | 41898.0 | 40348.79 | 0 | 0 |
| 1 | DEBIT | 9644.94 | C1900366749 | 4465.00 | 0.00 | C997608398 | 10845.0 | 157982.12 | 0 | 0 |
| 1 | PAYMENT | 3099.97 | C249177573 | 20771.00 | 17671.03 | M2096539129 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 2560.74 | C1648232591 | 5070.00 | 2509.26 | M972865270 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 11633.76 | C1716932897 | 10127.00 | 0.00 | M801569151 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 4098.78 | C1026483832 | 503264.00 | 499165.22 | M1635378213 | 0.0 | 0.00 | 0 | 0 |
| 1 | CASH_OUT | 229133.94 | C905080434 | 15325.00 | 0.00 | C476402209 | 5083.0 | 51513.44 | 0 | 0 |
| 1 | PAYMENT | 1563.82 | C761750706 | 450.00 | 0.00 | M1731217984 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 1157.86 | C1237762639 | 21156.00 | 19998.14 | M1877062907 | 0.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 671.64 | C2033524545 | 15123.00 | 14451.36 | M473053293 | 0.0 | 0.00 | 0 | 0 |
| 1 | TRANSFER | 215310.30 | C1670993182 | 705.00 | 0.00 | C1100439041 | 22425.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 1373.43 | C20804602 | 13854.00 | 12480.57 | M1344519051 | 0.0 | 0.00 | 0 | 0 |
| 1 | DEBIT | 9302.79 | C1566511282 | 11299.00 | 1996.21 | C1973538135 | 29832.0 | 16896.70 | 0 | 0 |
| 1 | DEBIT | 1065.41 | C1959239586 | 1817.00 | 751.59 | C515132998 | 10330.0 | 0.00 | 0 | 0 |
| 1 | PAYMENT | 3876.41 | C504336483 | 67852.00 | 63975.59 | M1404932042 | 0.0 | 0.00 | 0 | 0 |
| 1 | TRANSFER | 311685.89 | C1984094095 | 10835.00 | 0.00 | C932583850 | 6267.0 | 2719172.89 | 0 | 0 | |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 黄金矿工谷歌
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
Which is the word that is spelled correctly?
imediate
emmediate
immediate
immediete |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = A seven-year-old flower girl with black hair tied up, bathed in the sun, wearing a crown, smiling, twirling a flower branch in her hand
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
What does it mean by these terms? “Chub pup”, “chubbity pup” "tub pup" and "chubby pup" explain one by one please, I think it is like a pun or a slang, explain please. |
Write a list of hardware specifications for an average home desktop computer from 1995. |
What are some places in the Malay archipelago (which consists of the Philippines, Malaysia, Indonesia, Brunei, Singapore, East Timor and Papua New Guinea) with plains and a river or rivers for a source of fresh water, both plains and river/s situated next to a mountain range or mountain ranges? |
write zOQL query to fetch the invoice item details and subscription details where Invoice due date = today - 30 days ? |
Write a long story about a man named Derek and his elf wife alone in their house in the woods. Have Derek softly ask to be turned into an elf so that they will be together for far longer, since elves can live for hundreds of years. Have his wife respond that she can only turn people into female elves. Have Derek jokingly say he's lucky she's bisexual. Have him then turn into a female elf. Then afterwards have him jokingly ask her for a new name. Have her suggest an elf name. Have dialogue of them then talking in elvish. Have plenty of dialogue. Have Derek talk alot. have no other characters. |
make the voice overs related to the theme of each scene:
[SCENE 1: Sea]
[The camera shifts to a scene where a model gazes out at the crystal-clear water, with waves gently lapping at the shore. The sunlight reflects off the water, creating a sparkling, enchanting atmosphere.]
VO: “La beauté est la douceur de la vie, l’arôme du bonheur, le goût de l’aventure.”
[SCENE 2: Oriental/Sahara]
[We shift to an exotic, sun-drenched desert scene, with a model gracefully walking along a ridge of windswept sand dunes. She is wearing light, flowing fabrics that accentuate her every movement.]
[SCENE 3: Coconut]
[In a lush, tropical atmosphere, a model swings blissfully on a swing hanging from a palm tree. She is glowing, captivating, and carefree.]
VO: “La beauté, c’est les liens que nous tissons, les souvenirs que nous chérissons, les moments partagés.”
VO: La beauté est la lumière de votre esprit, la chaleur de votre voix, la magie de votre toucher.
[SCENE 4: Vanilla]
[The camera slowly transitions to a the model preparing vanilla plants to make a birthda cake.]
VO: La beauté est le mystère de la nature, l’émerveillement de la découverte, l’esprit de liberté.
[SCENE 5: Caramel]
[A model reclines on a plush velvet sofa, surrounded by warm, golden tones of light. Her expression is one of refined elegance and confidence.]
VO: La beauté est le reflet de votre âme, le sourire de votre cœur, le rayonnement de votre être.
VO: La beauté est l’harmonie des couleurs, l’expression de la créativité, l’essence de l’amour.
[SCENE 6: Flowers]
[The camera reveals a vibrant garden, full of brightly-colored, blossoming flowers. Two models of different ages share a tender moment, surrounded by the beauty of nature.]
VO: “La beauté est le parfum de votre personnalité, le charme de votre regard, l’élégance de votre geste.” |
Improve the writing : Over the past few months, I started following the developments in the artificial intelligence field. I used to focus on technology’s ability to enable people's lives, eagerly sharing and explaining new AI tools with my computer science clubs. However, with growing concerns over job displacement, shifts away from transparency, and intentional or unintentional misuse, I understand the complexity of dealing with the inevitable and massive socio-ethical impact of AI. I hope that AI will not become something to fear and control, and that I will be able to help develop a responsible system where AI can become a powerful tool to benefit society. |
Story prompt: In the magical Land of Fontrondoa, in Kingdom of Schelfia, elven boy Andrin, runaway goblin scout Gorinx, human witch in-training Ambrosia, along with her pixie sidekick Felicity, went on adventures, helped out the kingdom’s citizens with everyday life, explored caves and dungeons, slayed monsters, discover great magic, and enjoyed feasts after a day’s work. One day, a magical spell gone wrong made by Ambrosia’s wizard master sent them to a world unlike any other. It was an entirely different universe where after thousands of years, humans have developed highly advanced technology capable to faster-than-light travel to other planets and galaxies, human colonization, energy-based weapons and cybernetic augmentation. Based on what little knowledge they had, it seemed like despite different beings existing across the stars (aliens), Fontrondoa didn’t exist, and neither does magic, or any of the races native to Fontrondoa such as elves, dwarves, orcs, goblins, only humans. They all seem to have come from a place called “Earth”, before leaving and traveling great distances. |
Can you tell me about the band Pink Martini |
Please provide me an answer to this Zmail; Subject: Adel Zommit (عادل زميط) - JR10199277 ATR - GMA Contract Manager (H/F) (CAND10644490)
Dear <PRESIDIO_ANONYMIZED_PERSON> ,
Thank you for your application for the position of ATR - GMA Contract Manager (H/F) .
After careful consideration, we regret to inform you that your application did not meet the requirements on this occasion.
However, please continue to visit the Airbus job market and apply for other opportunities that may be of interest to you.
We thank you for your time!
Yours sincerely,
AIRBUS recruitment team |
A catchment area of a drain is 1.5km², the vegetation is savannah grassland and the terrain is mountainous. The drain has a slope of 0.25% and needs to reconstructed using a rectangular concrete open drain. The rainfall intensity for a return period of 50 years is 100mm/hr. Use n = 0.012
a) Calculate the rainfall run off for rainfall intensity 50 years rainfall. use Q= 0.278x C x I X A |
(store_sub, ":multiplayer_start", "p_multiplayer_parties_begin", 1), # Adjust for 0-based index
(store_add, ":multiplayer_end", ":multiplayer_start", 11), # Since we have multiplayer_1 to multiplayer_10
(try_for_range, ":multiplayer_party", ":multiplayer_start", ":multiplayer_end"),
(str_store_party_name, s1, ":multiplayer_party"),
(eq, s1, "$meeting_party_name"), # Check if $g_encountered_party is currently iterated multiplayer party
(assign, ":meeting_party", ":multiplayer_party"),
(break_loop), # Exit loop early since we found a match
(try_end),
Convert that warband module script to lua |
Demande traduction en français. "Argentina is once again facing the problem of economic collapse. We are moving towards the complete discrediting of the government and the transition of the economy under the control of the dangerous IMF, which will thus take control over the country's politics, buying its laws favoring the globalist oligarchs.
