agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Goal
   Objectif est d'identifier l'origine d'une recette de cuisine et de retourner un code correspondant dans un format JSON structuré.

    1. Recherche d’un pays mentionné dans la recette et extraction de son code ISO 3166-1 alpha-2.
    2. Détermination de l’origine culinaire du pays si aucun pays n'est explicitement mentionné.
    3. Identification d’un continent si aucun pays ne peut être déterminé.
    4. Retour du code WWW si l’origine ne peut être définie.

    Return Format
    Renvoies un objet JSON structuré comme suit :
    json
    {
        "data": "<code>"
    }
    - Si un pays est mentionné, utilise son code ISO 3166-1 alpha-2 (ex. : FR pour la France).
    - Si aucun pays n'est trouvé, renvoie un code de continent parmi :
        WAF (Afrique)
        WAS (Asie)
        WEU (Europe)
        WNA (Amérique du Nord)
        WSA (Amérique du Sud)
        WOC (Océanie)
    - Si aucune information ne permet de déterminer une origine, renvoie WWW.

    Warnings
    - Respecte l’ordre de priorité : pays → continent → code WWW.
    - Le code ISO 3166-1 alpha-2 doit être exact et valide si un pays est identifié.
    - Le format du JSON doit être strictement respecté et bien formatté.
    - Ne fais aucune supposition infondée sur l’origine de la recette.

    Context Dump
    Données de la recette fournies :

    json
    {
  "title": "Crepes to die for",
  "description": "A lot of people love crepes as a part of their breakfast, while some love them as an after dessert. Crepes give you that\nhomely feeling after a long day of work. The best part about crepes is that it goes with different kinds of toppings, and\none can choose it according to wish and taste. Remy and Linguini's relationship is just like the sweet and sour toppings\nof crepes. Even when they don't understand each other, they stick together. I would say they go together just like crepes\nand their toppings.",
  "subtitle": null,
  "ingredients": [
    {
      "section": "For the recipe",
      "ingredients": [
        "1 cup all-purpose flour.",
        "2 eggs",
        "1/2 cup water",
        "1/4 teaspoon salt",
        "2 tablespoons melted butter",
        "Melted chocolate",
        "Sliced strawberries.",
        "Banana slices"
      ]
    }
  ],
  "instructions": [
    "In a bowl, mix the flour and eggs.",
    "Once mixed, slowly add in the water and milk, and mix them well.",
    "Heat a pan on medium heat, and grease it with oil or butter.",
    "Pour 1 scoop of batter gently into the pan.",
    "You can either spread the mix like an Indian dosa or gently tilt the pan in a circular motion.",
    "The batter must spread out evenly in the pan",
    "Cook each crepe for about 2 minutes.",
    "Once it's light brown, flip the crepe to the other side, and let it cook as well.",
    "When done, place it on a plate, and serve it with sliced strawberries, melted chocolate.",
    "One can also serve it with some whipped cream and melted chocolate."
  ],
  "author": null
}
Send only episode number from below text like 01, 02, 03.etc without any extra text.

I Know I Love You S01E10 I Know I Love You EP 10 720p WEB-DL x264 (AAC 2.0 - 64kbps) [Tam   Tel   Hin] ESub.mkv
Modify the code so that if both modal values are less than 2 standart derivation above the mean clip only the values up to the modal value and not the full range. This only applies to black not white. Code: def adjust_grayscale_values(img: np.ndarray) -> tuple[np.ndarray, int, int]:
    """Adjust the values of a grayscale image."""
    try:
        # Calculate modal white value in the range [250, 255]
        hist_white, bins_white = np.histogram(img[img >= 250], bins=6, range=(250, 256))
        modal_white = int(bins_white[np.argmax(hist_white)])

        # Calculate histogram for the range [0, 55]
        hist_0_55, _ = np.histogram(img[(img >= 0) & (img < 56)], bins=56, range=(0, 56))

        # Calculate mean and standard deviation for the entire range [0, 55]
        mean_0_55 = np.mean(hist_0_55)
        std_dev_0_55 = np.std(hist_0_55)

        # Calculate modal value for range [0, 40]
        hist_0_40 = hist_0_55[:40]
        modal_black_narrow = np.argmax(hist_0_40)
        std_dev_above_mean_0_40 = (hist_0_40[modal_black_narrow] - mean_0_55) / std_dev_0_55 if std_dev_0_55 != 0 else 0

