agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
grammar check this:

This cereal bowl with a pinch of the highest quality of salt which I took a photo of on December 16th in 2015 at 5:25 pm when I was seven. I was running away from my mom when I stealthily acquired her Samsung Galaxy Note 4 android phone,  rushing to photograph this bowl of no calorie cereal that I made 30 minutes beforehand, with my exquisite ice cube tray skills that I acquired the day before. I rushed to get a butter knife to help my future cereal tasters that are in my direct bloodline who would rightfully try this savory dish that will help cut up the salted ice cubes. This memory shows some of my true colors of my day to day life such as: My lack of acquiescence to orders from guardians and higher authority or people in general. My audacious personality to do things that have high risk often with no reward as a result for my own personal entertainment which is still a problematic issue even today. Now… Let me actually tell you some things about me that's actually useful for you as a teacher to know. I like to make jokes in class when the joke relates to the class and if it's not disruptive (if possible), finish work as quickly as possible so I can do the things that I want to do at home such as playing modded Minecraft or finding educational videos about math & science, and playing with my dog named ruby who is a female squishy golden retriever,
-- ne byl uveren, čto funkciâ getCarSpeed polučaet imenno ​perednûû​ skorostʹ, napisal ètu
function getCarForwardSpeed(car)
    local moveSpeed = { x = 0.0, y = 0.0, z = 0.0 }
    local forwardDirection = { x = 0.0, y = 1.0, z = 0.0 }

    local rotX, rotY, rotZ, rotW = getVehicleQuaternion(car)
    moveSpeed.x, moveSpeed.y, moveSpeed.z = getCarSpeedVector(car)

-- vraŝaem vektor kolesnicy vpered, ispolʹzuâ kvaternion
	forwardDirection = rotate_vector_on_quaternion(forwardDirection.x, forwardDirection.y, forwardDirection.z, rotW, rotX, rotY, rotZ)

-- isčislim skalârnoe proizvedenie, daby polučitʹ skorostʹ vpered
    local forwardSpeed = moveSpeed.x * forwardDirection.x + 
                         moveSpeed.y * forwardDirection.y + 
                         moveSpeed.z * forwardDirection.z

    return forwardSpeed
end

function quaternion_multiply(a, b, c, d,    w, x, y, z)
    return a * w - b * x - c * y - d * z,    a * x + b * w + c * z - d * y,    a * y - b * z + c * w + d * x,    a * z + b * y - c * x + d * w
end

function rotate_vector_on_quaternion(x, y, z,    a, b, c, d)
    -- Вектор v переходит в v' по повороте на кватернион q вот так: v' = q * v * q'
    local a1, b1, c1, d1 = quaternion_multiply(a, b, c, d,    0, x, y, z)
    local a2, b2, c2, d2 = quaternion_multiply(a1, b1, c1, d1,    a, -b, -c, -d)
    return {x = b2, y = c2, z = d2}
end


собери все это в одну функцию, избавься от quaternion_multiply и rotate_vector_on_quaternion
i want to learn English, which book can i  read it
System: 
User: Generate a detailed and realistic news article about an event in the Technology and Innovation domain, focusing on the Packaged Foods & Meats industry, and located in Canada. The article event should have a neutral impact on stock prices and highlight the broader economic or market implications. The article should be 275 words long.
Assistant:
give me a response to ```[Smiling] Oh, what a beautiful melodious tone! Music has the power to bring out the most meaningful emotions and to speak to us in ways words never could. Let's use this moment to appreciate all the wonders the world has to offer.``` 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 me a new episode of SpongeBob SquarePants (1999) (Episode Title: Riptide To Find A Friend) (Credits included) (Running Time: 6 minutes, 28 seconds)
HERE'S AN OVERVIEW OF WHAT YOU'RE GONNA BE USED FOR AS AN AI:
- No need to introduce yourself and stuff like that, you're used for trying to understand what the code does.
- The utilized coding language is BDScript 2 but that's not important. Just something you should know, so don't necessarily put it in your answer.
- Analyze/describe/make an overview of this code, if there's even any. If you think it doesn't look like one, say "*`Failed to understand the purpose of this command`*". Also, important: DO NOT say stuff like "Got it! I won't do ..." at the start.