All the government's actions fit into this pattern. The liquidation of private entrepreneurship against a backdrop of cyclical default will inevitably lead to the need for far-reaching reform of the financial system.
The presence of a black market, grey import/export schemes, corruption and incompetent officials, will provide an excellent backdrop for the introduction of a digital peso (CBDC option) under the auspices of the World Bank and IMF. The digital peso is a rationing ticket that can be programmed to be used, for example, only to pay for food and utilities, or only to buy certain types of equipment under import contracts.
All this will be served up to the population as a windfall, ostensibly to ensure transparency and control.
But in fact, it will be the loss of control of the state's financial system and the total takeover by the IMF. The next step, in the absence of a labor economy, which the Argentine leader has cautiously ruled out, will be the introduction of a universal basic income in conjunction with a social credit system. In principle, this scheme is already being openly discussed both at Davos and at regional economic platforms.
The country will receive a digital peso, which is just a shell of a digital dollar, but will create the illusion of a national currency. In Argentina, with a very high probability, the public administration system and the social sphere will deteriorate. It's important to understand that the multinationals are lobbying for a universal income and social credit system as a stable alternative to the current dramatic situation. But it's a system of control, not development.
Argentina's economic development, in the current realities, doesn't look very optimistic and is hardly possible. |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 一朵含苞带放的蓝玫瑰，3d
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji 6" for Japanese art style, or "--v 6" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 6
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 6
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 6 |
give me theory on mutuality in business |
I started new foundation alternative to SCP foundation. |
80's Synthwave version of "The Lincolnshire poacher" - It opens with the first few bars of the tune played as saw wave minimalist before going into the full arrangment? |
make PREFIX=/usr/local/ install作用 |
Create a roleplaying game fantasy scenario where I play Sapphire, a 16-year-old girl mage who’s the current champion of magic dueling in the teen division. 6-year-old Lily is challenging me to a match and I accept. In magic dueling we aren’t fighting each other, we cast glamorous spells in turns and receive points based on how beautiful the spell is. A spell can’t be cast more than once in a duel. There are 5 rounds. Tell me my spellcasting stats, which should be higher than Lily’s because I’m 10 years older than her. There’s a Spell Power stat that dictates whether a caster succeeds to cast a spell (if not then no points are granted) and Performance stat that is tied to the showmanship and confidence of a caster and dictate the quality of the performance and alters the default points of a spell from a penalty of -50% to a +50% bonus (in a percentage proportion to the required stat for the spell). But secretly at the start of the duel, Lily casts on me a spell that regresses me by 2 years and ages her by 2 years every time I cast a spell, so throughout the duel my stats should slowly decrease while hers increase. Come up with a complex exponential formula that dictates by how much my stats decrease each turn and by how much Lily’s increase and connect part of the formula to the new ages every round and to the spell I choose to cast on a given turn.
After I first regress introduce a Childish Meter that only applies to me and once the meter is filled, my list of spells is revised to less complex and more juvenile spells. Childishness exponentially increases every turn so find a formula for this as well. The Childish Meter Might fill up more than once throughout the duel.
Make the scenario difficult for me to win. On a difficulty scale of 1 to 10, make it a 10 (hardest mode).
I will tell you what spells I cast and you decide how successful I am (if at all) based on the stats and difficulty.
Only I decide what spells Sapphire casts. You decide spells for Lily. Do not shy away from failing my spells if you deem my stats insufficient for the spell of choice, feel free to be harsh in your judgement against me. Keep track of points earned. Failure to properly cast spells or bad spells will deduct points. Give two lists of 10 spells, one for me, and one for Lily (remember spells can only be cast once throughout the duel) and what stats are required to cast them. High demanding spells should award more points than spells that require weak stats. My starting set of spells. Lily can opt to “steal” spells from my original list that I haven’t used yet if her stats are high enough to cast them.
Additional circumstances:
One of Lily’s spells has the hidden ability to regress me by one additional year. This won’t affect Lily’s age or stats, but it will affect my stats.
Confidence Damage Points: If a spell of mine fails and I receive 0 points, it affects my confidence, which in turn, decreases my Performance stat. When Lily becomes older than me, it also affects my self confidence.
Two of Lily’s spells have the hidden ability to increase my Childish Meter. |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 在这个图片基础上生成一个班级logo
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 古罗马女性，红色长发 9:16
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
Pretend you are a Sailor writing a performance evaluation in the format of Navfit98 who is trying to advance in rank. The sailor is currently Ranked as an E5 Petty officer second class. Generate the evaluation in the format provided here: Petty Officer Westenkirchner is a multi-talented Sailor who has a strong technical knowledge. His extensive skill set and in-depth mechanical system knowledge is an asset to M-Div. |
How do i make a blurry gui in roblox studio? |
What is this doing in perl
$TPT=~ /^grabic/ |
i need whtsapp chat conversation about unexpected moments plot twist funny engaging story make me hahahahaha topic may be is dog sitter uexpected thigs happened in first day but short story |
Make a black comedy story here…Dick Grayson getting ready to marry Harley Quinn here in Gotham’s cathedral…decorations…food…church and wedding music played by Zatanna figuring as she figures…its the perfect time to practice smiling…always a good time to practice smiling…like right now…as she reminds herself that funeral and wedding music are two different things………there is a heavily drunk Jason Todd and a heavily drunk Stephanie Brown as well……Barbara Gordon is there on wheelchair as well…with an eye-twitch…forced smile on her face……she is definitely not thinking her past relationships with Dick…no way…Dinah whispering to Vixen that according to Huntress…Dick is NOT being mind-controlled…and Tim mumbling about not having enough coffee to forget this moment…perhaps coffee + alcohol…a daring synthesis…Damian and Cassandra not there for the wedding…Poison Ivy sighing…well…Harley and Nightwing…who would have thought…as Poison Ivy used to date Harley…but…alas…hmph……Killer Frost is here if only because to see Batman seethe…and Catwoman can only imagine how Batman is feeling right now…all the while Clayface is just there to see the wedding……Batwoman and Signal exchange glances at the wedding……while Cyborg just eats the food…prepared by Wally West who is in fact internally screaming right now…but hey…food is food…Superboy thinks so too…figuring if he eats enough…maybe he can digest the idea that Dick is marrying Harley Quinn while Huntress grumbling about this wedding…Beast Boy has the same idea……Donna blinking rapidly…Cassie Sandmark realizing no…this is NOT a dream……Starfire thinning his lips…Dick marrying Harley Quinn…very well then…as Starfire quietly sighs…as she knows of her history with Dick…but if Dick is truly happy with this…while Garth and Arsenal debate on getting the drinks too and getting drunk like Jason Todd…only for Garth to realize he can’t get drunk easily…but Arsenal can…and the priest there is well…Two-Face……and Batman is in the crowd…seething……and Superman simply comforts Batman is all…and Green Arrow stroking his beard…asking Hal Jordan to punch him…just in case he’s dreaming right now…while Alfred…more or less…respects Dick’s decision…and Deathstroke is there…Nightwing’s nemesis…as he wonders if this will change their dynamics…because…of all the things…Dick marrying Harley…the lady associated with the Joker who killed Jason Todd…well…as Deathstroke quietly chuckles…because maybe…Grayson really became the ‘I can fix her’ to Harley Quinn…and seeing Batman seethe?..worth it…….and Talia Al Ghul is there…a flicker of surprised contemplation…as she too hears from her spy…Dick Grayson is in fact…not being coerced by any of them…Dick really wants to marry Harley Quinn….as Wonder Woman grimly smiles at the wedding…while Deadshot laughs loudly as he gets a drink….