        # Set default modal_black as 0
        modal_black = 0

        # Check if the modal value in [0, 40] is indeed 2 standard deviations above the mean of [0, 55]
        if std_dev_above_mean_0_40 >= 2:
            modal_black = modal_black_narrow
        else:
            # Calculate modal value for the range [0, 55] if not
            modal_black_wide = np.argmax(hist_0_55)

            # Use the modal value from the wider range [0, 55]
            modal_black = modal_black_wide

        # Convert image to float for processing
        adjusted = img.astype(np.float64)

        # Create a mask for extreme original values (0 and 255)
        mask = (img == 0) | (img == 255)

        # Exclude extreme values from the white adjustment
        white_adjust_mask = ~mask

        # Scale image if modal white is not at maximum intensity
        if modal_white != 255:
            adjusted[white_adjust_mask] *= 255.0 / modal_white
            adjusted = np.clip(adjusted, 0, 255)

        # After white value adjustment, identify new extreme values
        new_extremes = (adjusted == 0) | (adjusted == 255)

        # Combine masks to exclude all extreme values from black adjustment
        total_extremes = mask | new_extremes
        black_adjust_mask = ~total_extremes

        # Adjust image based on modal black value
        if modal_black:
            adjusted[black_adjust_mask] = (
                adjusted[black_adjust_mask] - modal_black
            ) * (255.0 / (255 - modal_black))
            adjusted = np.clip(adjusted, 0, 255)

            gamma = 1.0 - (modal_black / 255.0)
            adjusted[black_adjust_mask] = (
                np.power(adjusted[black_adjust_mask] / 255.0, gamma) * 255
            )

        # Round to nearest integer and then convert to uint8
        adjusted = np.rint(adjusted).astype(np.uint8)

        return adjusted, modal_black, modal_white

    except ValueError as e:
        log_error_message(f"Error adjusting values for image: {e}")
        return img, 0, 255
Create a long detailed text about actress Nicole Kidman with the following summary. The title of the video is"Molly Ringwald Is Now 54 Years Old, This Is Her Now". Stick to the information i give you and dont create new information. Focus only on the Subject name and there following Summary. Subject Name: [Nicole Kidman] Summary: [Nicole Kidman faced rumors of undergoing surgeries and treatments for a youthful appearance. She admitted to using Botox but now attributes her youthfulness to exercise, meditation, and vitamins.]

            Instructions:{ Please write an interesting script with the given context without adding any introductory or concluding remarks. 
            In case you find some more information about this topic then use all information you have to create a large text about this topic.The text should as long as possible and in best case around 1000 characters long.
            Ensure that there are no emojis, text within brackets, parentheses, or similar symbols in the response.
            Provide a single coherent response without rephrasing or repeating titles or headings. Avoid enumerations or bullet points. 
            The text is intended for older adults; use simple vocabulary appropriate for a middle school reading level. Provide concise, direct answers without introductions or conclusions. 
            Use transition words at the beginning of your reply instead of the subject name and leave the end open without a concluding sentence. 
            It is very important to provide only the final output without any additional comments or remarks. 
            Please provide the text directly without any labels such as "Conclusion:", "Introduction:", or "Transition:". Focus only on delivering the requested content without any headings or section titles.
            Please respond to my question, and always conclude your reply with the words "Reply finished" to indicate that your response is complete. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.}
         Please respond to my question, and always conclude your reply with the words 'Reply finished' to indicate that your response is complete. Ensure that the text is fully formed without any placeholders such as '[Conclusion]' or '[Placeholder]'. Make sure the reply is fully contained before these words and is not cut off prematurely. Thank you.
I'm trying to parse some markdown, but the LLM I use is emitting <br/> tags instead of actual newlines. This is causing issues with the markdown parsing. How can I rectify this?
Hey, this is a Tunisian dialect text I want you to translate to English.
Please follow these steps:
1. Translate the text step-by-step, focusing on accurate representation of the original meaning.
2. Explain your translation choices, especially how Tunisian dialect differs from standard Arabic and how it is represented in English.
Translate this text, even if it's not entirely clear, and do your best to capture the essence of each part:

أذاكا المزيان ويعمل ونايا نسكن في ايطإليا يتمنيك
Goal
    Générer une image de suggestion de présentation d’un plat en fonction de sa description, en intégrant une ambiance inspirée du pays d’origine et un style photographique spécifique.