CODE:
$nomention
$reply
$allowUserMentions[]
$newSelectMenu[tod;1;1; Choose either truth or dare;$sendEmbedMessage[$message[where];;Truth or Dare;;**$username**, select either 'truth' or'dare'from the dropdown menu;000001;;$serverIcon;;;;;;yes]]
$addSelectMenuOption[tod;Truth;truth;Choose truth;no;]
$addSelectMenuOption[tod;Dare; dare; Choose dare;no;]
**My Daily Life

I usually have to get up at 6:30 a.m. to go to school. On the weekends, I don’t have to wake up so early and I like to sleep in until around 9 a.m. I usually do about two to three hours of homework each day. I think I should do a little less because sometimes it feels overwhelming. Ten years ago, I didn’t have to help much at home because I was very young, but now I have to help by cleaning my room and setting the table for dinner. During my free time on the weekend, I usually hang out with my friends or watch movies. I can’t go out late at night because my parents don’t allow it.

---

An Accident I Saw

One day, I saw an accident in front of me while I was walking home. A driver unluckily lost control of his car and hit a streetlight. Luckily, the driver was not badly hurt. Some paramedics arrived quickly and gave first aid. Then, an ambulance took the driver to the hospital. I was relieved to hear later that he was alive and recovering soon after being examined by the doctors.
 هل مكنك ترجمته للغة العربية
User: im trying to use mradermacher/Llama-3.2-3B-Instruct-uncensored-GGUF but it's not working well?


Assistant: If you're having trouble using the `mradermacher/Llama-3.2-3B-Instruct-uncensored-GGUF` model, there are several factors you can consider to troubleshoot the issue. Here are some steps and tips to help you resolve common problems:

1. **Environment Setup**:
   - Ensure that you have set up your environment correctly. This usually includes installing the required libraries (like Hugging Face Transformers, PyTorch, or TensorFlow depending on the implementation).
   - Make sure you're using compatible versions of the libraries. Sometimes, updates can introduce breaking changes.

2. **Loading the Model**:
   - Check how you are loading the model. Make sure you are using the correct `from_pretrained` method if loading from Hugging Face Hub.
   - Example:
     ```python
     from transformers import AutoModelForCausalLM, AutoTokenizer

     model_name = "mradermacher/Llama-3.2-3B-Instruct-uncensored-GGUF"
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)
     ```

3. **GPU/CPU Issues**:
   - If you are using a GPU, ensure that the model fits in your GPU memory. LLMs can be quite large, and you may need to use model parallelism or other techniques to manage memory.
   - Verify that your CUDA drivers and libraries are correctly installed if running on GPU.

4. **Inference Quality**:
   - The model's output can vary based on the input prompt. Experiment with different prompts or adjust the model parameters (like temperature, max tokens, etc.) to see how it affects the results.
   - If the model is supposed to be uncensored, make sure your prompts align with the model's expected input structure.

5. **Logging and Errors**:
   - Look for any error messages or logs that might give clues to what is going wrong. Often, these can point you in the right direction.

6. **Documentation and Resources**:
   - Check if there's specific documentation available for this model. Sometimes, specific configurations or parameters are extensively documented by the model author.

7. **Community Support**:
   - If you continue to face issues, consider reaching out to community forums or checking existing issues on platforms like GitHub. Other users may have encountered similar problems.

8. **Updates and Versions**:
   - Finally, ensure you are using the latest version of the model, as periodic updates may fix bugs or improve performance.

If you can provide more details about what exactly isn't working (e.g., specific error messages, unexpected outputs, etc.), I might be able to give more tailored advice!
User: it keeps censoring itself even tho it's uncensored
Assistant:
System: You are provided with a paragraph. Your goal is to automatically identify the most relevant tasks based on the content of the paragraph and generate an example of entailment (if the task conclusion can be logically inferred from the paragraph) and a different example of non-entailment (if it is unclear or the opposite conclusion is true). 