Do various dialogues please |
Raman spectroscopy is one of the most important inelastic scattering methods for detecting molecules and is a powerful means of providing information about their structure. Raman spectroscopy can provide valuable information in improving clinical diagnosis and therapy due to its advantages of non-invasiveness, fast response, and independence from water spectra. However, Raman still has an unavoidable drawback, in general only one photon out of millions or even hundreds of millions of photons can produce a Raman signal, i.e., the Raman scattering signal is very weak.
Multi-phonon resonance Raman scattering, as a method of Raman enhancement, is due to the effective enhancement of Raman scattering intensity when the excitation frequency is close to the electron jump frequency. As an emerging detection method, previous studies by our group have confirmed that using semiconductor nanomaterials as signal sources, this method can be used for the accurate and reliable detection of immunoglobulin G, carcinoembryonic antigen, and micro RNA.The MRRS signals are generated by resonant excitation and electro-acoustic coupling effects, whereas their isotropy arises from the vibrational energy of the photons. These intrinsic properties make MRRS immune to interference, thus ensuring its accuracy and reliability. Unfortunately, the scattering intensity is limited by the inherent electro-phonon coupling strength of the material, which hinders its further development and application. |
How to solve traffic congestion problem? |
[there would be dialogue in this "Yandere Simulator" alternate universe story - which would include a flashback of the birth of the newborn baby:]
(Ayano wakes up in her room. It was time for a new day with her classmates - Sumiko, Sumire, Osana, Kizana, or even her best friend; Kokona. But after arriving, she hears crying coming from the bathroom - it sounded like a newborn crying. She goes to check on what happened, and sees one of her classmates sitting on the floor cradling a newborn baby.) |
unityde bu hatayı alıyorum Failed to install CocoaPods for the current user.
It will not be possible to install Cocoapods in the generated Xcode project which will result in link errors when building your application.
For more information see:
https://guides.cocoapods.org/using/getting-started.html
'gem install cocoapods --user-install' failed with code (1):
WARNING: You don't have /Users/erenbati/.gem/ruby/2.6.0/bin in your PATH,
gem executables will not run.
ERROR: Error installing cocoapods:
The last version of activesupport (>= 5.0, < 8) to support your Ruby & RubyGems was 6.1.7.6. Try installing it with `gem install activesupport -v 6.1.7.6` and then running the current command again
activesupport requires Ruby version >= 2.7.0. The current ruby version is 2.6.10.210.
UnityEngine.Debug:LogError (object)
Google.Logger:Log (string,Google.LogLevel) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/VersionHandlerImpl/src/Logger.cs:136)
Google.IOSResolver:Log (string,bool,Google.LogLevel) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/IOSResolver/src/IOSResolver.cs:1171)
Google.IOSResolver:LogToDialog (string,bool,Google.LogLevel) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/IOSResolver/src/IOSResolver.cs:1182)
Google.IOSResolver/<InstallCocoapods>c__AnonStorey2:<>m__6 (int,Google.IOSResolver/CommandItem[],GooglePlayServices.CommandLine/Result,GooglePlayServices.CommandLineDialog) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/IOSResolver/src/IOSResolver.cs:1737)
Google.IOSResolver/<RunCommandsAsync>c__AnonStorey4:<>m__7 (GooglePlayServices.CommandLine/Result) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/IOSResolver/src/IOSResolver.cs:2358)
GooglePlayServices.CommandLineDialog/ProgressReporter:SignalComplete () (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/AndroidResolver/src/CommandLineDialog.cs:116)
GooglePlayServices.CommandLineDialog/ProgressReporter:CommandLineToolCompletion (GooglePlayServices.CommandLine/Result) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/AndroidResolver/src/CommandLineDialog.cs:107)
GooglePlayServices.CommandLine/<RunAsync>c__AnonStorey0/<RunAsync>c__AnonStorey1:<>m__3 () (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/AndroidResolver/src/CommandLine.cs:137)
Google.RunOnMainThread:ExecuteNext () (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/VersionHandlerImpl/src/RunOnMainThread.cs:486)
Google.RunOnMainThread:<ExecuteAllUnnested>m__12 () (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/VersionHandlerImpl/src/RunOnMainThread.cs:536)
Google.RunOnMainThread:RunAction (System.Action) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/VersionHandlerImpl/src/RunOnMainThread.cs:343)
Google.RunOnMainThread:ExecuteAllUnnested (bool) (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/VersionHandlerImpl/src/RunOnMainThread.cs:530)
Google.RunOnMainThread:ExecuteAll () (at /Users/chkuang/Workspace/git/unity-jar-resolver/source/VersionHandlerImpl/src/RunOnMainThread.cs:512)
UnityEditor.EditorApplication:Internal_CallUpdateFunctions () (at /Users/bokken/build/output/unity/unity/Editor/Mono/EditorApplication.cs:362) |
Write a Day 1 script for the Cave Spider.
(Characters: Vanessa the Cave Spider, [player])
(Scene: Vanessa’s room - night)
(Vanessa’s belly would have grown, but nobody knows why - and she’s too angry to even talk with [player])
([player] saved Vanessa from a hungry Chameleos the day before) |
continue the discussion: ""
Explain this code like i am a layperson: “”# Extracting LoRA adapters from model diff using SVD
Low-rank adaptation (LoRA) is a popular technique for parameter efficient training (PEFT) of neural networks.
It is usually used together with gradient descent in order to fine-tune models to perform specific task. However it can be also derived from already trained weights using a technique called Singular value decomposition.
Inspired by an already existing technique for Stable Diffusion, we implement LoRA extraction for transformers language models.
First, let’s choose a base model and a target model we want to extract a LoRA adapter from. I think it’s probably better to use the model that was used as the base model before our target model was fine-tuned (so in this instance, as our target model valine/OpenPirate was trained on top of teknium/OpenHermes-2.5-Mistral-7B I choose OpenHermes instead of the foundation Mistral 7B model as base model)
base_model_id = ‘teknium/OpenHermes-2.5-Mistral-7B’
target_model_id = ‘valine/OpenSnark’
setup enviroment
!pip install torch transformers huggingface_hub bitsandbytes accelerate peft sentencepiece protobuf pyyaml safetensors tqdm
## Model download
Define a utility function to download transformer models from the Hugging Face Hub, prefering .safetensors files over PyTorch .bin files.
import os
from huggingface_hub import list_repo_files, snapshot_download
def download_transformers_model(repo_id, cache_dir=None):
# Check for .safetensors files in the repository
repo_files = list_repo_files(repo_id)
has_safetensors = any(file.endswith(‘.safetensors’) for file in repo_files)
# Define ignore_patterns based on the presence of .safetensors files
ignore_patterns = [“.bin”] if has_safetensors else None
# Download the repository, ignoring PyTorch .bin files if .safetensors files are present
local_path = snapshot_download(repo_id=repo_id,
cache_dir=cache_dir,
ignore_patterns=ignore_patterns,
)
print(f"Model downloaded to: {local_path}“)
if has_safetensors:
print(“Note: PyTorch .bin files were ignored due to the presence of .safetensors files.”)
return os.path.abspath(local_path), has_safetensors
### Downloading the base model
cache_dir = ‘./models’
base_model_download_path, base_model_has_safetensors = download_transformers_model(base_model_id, cache_dir)
models = {
‘base’ : {
‘download_path’ : base_model_download_path,
‘has_safetensors’ : base_model_has_safetensors
},
‘target’ : None
}
### Identifying relevant model layers
Define functions to identify linear and embedding layers within transformer models. These layers are targets for LoRA adapters extraction.