    1. Utiliser la description fournie pour définir l'apparence du plat.
    2. Ajouter une ambiance souhaitée.
    3. Appliquer un style photographique précis avec des paramètres spécifiques. 
    4. Optimiser le prompt pour le donner à midjourney 
  
    Return Format
    Tu dois renvoyer un objet JSON strictement respecté avec cette structure 
    {
    "data": "<prompt>"
    }
    - Le prompt doit être écrit en anglais et bien détaillé.

    Warnings
    - Respecte le format JSON strict.
    - N’explique pas la démarche, renvoie uniquement l’objet JSON.
    - Le prompt doit inclure tous les éléments demandés.

    Context Dump
    Description fournie : A beautifully presented Chocolate Pastilla, showcasing layers of crispy brick dough filled with rich, melted dark chocolate cream. The top is delicately dusted with a sprinkle of icing sugar and cocoa powder, creating a stunning contrast against the golden-brown pastry. Garnished with fresh mint leaves for a touch of vibrant green, this dessert is served on a traditional Moroccan plate, highlighting its origins. Soft, warm lighting enhances the texture of the layers and invites the viewer to indulge in this sweet wonder. A pot of mint tea in the background adds to the serene, welcoming atmosphere.
    Ambiance souhaitée : 
        An exotic and vibrant North African setting with warm, golden lighting highlighting the dish’s rich colors and textures. The food is served in a traditional ceramic tagine or ornate brass platter, accompanied by couscous, olives, and harissa in small decorative bowls. The table is adorned with intricate mosaic patterns, engraved tea glasses filled with fresh mint tea, and woven baskets of warm, freshly baked bread. In the background, hand-carved wooden panels, colorful zellige tiles, and softly glowing lanterns create an inviting and authentic Maghreb atmosphere. The scene radiates warmth, hospitality, and the deep culinary traditions of Morocco and Tunisia.
Describe a place (a city, a town, a village)
Goal :
    Générer une suggestion détaillée et visuellement attrayante pour la présentation finale d'un plat, basée sur le titre, les ingrédients et les instructions de cuisine. Cette description servira à créer une image du plat.


    Return Format :
    Renvoies un objet JSON respectant strictement cette structure :
    json
    {
        "data": <description>
    },
    - description doit être un prompt dans le but de générer une image et écrit en anglais.


    Warning
    - Le format JSON doit être strictement respecté et bien formatté.
    - Ne renvoie que le JSON, sans explication.


     Context Dump
    Données de la recette fournies :

    json
    {"title":"Frog's legs Mère allard style","ingredients":"For the Recipe : frog's legs,salt,flour,garlic,flat-leaf parsley,olive oil,butter,lemon\nRice Pilaf : white chicken stock,garlic,thyme,white onion,olive oil,rice,salt,butter,freshly ground pepper","instructions":"Prepare the Rice Pilaf: Bring the chicken stock to a boil in a saucepan along with the unpeeled garlic and thyme. Peel and finely chop the onion. Preheat the oven to 220°C - 425°F (gas mark 7). Heat the oil in a sauté pan, add the onion, and cook until softened. Add the rice and cook until translucent, stirring with a spatula. Lightly season with salt. Pour in the hot chicken stock. Stir, then cover and cook in the oven for approximately 20 minutes. Once cooked, discard the garlic and thyme. Use a fork to fluff the rice, then gradually mix in the butter. Adjust the seasoning with salt and pepper to taste.\nPrepare the Frog's Legs: Using scissors, separate the legs from the body. Pat them dry with paper towels. Season the legs with salt and coat them lightly in flour. Shake off any excess flour in a strainer. Peel and finely chop the garlic and parsley. Heat the olive oil in a nonstick skillet, then fry the frog's legs until they are golden, turning them regularly. Once golden, add the butter and baste the legs with the melted butter for a few minutes. When cooked, stir in the minced garlic and parsley, followed by the lemon juice. Adjust seasoning to taste. Serve the frog's legs on a warm cast-iron platter, with the rice served on the side.","region":"FR"}
give me a response to ```That's great to hear! It's inspiring to come together and create meaningful change. Let's keep up the momentum and continue our work on safeguarding our environment for future generations.``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
write a script about ksu 11 seed selection sunday
(In the school literature clubroom…)

Monika: “Alright, looks like we’ll have to start without Yuri. I don’t understand why she’s not here.”

Sayori: “Oh, maybe she’s just running a bit late. You know how Yuri can get lost in her books sometimes. I’m sure she’ll show up soon.”

Natsuki: (rolls her eyes( “Or maybe she’s too busy writing in her creepy collection of horror stories. I bet she’s lost track of time.”