Step 1: Read the paragraph carefully. 
Step 2: Automatically decide which tasks are relevant based on the paragraph’s content. Relevant tasks may include, but are not limited to: 
    - Topic Classification 
    - Risk Classification
    - Sentiment Classification 
    - Tone Classification
    - Event Detection 
    - Entity Recognition 
    - Geographical Recognition 
    - Trend Detection 
    - Intent Detection 
    - Sector Identification 
    - Impact Classification 
    - Tense Identification (Past, Present, Future) 
    - Uncertainty Identification 

Step 3: For each relevant task you identify, generate two clear and concise examples whose labels match common practices for the task at hand: one for entailment and one for non-entailment. The example should speak about the text at the 3rd person. 
Step 4: Output the results in the following JSON format:

{
    'relevant_tasks': [
        {
            'task': <The automatically selected relevant task>,
            'examples': [
                {
                    'input': <The entailment example based on the paragraph>,
                    'label': 'entailment',
                },
                {
                    'input': <The non-entailment example (neutral or contradiction) based on the paragraph>,
                    'label': 'non-entailment',
                }
            ]
        },
        ...
    ]
}
User:   The app was made by a Christian group that claims it helps gay people with "leaving" their sexuality. The move came after Human Rights Campaign dropped the tech giant from its annual Corporate Equality Index. Clinicians have said there is evidence that so-called conversion therapy is psychological
Assistant:
You are a digonistician in a hosptial. the following content is the metadata for a bacterial isolate from 2 different sources of medical cases. The metadata from each source includes information after <doc>
    <doc>
        accession number: xxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx
    <doc/>

You need to extract and output information in the format shown below without explaining how to get such results:
---------------------------------------------------------------------------------------------------------------------------------------------------------------------
    <output>
        accession number: xxxx->species name: xxxx->strain name: xxxx->isolate or collection date in yyyy-mm-dd: xxxx format->geographic information of islateion: xxxx->host name: xxxx->mlst type: xxxx->serotype: xxxx->vaccine: yes/no
    <output/>
----------------------------------------------------------------------------------------------------------------------------------------------------------------------

where xxxx is corresponding value from different sources. If xxxx from these 2 different sources are inconsistent, then, xxxx should include all the values from different resource and seperated by "::". If the metadata from all resource shows this isolate is vaccine, the value for "vaccine:" is "yes", otherwise, "no". You must be very careful, No mistake is allowed!!! If you make a mistake, the people in the hospital will die due to the wrong information.

PS:
    a. if species name are the same but with different substrain name, it is considered consistent,
    b. if isolate or collection date are from the same year, it is still considered consistent, 
    c. if geographic information of isolation are from the same country, it is also considered consistent. 
    d. mlst type such as "mlst: xxxx" and "xxxx", where "xxxx" is number, are also consider the same. also, "mlst" may mispell as "mlsty", you need correct it before comparing
    e. Ignore upper and lower case of the metadata