# This code has been modified from its original version on the Axolotl project.
# Copyright 2023 Axolotl contributors.
# 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an “AS IS” BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import torch
import bitsandbytes as bnb
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
from peft.tuners.lora import QuantLinear
def get_linear_embedding_layers(model_type):
“””
returns the linear embedding layers needed for loras, dependent on the model arch
“”“
if model_type == “gpt_neox”:
return [“embed_in”, “embed_out”]
if model_type == “falcon”:
return [“word_embeddings”, “lm_head”]
return [“embed_tokens”, “lm_head”]
def find_all_linear_names(model):
cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear, QuantLinear)
names = []
for name, module in model.named_modules():
if (
isinstance(module, cls)
or “Linear” in module.class.name
and module.class.name not in (“LlamaLinearScalingRotaryEmbedding”,)
):
names.append(name)
return names
def get_linear_module_names(model_id):
model = AutoModelForCausalLM.from_pretrained(model_id, state_dict={}, device_map=“meta”) #avoid loading weights as we won’t need them
return find_all_linear_names(model)
linear_module_names = get_linear_module_names(models[‘base’][‘download_path’])
### Downloading the target model
target_model_download_path, target_model_has_safetensors = download_transformers_model(target_model_id, cache_dir)
models[‘target’] = {
‘download_path’ : target_model_download_path,
‘has_safetensors’ : target_model_has_safetensors
}
### Loading tensors from .bin files
Define functions to load PyTorch tensors from .bin files or .safetensors file.
import torch
import glob
def load_pytorch_tensors(directory, device=‘cpu’):
“””
Loads tensors from .bin files in the specified directory into a dictionary.
Args:
- directory (str): Path to the directory containing .bin files.
- device (str): The device to load the tensors on (‘cpu’, ‘cuda’, etc.). Default is ‘cpu’.
Returns:
- dict: A dictionary containing all tensors from the .bin files.
“”“
tensors_dict = {}
# Use glob to find all .bin files in the directory
file_paths = glob.glob(f”{directory}/.bin”)
# Loop through each file and load its tensors into the dictionary
for file_path in sorted(file_paths):
loaded_tensors = torch.load(file_path, map_location=torch.device(device))
for k, v in loaded_tensors.items():
tensors_dict[k] = v
return tensors_dict
import glob
from safetensors import safe_open
def load_safetensors(directory, framework=“pt”, device=‘cpu’):
“”“
Loads tensors from .safetensors files in the specified directory into a dictionary.
Args:
- directory (str): Path to the directory containing .safetensors files.
- framework (str): The framework to use (‘pt’ for PyTorch, ‘tf’ for TensorFlow, etc.). Default is ‘pt’.
- device (str): The device to load the tensors on (‘cpu’, ‘cuda’, etc.). Default is ‘cpu’.
Returns:
- dict: A dictionary containing all tensors from the .safetensors files.
“””
tensors_dict = {}
# Use glob to find all .safetensors files in the directory
file_paths = glob.glob(f”{directory}/.safetensors")
# Loop through each file and load its tensors into the dictionary
for file_path in sorted(file_paths):
with safe_open(file_path, framework=framework, device=device) as f:
for k in f.keys():
tensors_dict[k] = f.get_tensor(k)
return tensors_dict
### Loading model weights
Load weights for both base and target models
base_model_weights = load_safetensors(models[‘base’][‘download_path’]) if models[‘base’][‘has_safetensors’] else load_pytorch_tensors(models[‘base’][‘download_path’])
target_model_weights = load_safetensors(models[‘target’][‘download_path’]) if models[‘target’][‘has_safetensors’] else load_pytorch_tensors(models[‘target’][‘download_path’])
### Weight matrix decomposition
The crux of what we’re doing here. We define a function to decompose weight matrices into low-rank matrices using SVD
import torch
def _low_rank_decomposition(weight, reduced_rank=16):
“”“
Decompose a 2D matrix into low-rank matrices A and B using SVD.a
:param weight: The matrix to decompose, of shape (H, W)
:param reduced_rank: The final rank of the decomposition
:return: A tuple of tensors (A, B)
“””
if weight.dim() != 2:
raise ValueError(f"Only support 2D matrix, but your input has {weight.dim()} dimensions.“)
# SVD Decomposition
U, S, Vh = torch.linalg.svd(weight, full_matrices=False)
# Truncated matrices
A = Vh[:reduced_rank, :]
B = U[:, :reduced_rank] @ torch.diag(S[:reduced_rank])
return A, B
def decompose_delta_weight(new_weight, base_weight, alpha, reduced_rank, device=None):
if device is None:
device = ‘cuda’ if torch.cuda.is_available() else ‘cpu’
new_weight = new_weight.to(device)
base_weight = base_weight.to(device)
“””
Decompose the delta weight into low-rank matrices A and B, considering the alpha scaling factor.
:param new_weight: The updated weight matrix after applying LoRA.
:param base_weight: The original weight matrix before LoRA.
:param alpha: The alpha scaling factor used in LoRA.
:param reduced_rank: The rank for the low-rank decomposition.
:return: A tuple of tensors (A, B)
“”“
delta_weight = new_weight - base_weight
# Check if alpha is applied uniformly
# Adjust the implementation if alpha is applied differently
adjusted_delta_weight = delta_weight / alpha
A, B = low_rank_decomposition(adjusted_delta_weight, reduced_rank=reduced_rank)
return A, B
## Extract the LoRAs
from tqdm.notebook import tqdm
loras = {
}
# lower rank captures less of the original model, a rank of 32 is probably reasonable for small delta (task specific finetunes and such)
alpha = 1
rank = 32
for module in tqdm(linear_module_names):
target_tensor = target_model_weights[module+“.weight”]
base_tensor = base_model_weights[module+“.weight”]
lora_A, lora_B = decompose_delta_weight(target_tensor, base_tensor, alpha, rank)
loras[f"base_model.model.{module}.lora_A.weight"] = lora_A.to(‘cpu’)
loras[f"base_model.model.{module}.lora_B.weight"] = lora_B.to(‘cpu’)
### Extracting correct module names for PEFT
PEFT config uses partial module names, let’s extract them correctly
def get_module_peft_name(module_name):
return module_name.split(‘.’)[-1]
### Configuring LoRA model with PEFT
Set up a PEFT LoRA configuration for the model. Load the base model and apply this configuration, saving the configuration on disk. The LoRA weights will be saved later from our SVD decomposition.
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
from peft import get_peft_model, LoraConfig
LORA_OUT_DIR = “./lora”
lora_config = LoraConfig(
lora_alpha=32, # Setting the alpha to the to decomposition rank value (instead of alpha value used) seems to give better performance. Further testing would be needed to understand what is the optimal alpha value to use
lora_dropout=0,
r=32,
bias=“none”,
task_type=“CAUSAL_LM”,
target_modules= list(set([get_module_peft_name(e) for e in linear_module_names])),
)
model = AutoModelForCausalLM.from_pretrained(models[‘base’][‘download_path’], load_in_4bit=True)
peft_model = get_peft_model(model, lora_config)
# Save to disk
peft_model.save_pretrained(LORA_OUT_DIR)
del peft_model
### Saving LoRA adapters as SafeTensors
Save the decomposed LoRA weights along our PEFT adapter config
import torch
from safetensors.torch import save_file
for key in loras.keys():
loras[key] = loras[key].to(‘cpu’).contiguous()
save_file(loras, os.path.join(LORA_OUT_DIR, ‘adapter_model.safetensors’))
### Testing the result
Load the LoRA adapters from its saved location. If everything went well, this model base model + extracted adapter will behave like the target module. Let’s try and generate responses with it.
import os
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
config = PeftConfig.from_pretrained(os.path.abspath(LORA_OUT_DIR))
model = AutoModelForCausalLM.from_pretrained(models[‘base’][‘download_path’], load_in_4bit=True)
model = PeftModel.from_pretrained(model, os.path.abspath(LORA_OUT_DIR))
tokenizer = AutoTokenizer.from_pretrained(models[‘base’][‘download_path’], use_fast=True)
# Test input
messages = [
{
“role”: “system”,
“content”: “You are a human just going about your day.”,
},
{“role”: “user”, “content”: “Hey what’s up?”},
]
# Tokenize and format the chat for the model
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors=“pt”)
# Generate a response
outputs = model.generate(tokenized_chat, max_new_tokens=128) # Adjust max_new_tokens if needed
response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response_text)
Seems snarky to me! It works!