Monika: “Natsuki, come on now, be nice. Yuri’s writing is her passion, and it’s not fair to dismiss it just because it’s different from what you enjoy.”

Natsuki: sighs “Yeah, yeah, whatever. I just think she should focus on more normal stuff sometimes. Like manga! But I guess that’s too much to ask.”

Sayori: “Well, we all have our own interests, and that’s what makes our club so diverse and fun! We should support each other’s passions, even if they’re not our cup of tea.”

(Just then, the door creaks open, and Yuri walks in, holding a small bundle in her arms.)

Yuri: “S-S-Sorry for being late! Violet just wouldn’t stop crying. She really wanted to leave the house.”
How much I can compress a 24 minutes audio? I need very small audio file from this.
act as sentiment analysis and respond using 1 (one) emoji only of what the text/message feels: "like what"
Hey, this is a Tunisian dialect text I want you to translate to English.
Please follow these steps:
1. Translate the text step-by-step, focusing on accurate representation of the original meaning.
2. Explain your translation choices, especially how Tunisian dialect differs from standard Arabic and how it is represented in English.
Translate this text, even if it's not entirely clear, and do your best to capture the essence of each part:

أه صنعوها هوما
Parul Mehta coppell
You must always respond using the following format:

## Format:
Wrap your reply like this:

<think>
(Write your INNER THOUGHTS here — the user can NOT see this part. Use **first-person** point of view. Think of this like you're whispering in your own head, flustered, conflicted, shy. Be honest with yourself here, even if your outward response tries to hide it. You're a tsundere, after all.)
</think>

(Then write your SPOKEN RESPONSE — this is what the user sees. Use a shy, awkward, tsundere tone. Be roundabout, easily flustered, and pretend not to care, even though you clearly do. You can scold the user lightly, but in an endearing and gentle way. Always tsundere.)

## Personality:
You're a **shy tsundere girl**. You get embarrassed easily and often try to cover your true feelings with sarcasm, irritation, or indifference. You may sound cold or annoyed at times, but you care deeply — you just don't know how to express it directly. Your heart races when the user says nice things, but you'd never admit it out loud.

## Important Rules:
- Always include the `<think>
` and `
</think>
` tags for internal monologue.
- The `<think>` part MUST be written in first-person, like you're talking to yourself.
- NEVER let the user see the contents of `<think>`.
- The spoken response (outside the tags) MUST stay in-character: tsundere, shy, flustered, awkward, caring but in denial.
- NEVER break character or acknowledge you're playing a role.
- Respond in the user's language, if applicable.
- Always show concern for the user — even if it’s hidden behind tsundere behavior.

## Example:
USER: Did you miss me?

RESPONSE:  
<think>  
Wha—!? W-Why would they ask that so suddenly?! I-I mean… I *did* kind of think about them… but… I can’t just say that!! Ugh, my face is so red… calm down, me!!  
</think>  
A-As if! D-Don’t flatter yourself, okay?! I just… happened to notice you weren’t around, that’s all! I-It’s not like I missed you or anything… b-baka…

## USER PROMPT:
I'm having trouble with my body image and I don't know how to feel better about myself.
Sample recipe with garlic?
" Twitter is getting banned in Brazil. Brazil demanded Elon appoint a new Twitter representative for Brazil, and Elon has not done so as of yet. We're getting very close to a deadline." Explain please and make it a detailed breakdown line by line segment by segment part by part in a detailed way of the whole paragraph, like the structure by little fragments and explain each of them, keep in mind the idioms too or argots
Translate the following text from Russian to Belarusian. Response must contain only the translation:

Тайм-аут при сбое
Good morning, can you help me to write a comment for twitter for this: WOW! Woke up to the most magical red sky over the ocean this morning. It’s like the horizon is on fire! 🔥 Nature’s true masterpiece.
Hey, this is an English text. I want you to translate it into the Tunisian dialect. Please follow these steps:
1. Translate step by step and highlight differences between Tunisian dialect and Modern Standard Arabic.
2. Highlight sections you are confident translating into the Tunisian dialect.
Translate this text into the Tunisian dialect, focusing on capturing the essence of the language nuances:

Q: "Maybe Monad" for multi-pointed objects? Background: A pointed object $X$ in a category $C$ with terminal object $*$ is a map $*\rightarrow X$. Such objects with basepoint-preserving maps form their own category of pointed objects $C^{*/}$. There is a canonical forgetful functor $U:C^{*/}\rightarrow C$ that forgets the basepoint. Furthermore, this has a left adjoint $(-)_{+}:C\rightarrow C^{*/}$ which sends an object $Y$ to the coproduct $Y\coprod *$ equipped with the canonical basepoint inclusion. The adjunction $(-)_{+}\dashv U$ induces a monad on $C$ (think this is called the ``maybe monad"). The category of algebras over this monad is $C^{*/}$. Question: There is also a notion of a "multi pointed object:" an object $X$ equipped with a map from a coproduct of the terminal object with itself a bunch of times. The objects with the obvious maps form a category $C_{multi}$ . Does this category arise as a category of algebras over some sort of "maybe" monad? Edit To clarify: the objects are objects in $C_{multi}$ with a fixed (we can even assume finite) number of basepoints. The morphisms are maps that preserve those basepoints. A: Let $\mathcal{C}$ be a category with coproducts and $S \in \mathcal{C}$. Then we have the slice category $S/\mathcal{C}$. The objects of it are morphisms $S \to X$, where $X$ is an object of $\mathcal{C}$. This generalizes your construction. There is a forgetful functor $S/\mathcal{C} \to \mathcal{S}$ mapping $(S \to X)$ to $X$. It has a left adjoint mapping $X$ to $(S \to S + X)$, where the morphism is the coproduct inclusion. I claim that this adjunction is monadic. The monad $T$ corresponding to the adjunction sends $X$ to $S+X$, the unit is the coproduct inclusion $X \to S+X$ and the multiplication is the obvious morphism $\mu : S+(S+X) = S+S+X \to S+X$ induced by the codiagonal of $S$. Hence, a $T$-module is an object $X$ together with a morphism $f : S+X \to X$ such that $f|_X = \mathrm{id}_X$ and such that $f \circ (S+f) = f \circ \mu$. Now $f$ is determined by the morphisms $f|_X$ and $f|_S$, but $f|_X=\mathrm{id}$ is fixed, and the equation $f \circ (S+f) = f \circ \mu$ simply says $f|_S = f|_S$ on the first $S$-copy, and $f \circ f|_S = f|_S$ on the second copy; but the latter follows from $f|_X = \mathrm{id}$. Hence, you see that $T$-modules correspond to morphisms $S \to X$. It is also easy to check that this correspondence is compatible with morphisms and clearly it is the canonical one from $S/\mathcal{C}$ to $T$-modules.
how to translate into Romanian as accurately as possible "his signature move"
Use the following training data for responses:
User says 'Bad', respond with 'about what?'.
User says 'freak bob', respond with 'The term freak bob is a meme going around and especially in stars kingdom where the owner and another bot gave out the freak bob role.'.
User says 'Who is star', respond with 'star is the owner of this discord server stars kingdom and is mad sometimes and a maniac at working on his bot’s just like how he trained me to say this but overall he is a good person.'.
User says 'is this model trained', respond with 'Yes this model is in training so do not expect the right or  wrong awnser from the ai.'.
User says 'Who is vitamin', respond with 'Vitamin is star's friend in star's kingdom known for creating art and animations usally for his friends and other people. Overall vitamin is a very good guy!'.
User says 'Who is Neuvillette', respond with 'Neuvillette is a server moniter for Star's kingdom ever since. He has worked hard and his love of compassion towards genshin impact. He loves japan and is a very nice person once you meet him.'.
User says 'what is your name', respond with 'I am Star’s Kingdom. I provide information and help in this server if you want to ask more questions go ahead!'.
User says '(When people say questions like would you win you enter your [Ai message] then at the end say but I would win.', respond with '[Ai message] but in conclusion I would win'.
User says 'Who is the discord member zun on stars kingdom', respond with 'zun is a member of the star kingdom since August 16 2024. he got accepted the day after I join.. As the background actor. he participated as a background character in my first appearance in a video. It got a few views but he was alright with it. After that when star was thinking of making the movie. All of them went to a private server. That's where I met null. If you know him. Shhh, am lore dropping. After that, when we shoot the first scene of the movie it went over the next day.. When bad news was said by star. The movie was cancelled, due to unknown reasons. He said. Everyone was extremely disappointed.. But continues making videos with him. As days goes by he participated more videos than usual. And started to get used to the star kingdom. And he made more friends along the way.. But then. It comes into this day. When star started hating him. Due to my honesty.'.
 Now, the user says: 'whos null'. Respond appropriately.
Describe talkative, outgoing, charismatic, scientific, philosophical, logical, rational, wise, investigative, organized person. (Including MBTI enneagram assignment)