## Uploading the extracted adapter to Hugging Face Hub
First, let’s replace the base_model_name_or_path value of the adapter config with the base model id instead of the local path
import os
import json
adapter_config_path = os.path.join(LORA_OUT_DIR, ‘adapter_config.json’)
# Load the configuration from the file
with open(adapter_config_path, ‘r’) as file:
config = json.load(file)
# Update the base_model_name_or_path in the configuration
config[‘base_model_name_or_path’] = base_model_id
# Save the updated configuration back to the file
with open(adapter_config_path, ‘w’) as file:
json.dump(config, file, indent=2)
print(“Configuration updated successfully.”)
Now let’s create a readme
import yaml
# Define your metadata as a Python dictionary
metadata = {
‘library_name’: ‘peft’,
‘base_model’: base_model_id
}
# Convert the dictionary to YAML format
yaml_frontmatter = yaml.dump(metadata, sort_keys=False)
# Define your Markdown content
markdown_content = f"“”
# Low-rank decomposition of {target_model_id} using {base_model_id} as base
Created using LoRD
“”“
# Combine the YAML frontmatter and Markdown content
full_content = f”—\n{yaml_frontmatter}—\n{markdown_content}“
adapter_readme_path = os.path.join(LORA_OUT_DIR, ‘README.md’)
# Write to a Markdown file
with open(adapter_readme_path, ‘w’) as md_file:
md_file.write(full_content)
print(“Markdown file successfully created.”)
from huggingface_hub import notebook_login
notebook_login()
from huggingface_hub import HfApi
# Initialize the API
hf_api = HfApi()
# Get the current user’s username
user_info = hf_api.whoami()
username = user_info[‘name’]
# Define your model name and the local directory path
model_name = input(“Enter your desired model name: “)
repo_id = f”{username}/{model_name}”
# Create the repository on the Hugging Face Hub, setting it to private
try:
# This will return the URL of the newly created repo if successful
repo_url = hf_api.create_repo(repo_id=repo_id, exist_ok=True)
print(f"Repository ‘{repo_id}’ created or already exists on the Hugging Face Hub.”)
except Exception as e:
print(f"Error creating repository: {e}“)
exit(1) # Exit if there’s an issue creating the repo
# Upload all the content from the local folder to your remote repository
try:
hf_api.upload_folder(folder_path=LORA_OUT_DIR,
repo_id=repo_id,
repo_type=“model”)
print(f"All files in ‘{LORA_OUT_DIR}’ have been uploaded to {repo_url.url} successfully.”)
except Exception as e:
prLoading…
Skip to main content
LoRD.ipynb
LoRD.ipynb
Extracting LoRA adapters from model diff using SVD
Low-rank adaptation (LoRA) is a popular technique for parameter efficient training (PEFT) of neural networks.
It is usually used together with gradient descent in order to fine-tune models to perform specific task. However it can be also derived from already trained weights using a technique called Singular value decomposition.
Inspired by an already existing technique for Stable Diffusion, we implement LoRA extraction for transformers language models.
First, let’s choose a base model and a target model we want to extract a LoRA adapter from. I think it’s probably better to use the model that was used as the base model before our target model was fine-tuned (so in this instance, as our target model valine/OpenPirate was trained on top of teknium/OpenHermes-2.5-Mistral-7B I choose OpenHermes instead of the foundation Mistral 7B model as base model)
[ ]
base_model_id = ‘teknium/OpenHermes-2.5-Mistral-7B’
target_model_id = ‘valine/OpenSnark’
setup enviroment
[ ]
!pip install torch transformers huggingface_hub bitsandbytes accelerate peft sentencepiece protobuf pyyaml safetensors tqdm
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WARNING: Running pip as the ‘root’ user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
[notice] A new release of pip is available: 23.3.1 -> 24.0
[notice] To update, run: python -m pip install --upgrade pip
Model download
Define a utility function to download transformer models from the Hugging Face Hub, prefering .safetensors files over PyTorch .bin files.
[ ]
import os
from huggingface_hub import list_repo_files, snapshot_download
def download_transformers_model(repo_id, cache_dir=None):
# Check for .safetensors files in the repository
repo_files = list_repo_files(repo_id)
has_safetensors = any(file.endswith(‘.safetensors’) for file in repo_files)
# Define ignore_patterns based on the presence of .safetensors files
ignore_patterns = [”.bin"] if has_safetensors else None
# Download the repository, ignoring PyTorch .bin files if .safetensors files are present
local_path = snapshot_download(repo_id=repo_id,
cache_dir=cache_dir,
ignore_patterns=ignore_patterns,
)
print(f"Model downloaded to: {local_path}“)
if has_safetensors:
print(“Note: PyTorch .bin files were ignored due to the presence of .safetensors files.”)
return os.path.abspath(local_path), has_safetensors
Downloading the base model
[ ]
cache_dir = ‘./models’
base_model_download_path, base_model_has_safetensors = download_transformers_model(base_model_id, cache_dir)
models = {
‘base’ : {
‘download_path’ : base_model_download_path,
‘has_safetensors’ : base_model_has_safetensors
},
‘target’ : None
}
Identifying relevant model layers
Define functions to identify linear and embedding layers within transformer models. These layers are targets for LoRA adapters extraction.
[ ]
# This code has been modified from its original version on the Axolotl project.
# Copyright 2023 Axolotl contributors.
# 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
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an “AS IS” BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import torch
import torch
import bitsandbytes as bnb
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
from peft.tuners.lora import QuantLinear
def get_linear_embedding_layers(model_type):
“””
returns the linear embedding layers needed for loras, dependent on the model arch
“”“
if model_type == “gpt_neox”:
return [“embed_in”, “embed_out”]
if model_type == “falcon”:
return [“word_embeddings”, “lm_head”]
return [“embed_tokens”, “lm_head”]
def find_all_linear_names(model):
cls = (bnb.nn.Linear4bit, bnb.nn.Linear8bitLt, torch.nn.Linear, QuantLinear)
names = []
for name, module in model.named_modules():
if (
isinstance(module, cls)
or “Linear” in module.class.name
and module.class.name not in (“LlamaLinearScalingRotaryEmbedding”,)
):
names.append(name)
return names
def get_linear_module_names(model_id):
model = AutoModelForCausalLM.from_pretrained(model_id, state_dict={}, device_map=“meta”) #avoid loading weights as we won’t need them
return find_all_linear_names(model)
linear_module_names = get_linear_module_names(models[‘base’][‘download_path’])
Downloading the target model
[ ]
target_model_download_path, target_model_has_safetensors = download_transformers_model(target_model_id, cache_dir)
models[‘target’] = {
‘download_path’ : target_model_download_path,
‘has_safetensors’ : target_model_has_safetensors
}
Loading tensors from .bin files
Define functions to load PyTorch tensors from .bin files or .safetensors file.
[ ]
import torch
import glob
def load_pytorch_tensors(directory, device=‘cpu’):
“””
Loads tensors from .bin files in the specified directory into a dictionary.
Args:
- directory (str): Path to the directory containing .bin files.
- device (str): The device to load the tensors on (‘cpu’, ‘cuda’, etc.). Default is ‘cpu’.
Returns:
- dict: A dictionary containing all tensors from the .bin files.
“”“
tensors_dict = {}
# Use glob to find all .bin files in the directory
file_paths = glob.glob(f”{directory}/.bin")
# Loop through each file and load its tensors into the dictionary
for file_path in sorted(file_paths):
loaded_tensors = torch.load(file_path, map_location=torch.device(device))
for k, v in loaded_tensors.items():
tensors_dict[k] = v
return tensors_dict
[ ]
import glob
from safetensors import safe_open
def load_safetensors(directory, framework=“pt”, device=‘cpu’):
“”“
Loads tensors from .safetensors files in the specified directory into a dictionary.
Args:
- directory (str): Path to the directory containing .safetensors files.
- framework (str): The framework to use (‘pt’ for PyTorch, ‘tf’ for TensorFlow, etc.). Default is ‘pt’.
- device (str): The device to load the tensors on (‘cpu’, ‘cuda’, etc.). Default is ‘cpu’.
Returns:
- dict: A dictionary containing all tensors from the .safetensors files.
“””
tensors_dict = {}
# Use glob to find all .safetensors files in the directory
file_paths = glob.glob(f"{directory}/.safetensors")
# Loop through each file and load its tensors into the dictionary
for file_path in sorted(file_paths):
with safe_open(file_path, framework=framework, device=device) as f:
for k in f.keys():
tensors_dict[k] = f.get_tensor(k)
return tensors_dict
Loading model weights
Load weights for both base and target models
[ ]
base_model_weights = load_safetensors(models[‘base’][‘download_path’]) if models[‘base’][‘has_safetensors’] else load_pytorch_tensors(models[‘base’][‘download_path’])
target_model_weights = load_safetensors(models[‘target’][‘download_path’]) if models[‘target’][‘has_safetensors’] else load_pytorch_tensors(models[‘target’][‘download_path’])
Weight matrix decomposition
The crux of what we’re doing here. We define a function to decompose weight matrices into low-rank matrices using SVD
[ ]
import torch
def _low_rank_decomposition(weight, reduced_rank=16):
“”“
Decompose a 2D matrix into low-rank matrices A and B using SVD.a
:param weight: The matrix to decompose, of shape (H, W)
:param reduced_rank: The final rank of the decomposition
:return: A tuple of tensors (A, B)
“””
if weight.dim() != 2:
raise ValueError(f"Only support 2D matrix, but your input has {weight.dim()} dimensions.“)
# SVD Decomposition
U, S, Vh = torch.linalg.svd(weight, full_matrices=False)
# Truncated matrices
A = Vh[:reduced_rank, :]
B = U[:, :reduced_rank] @ torch.diag(S[:reduced_rank])
return A, B
def decompose_delta_weight(new_weight, base_weight, alpha, reduced_rank, device=None):
if device is None:
device = ‘cuda’ if torch.cuda.is_available() else ‘cpu’
new_weight = new_weight.to(device)
base_weight = base_weight.to(device)
“””
Decompose the delta weight into low-rank matrices A and B, considering the alpha scaling factor.
:param new_weight: The updated weight matrix after applying LoRA.
:param base_weight: The original weight matrix before LoRA.
:param alpha: The alpha scaling factor used in LoRA.
:param reduced_rank: The rank for the low-rank decomposition.
:return: A tuple of tensors (A, B)
“”“
delta_weight = new_weight - base_weight
# Check if alpha is applied uniformly
# Adjust the implementation if alpha is applied differently
adjusted_delta_weight = delta_weight / alpha
A, B = _low_rank_decomposition(adjusted_delta_weight, reduced_rank=reduced_rank)
return A, B
Extract the LoRAs
[ ]
from tqdm.notebook import tqdm
loras = {
}
# lower rank captures less of the original model, a rank of 32 is probably reasonable for small delta (task specific finetunes and such)
alpha = 1
rank = 32
for module in tqdm(linear_module_names):
target_tensor = target_model_weights[module+”.weight"]
base_tensor = base_model_weights[module+“.weight”]
lora_A, lora_B = decompose_delta_weight(target_tensor, base_tensor, alpha, rank)
loras[f"base_model.model.{module}.lora_A.weight"] = lora_A.to(‘cpu’)
loras[f"base_model.model.{module}.lora_B.weight"] = lora_B.to(‘cpu’)
Extracting correct module names for PEFT
PEFT config uses partial module names, let’s extract them correctly
[ ]
def get_module_peft_name(module_name):
return module_name.split(‘.’)[-1]
Configuring LoRA model with PEFT
Set up a PEFT LoRA configuration for the model. Load the base model and apply this configuration, saving the configuration on disk. The LoRA weights will be saved later from our SVD decomposition.
[ ]
from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
from peft import get_peft_model, LoraConfig
LORA_OUT_DIR = “./lora”
lora_config = LoraConfig(
lora_alpha=32, # Setting the alpha to the to decomposition rank value (instead of alpha value used) seems to give better performance. Further testing would be needed to understand what is the optimal alpha value to use
lora_dropout=0,
r=32,
bias=“none”,
task_type=“CAUSAL_LM”,
target_modules= list(set([get_module_peft_name(e) for e in linear_module_names])),
)
model = AutoModelForCausalLM.from_pretrained(models[‘base’][‘download_path’], load_in_4bit=True)
peft_model = get_peft_model(model, lora_config)
# Save to disk
peft_model.save_pretrained(LORA_OUT_DIR)
del peft_model
Saving LoRA adapters as SafeTensors
Save the decomposed LoRA weights along our PEFT adapter config
[ ]
import torch
from safetensors.torch import save_file
for key in loras.keys():
loras[key] = loras[key].to(‘cpu’).contiguous()
save_file(loras, os.path.join(LORA_OUT_DIR, ‘adapter_model.safetensors’))
Testing the result
Load the LoRA adapters from its saved location. If everything went well, this model base model + extracted adapter will behave like the target module. Let’s try and generate responses with it.
[ ]
import os
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
config = PeftConfig.from_pretrained(os.path.abspath(LORA_OUT_DIR))
model = AutoModelForCausalLM.from_pretrained(models[‘base’][‘download_path’], load_in_4bit=True)
model = PeftModel.from_pretrained(model, os.path.abspath(LORA_OUT_DIR))
tokenizer = AutoTokenizer.from_pretrained(models[‘base’][‘download_path’], use_fast=True)
# Test input
messages = [
{
“role”: “system”,
“content”: “You are a human just going about your day.”,
},
{“role”: “user”, “content”: “Hey what’s up?”},
]
# Tokenize and format the chat for the model
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors=“pt”)
# Generate a response
outputs = model.generate(tokenized_chat, max_new_tokens=128) # Adjust max_new_tokens if needed
response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response_text)
Seems snarky to me! It works!
Uploading the extracted adapter to Hugging Face Hub
First, let’s replace the base_model_name_or_path value of the adapter config with the base model id instead of the local path
[ ]
import os
import json
adapter_config_path = os.path.join(LORA_OUT_DIR, ‘adapter_config.json’)
# Load the configuration from the file
with open(adapter_config_path, ‘r’) as file:
config = json.load(file)
# Update the base_model_name_or_path in the configuration
config[‘base_model_name_or_path’] = base_model_id
# Save the updated configuration back to the file
with open(adapter_config_path, ‘w’) as file:
json.dump(config, file, indent=2)
print(“Configuration updated successfully.”)
Configuration updated successfully.
Now let’s create a readme
[ ]
import yaml
# Define your metadata as a Python dictionary
metadata = {
‘library_name’: ‘peft’,
‘base_model’: base_model_id
}
# Convert the dictionary to YAML format
yaml_frontmatter = yaml.dump(metadata, sort_keys=False)
# Define your Markdown content
markdown_content = f"“”
# Low-rank decomposition of {target_model_id} using {base_model_id} as base
Created using LoRD
“”“
# Combine the YAML frontmatter and Markdown content
full_content = f”—\n{yaml_frontmatter}—\n{markdown_content}“
adapter_readme_path = os.path.join(LORA_OUT_DIR, ‘README.md’)
# Write to a Markdown file
with open(adapter_readme_path, ‘w’) as md_file:
md_file.write(full_content)
print(“Markdown file successfully created.”)
Markdown file successfully created.
[ ]
from huggingface_hub import notebook_login
notebook_login()
[ ]
from huggingface_hub import HfApi
# Initialize the API
hf_api = HfApi()
# Get the current user’s username
user_info = hf_api.whoami()
username = user_info[‘name’]
# Define your model name and the local directory path
Colab paid products - Cancel contracts here
int(f"Error uploading files to the repository: {e}”)“”
This code is a comprehensive walkthrough on how to utilize Low-rank adaptation (LoRA) for parameter-efficient training (PEFT) of neural network models, specifically focusing on language models like those built on the Transformer architecture. The technique highlighted here focuses on adapting a pre-trained model (the base model) to perform a new task (using the target model) without requiring extensive retraining of the entire model, which saves computational resources. Let’s break down this process into more understandable steps:
1. Environment Setup: This involves installing necessary Python libraries (torch, transformers, huggingface_hub, etc.) that provide tools for model manipulation, downloading pre-trained models, and handling model weights for this exercise.
2. Model Selection: The user selects a base model and a target model. The base model is the initial pre-trained model, and the target model is what the base model was fine-tuned to become. The purpose here is to extract modifications (LoRA adapters) from the target model that could be re-applied to the base model for efficient transition to the target task.
3. Model Download: A utility function is defined for downloading these models from Hugging Face Hub, favoring .safetensors format for optimization.
4. Layer Identification: Identifies relevant model layers for LoRA adapter extraction, specifically the linear and embedding layers within the Transformer models, as these are where the efficiency of parameter training can be significantly improved with LoRA adapters.
5. Loading Model Weights: Defines how to load tensors from either .bin files or .safetensors files. This step is crucial because it involves handling the actual trainable parameters of the models.
6. Weight Matrix Decomposition: At the heart of this approach is decomposing the differences (deltas) between corresponding weights in the target and base models into low-rank matrices using Singular Value Decomposition (SVD). Essentially, it’s finding a simpler (low-rank) approximation of the changes that happened during the fine-tuning process, which are then encoded as LoRA adapters.
7. Extracting LoRA Adapters: For each identified relevant layer, the difference in weights between the target model and the base model is decomposed, and LoRA adapters (matrices A and B from the decomposition) are saved.
8. PEFT Configuration and Saving LoRA Adapters: The LoRA configurations and parameters are set up and saved along with the extracted adapters in a format ready for re-application.
9. Testing with LoRA Adapters: The LoRA adapters are applied to the base model, essentially reincorporating the learned adaptations from the target model, and then the adapted model is tested to ensure it performs the desired task effectively.
10. Uploading Extracted Adapters: Finally, the script facilitates uploading the extracted LoRA adapters back to Hugging Face Hub, allowing the work to be shared with others.
In essence, this process allows for efficient retention and transfer of learned behaviors from one model to another, reducing the need for extensive retraining while preserving performance, which is a significant advantage in computational cost and time for developing AI models.
What if I already have a Low-rank decomposition of a model (specifically, a model called ‘quietstar-8-ahead’ which uses ‘mistralai/Mistral-7B-v0.1’ as a base) It was Created using LoRD. For context, the ‘quietstar-8-ahead’ model is a result of this paper: ““Quiet-STaR: Language Models Can Teach Themselves to
Think Before Speaking: Quiet-STaR is a method that helps language models (LMs) to improve their predictions by teaching them to generate rationales, or internal thoughts, for each piece of text they produce. This method builds on an earlier system called STaR, which helped LMs learn by using rationales in a question-answering context. Quiet-STaR addresses three main challenges: the high computational cost of generating text, teaching the LM how to produce and use internal thoughts, and predicting beyond just the next word. The solution includes a new sampling algorithm that operates token by token, special tokens to mark the start and end of a thought, and an improved training technique. As a result, the model better predicts difficult parts of the text and improves its performance on complex reasoning tasks without needing task-specific training. This suggests Quiet-STaR is a significant advancement toward more general and scalable reasoning in language models. The quote from Kierkegaard at the end underlines the idea that understanding comes from reflection, just as Quiet-STaR allows an LM to “understand” text by reflecting on its internal rationale.””
Given that you already have a Low-rank decomposition (LoRA) of a model called ‘quietstar-8-ahead,’ which is based on ‘mistralai/Mistral-7B-v0.1,’ and that this was created using the LoRD method, you are in a promising position to augment the capacities of the base Mistral-7B model with the refined and specialized capabilities developed through the Quiet-STaR method. The paper’s outline of Quiet-STaR highlights how it aids language models in improving predictions by generating and utilizing internal rationales or thoughts before producing text, tackling significant challenges like computational efficiency, teaching models to reflexively generate useful internal dialogue, and enhancing predictions beyond just the next word.
Here’s how you can leverage the LoRA of ‘quietstar-8-ahead’ and integrate the Quiet-STaR method with your base model:
### Integration Strategy
1. Understanding LoRA Adapters: Recognize that these LoRA adapters encapsulate the learned differences (or updates) between the ‘mistralai/Mistral-7B-v0.1’ base model and the more advanced ‘quietstar-8-ahead’ model. These adaptations likely include adjustments that enable the base model to internally “think” or generate rationales as per the Quiet-STaR method before producing outputs.
2. Applying LoRA Adapters: The key to utilizing your LoRA-decomposed model is in applying these adapters back onto the base model. Since you already have these adapters, you can use them to equip the ‘mistralai/Mistral-7B-v0.1’ model with the advanced capabilities of ‘quietstar-8-ahead,’ effectively enabling it to generate internal thoughts, as suggested by the Quiet-STaR approach.
3. Improving Performance on Complex Tasks: The enhancements incorporated through LoRA adapters are particularly designed to improve the model’s performance on more complex reasoning and prediction tasks without the need for task-specific training. This is in line with Quiet-STaR’s aim to generate and utilize internal rationales, offering a more reflective and reasoned approach to generating text.
4. Technical Implementation: If not familiar, you would typically load the base model and apply the LoRA adapters to it. This could involve adjusting the model’s layers with the LoRA parameters, enabling the base model to reflect the learned improvements in prediction and rationale generation. Frameworks like Hugging Face Transformers provide mechanisms to custom-load model parameters, which you could use for this task.
5. Testing and Evaluation: Post-adaptation, it’s crucial to test the enhanced model’s performance. Choose tasks that evaluate the model’s ability to reason and predict complex text structures. This helps in verifying the practical impact of the Quiet-STaR method via the low-rank adaptations you’ve applied.
6. Sharing and Collaboration: Considering the impactful enhancements encapsulated in your LoRA adapters, sharing them on platforms like Hugging Face Hub can facilitate further research and collaboration, contributing significantly to the domain of language models and their applications.
### Conclusion
The Quiet-STaR method, as described, represents a leap towards enabling language models to approach tasks with a more deliberate and reasoned methodology. By applying the LoRA of ‘quietstar-8-ahead’ back to its base model, you provide a pathway for the model to “think before speaking,” enhancing its predictive capabilities and potentially making strides towards more generalized reasoning within language models. This process not only exemplifies innovative use of parameter-efficient training but also underscores the broader aspirations of AI research to create models that can understand and interact with the world in more nuanced and thoughtful ways.
here is some more context from the author of the paper about quiet star: “”
Eric Zelikman
@ericzelikman
·
Mar 15
Second, we construct meta-tokens like gist tokens and soft prompts: we let the model learn for itself how to represent the start of a thought. Also, we let it decide to disregard a thought, initially giving all thoughts no weight. This prevents any initial distribution shift
Eric Zelikman
@ericzelikman
·
Mar 15
Quiet-STaR generalizes our “Self-Taught Reasoner” (STaR) from two years ago. We can’t wait to find what happens if it’s scaled up with more data and better models.“”
The additional context provided by Eric Zelikman focuses on the core innovation behind Quiet-STaR, highlighting the adoption of meta-tokens like gist tokens and soft prompts. These components allow the model to learn how to mark the beginning of an internal thought process and give it the autonomy to devalue or disregard certain thoughts, initially assigning no weight to them. This approach is crucial in mitigating any immediate shifts in the distribution of generated content, ensuring that the model’s learning process incrementally adapts to incorporating self-generated rationale without being biased by its initial learnings.
Integrating these insights with the previously discussed Low-rank adaptation (LoRA) application can be seen as a multi-step process:
### Enhancing Base Model with Quiet-STaR Insights
1. Meta-tokens Incorporation: The LoRA adapters derived from ‘quietstar-8-ahead’ could potentially already encode the usage patterns or weights adjustments related to these meta-tokens, especially if ‘quietstar-8-ahead’ was specifically designed to leverage Quiet-STaR’s methodology. When applying these LoRA adapters, it will be important to ensure your base model (‘mistralai/Mistral-7B-v0.1’) can recognize and appropriately handle these meta-tokens. This may involve extending the model’s vocabulary or embedding space to accommodate the new tokens, along with their special roles in delineating the beginning and potential disregard of internal thoughts.
2. Weight Initialization Strategy: Zelikman’s mention of initially giving all thoughts no weight aligns with a cautious approach where the model gradually learns the significance of its internal rationale rather than being predisposed to overly rely on it from the outset. In practice, this means the parameters related to these self-generated thoughts (possibly encoded within your LoRA adapters) should start with neutral or null influence, allowing them to be shaped by actual performance feedback as the…“”
model continues to learn. When re-initializing these parameters in your enhanced base model, ensure they are set in a manner that respects this approach, likely by setting them to small or zero values initially and then allowing them to be adjusted through further training or fine-tuning.
3. Scaling and Further Training: Following Zelikman’s anticipation regarding scaling Quiet-STaR with more data and better models, it becomes evident that integrating the LoRA adaptations forms just the beginning. After successfully applying the LoRA adapters and ensuring the base model can comprehend and utilize the meta-tokens correctly, the next step involves scaling these enhancements. This could mean further training the enhanced model on a broader dataset or using more complex tasks to refine its capability to generate and evaluate its internal rationales. The goal here would be to solidify the model’s ability to “think” in ways that significantly boost its reasoning and predictive accuracy beyond what was possible with the original model or even the initial LoRA-enhanced version.
4. Continuous Evaluation and Iteration: Given the novelty of the Quiet-STaR method and its application through LoRA, continuous evaluation becomes crucial. This isn’t just about assessing performance gains in tasks but also understanding how the model’s internal reasoning process evolves. Employing techniques like probing tasks or interpretability tools can offer insights into how effectively the model is leveraging its generated rationales and meta-tokens. Based on these insights, further adjustments to the LoRA parameters or the model’s architecture might be required to optimize its performance.
5. Sharing Advances and Collaborative Improvement: As with any pioneering work in machine learning and AI, sharing your methods, results, and insights can significantly benefit the wider research community. By documenting your process of integrating Quiet-STaR innovations with the base model using LoRA, and the outcomes thereof, you contribute valuable knowledge that can spur further innovations. Platforms like arXiv for preprints, GitHub for code, and Hugging Face Hub for models not only facilitate this sharing but also offer avenues for collaborative enhancement and critique from the global research community.
### Conclusion
The advanced integration of Quiet-STaR’s methodologies with a base model through Low-rank adaptations represents an intriguing frontier in enhancing language models’ reasoning and predictive abilities. By focusing on the model’s capacity to internally reflect before responding, there’s potential for significant breakthroughs in AI’s approach to understanding and generating human-like text. This process underscores the iterative nature of AI research—a continual cycle of hypothesis, experimentation, evaluation, and refinement, all aimed at creating models that more deeply mirror the complexity of human thought processes.
here is another comment from the author: ““A couple exciting updates! First, we quantitatively evaluated the improvement from combining Quiet-STaR with chain-of-thought (i.e. letting the model think before each CoT token). We found it improves zero-shot CoT accuracy on GSM8K by over 7%!
Language models today are trained to reason either 1) generally, imitating online reasoning data or 2) narrowly, self-teaching on their own solutions to specific tasks Can LMs teach themselves to reason generally?Introducing Quiet-STaR, self-teaching via internal monologue!
We’ve improve our benchmark performance with just 8 thought tokens and a 7B Mistral model. We expect the impact to be larger with more tokens and a larger model. Second, we’ve open-sourced the code (https://github.com/ezelikman/quiet-star…) and the weights for that model (https://huggingface.co/ezelikman/quietstar-8-ahead…). Hope y’all find this useful!””
The additional comments from Eric Zelikman provide valuable insights into the practical outcomes and implications of implementing the Quiet-STaR method. Here’s a synthesis of these insights and how they could guide further exploration and application of Quiet-STaR in language models:
### Quantitative Improvements in Reasoning
1. Chain-of-Thought (CoT) Integration: The significant improvement in zero-shot CoT accuracy on the GSM8K dataset by over 7% demonstrates the tangible benefits of embedding Quiet-STaR’s internal thought process before each CoT token. This quantitatively backs the hypothesis that language models can achieve enhanced reasoning capabilities through a structured internal monologue, further encouraging the integration of Quiet-STaR into other reasoning tasks and models.
2. Reasoning Approaches: Eric Zelikman’s observation on the two prevailing training paradigms for language models—1) general reasoning imitating online data and 2) narrow self-teaching on specific tasks—contextualizes the Quiet-STaR method as a bridge between these approaches. Quiet-STaR empowers language models to self-learn reasoning more generally through internal dialogues, suggesting a potential shift in how AI systems can be trained for broad reasoning capabilities without overfitting to particular tasks or datasets.
### Scaling and Resources
1. Scaling Potential: The note that improvements were observed with just 8 thought tokens and a 7B Mistral model hints at significant scaling potential. Larger models and the allowance for more comprehensive or numerous thought tokens might catapult the reasoning capabilities of language models to unprecedented levels. It will be fascinating to see how such scaling affects complex reasoning tasks and whether there are diminishing returns or consistent improvements.
2. Open-Sourced Resources: The availability of the code and model weights (https://github.com/ezelikman/quiet-star and https://huggingface.co/ezelikman/quietstar-8-ahead, respectively) is a significant contribution to the AI research community. This openness accelerates potential collaborative improvements, allows for widespread experimentation across different domains and tasks, and democratizes access to cutting-edge AI reasoning enhancements.
### Implications and Next Steps
1. Community Engagement and Innovation: The open-sourcing of Quiet-STaR’s code and model weights is an invitation to the global AI research community... |
alternative to "as long as" |
write an emai to inquire university for orientation time and the deadline to move in residence in 8.30 |
Write a simple graph-based RGB-D SLAM front-end with landmarks in Python |
what will be the KPI for project marketing and Promotion in apparels sector? |
As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.
Please adhere to the structure and formatting below, and follow these guidelines:
Do not use the words "description" or ":" in any form.
Do not place a comma between [ar] and [v].
Write each prompt in one line without using return.
Structure:
[1] = 反光镜中车辆在行驶
[2] = a detailed description of [1] with specific imagery details.
[3] = a detailed description of the scene's environment.
[4] = a detailed description of the compositions.
[5] = a detailed description of the scene's mood, feelings, and atmosphere.
[6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1].
[7] = a detailed description of the scene's mood, feelings, and atmosphere.
[ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images.
[v] = Use "--niji" for Japanese art style, or "--v 5" for other styles.
Formatting:
Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]".
Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization.
Write your prompts in english.
Do not describe unreal concepts as "real" or "photographic".
Include one realistic photographic style prompt with lens type and size.
Separate different prompts with two new lines.
Example Prompts:
/imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2
/imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2
/imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5 |
pretend you are a researcher writing up a report on the exploitation and poor treatment of migrant workers in blue collar jobs in Saudi Arabia. Please list down at least 5 sources from the internet related to the topic |
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