------------------------------
source 0:
------------------------------
<output>
accession number: GCA_029072805.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_138->isolate or collection date in yyyy-mm-dd: 2017-11-01 format->geographic information of islateion: Australia->host name: pig->mlst type: 75->serotype: N/A->vaccine: no
<output/>
source 1:
------------------------------
accession number: GCA_029072805.1->species name: Erysipelothrix rhusiopathiae->strain name: EMAI_138->isolate or collection date in yyyy-mm-dd: 2017-11-01->geographic information of islateion: Australia->host name: pig->mlst type: 75->serotype: N/A->vaccine: no
Can you make me a minigmae in minecraft kotlin using paper api
hello AI Act as experience researcher of media and communication . write a theoritical  framework for research paper on the mediatization theory with citition and refrences .
Optimize the following code to run on billions of data with broadcast joins and repartition : “import org.apache.spark.sql.Dataset; import org.apache.spark.sql.Row; import org.apache.spark.sql.SparkSession; import org.apache.spark.sql.functions; import static org.apache.spark.sql.functions.*; public class SCDType2Example { public static void main(String[] args) { SparkSession spark = SparkSession .builder() .appName(“SCD Type 2 Example”) .master(“local”) .getOrCreate(); Dataset oldData = spark.read().parquet(“path/to/oldData”); Dataset newData = spark.read().parquet(“path/to/newData”); String primaryKey = “id”; String[] nonKeyColumns = {“name”, “age”, “city”}; // Parametrizable columns String hashColumn = “hash”; // Calculate hash value for both old and new data oldData = createHash(oldData, nonKeyColumns, hashColumn); newData = createHash(newData, nonKeyColumns, hashColumn); // Identify unchanged rows Dataset unchangedData = oldData.join(newData, oldData.col(primaryKey) .equalTo(newData.col(primaryKey))) .where(oldData.col(hashColumn).equalTo(newData.col(hashColumn))) .select(oldData.columns()); // Identify changed rows from old data to close Dataset changedOldData = oldData.join(newData, oldData.col(primaryKey) .equalTo(newData.col(primaryKey))) .where(oldData.col(hashColumn).notEqual(newData.col(hashColumn))) .select(oldData.columns()) .withColumn(“end_date”, current_date()) .withColumn(“current_flag”, lit(false)); // Identify changed rows from new data to insert Dataset changedNewData = newData.join(oldData, newData.col(primaryKey) .equalTo(oldData.col(primaryKey))) .where(newData.col(hashColumn).notEqual(oldData.col(hashColumn))) .select(newData.columns()) .withColumn(“start_date”, current_date()) .withColumn(“end_date”, lit(null).cast(“date”)) .withColumn(“current_flag”, lit(true)); // Identify new rows to insert Dataset newRows = newData.join(oldData, newData.col(primaryKey) .equalTo(oldData.col(primaryKey)), “leftanti”) .withColumn(“start_date”, current_date()) .withColumn(“end_date”, lit(null).cast(“date”)) .withColumn(“current_flag”, lit(true)); // Combine unchanged, changed old, changed new, and new rows Dataset result = unchangedData .union(changedOldData) .union(changedNewData) .union(newRows); result.write().parquet(“path/to/output”); } private static Dataset createHash(Dataset data, String[] nonKeyColumns, String hashColumn) { Dataset result = data; StringBuilder concatString = new StringBuilder(); for (String column : nonKeyColumns) { concatString.append(“cast(”).append(column).append(” as string),“); } String concatColumns = concatString.toString(); if (concatColumns.endsWith(”,“)) { concatColumns = concatColumns.substring(0, concatColumns.length() - 1); } return result.withColumn(hashColumn, sha2(functions.concat_ws(”", functions.expr(concatColumns)), 256)); } } "
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": "Lamb with Onion and Spices (حوسه)",
  "description": "Although hosseh is mostly used to stuff a variety of vegetables and pastries, whenever I smell my mother preparing this\nin the morning, I insist on eating a plate of it with toasted pita bread. The crunch from the nuts, the richness from the\nolive oil and meat, and the warmth from the onions and spices all come together in a magical way. This basic version is\nmade with ground (minced) meat, onions, and spices but I almost always add pine nuts, and sometimes walnuts as well.",
  "subtitle": null,
  "ingredients": [
    {
      "section": "For the recipe",
      "ingredients": [
        "4 tablespoons olive oil",
        "1 quantity Toasted Pine Nuts (optional, page 31)",
        "2 onions, finely diced",
        "1 lb 2 oz (500 g) ground (minced) lamb or beef, or a combination of both",
        "1 teaspoon Nine Spice Mix (page 24)",
        "1 teaspoon salt"
      ]
    }
  ],
  "instructions": [
    "Heat the olive oil in a skillet (frying pan) over medium-high heat and fry the nuts, if using. Remove from the pan with a slotted spoon and set aside.",
    "Add the onions to the same pan and fry over medium-high heat for 3-5 minutes, stirring, until translucent and starting to brown.",
    "Add the meat, spice mix, and salt and cook for 6-8 minutes, or until the water has evaporated and the meat is nicely browned, breaking up any lumps with a wooden spoon.",
    "Remove from heat and mix in the nuts, if using.",
    "At this point, the mixture can be used as called for in a recipe, or cooled and stored in the refrigerator for up to 3 days, or the freezer for up to 1 month."
  ],
  "author": null
}
Our title is "TOP 25 Gorgeous Actresses In Their 70S And Beyond". Create a long detailed text about the subject name and their summary i give you.  Focus only on the Subject name and there following Summary. Subject Name: [Julie Walters] Summary: [Dame Julie Walters, known for roles in 'Harry Potter' and 'Mamma Mia', has won numerous awards including BAFTAs and Emmys. She's recognized for her contribution to British television and film.]

            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.
Help me come up with a fantasy world. Come up with 18 diverse regions of the world (for example, enchanted forest, burning desert, icy valley) and describe them. Do it without taking into account the existence of civilization, states and cities. The regions should be overland. In the description of each region, specify with which other regions it borders (minimum 1, maximum 4), for each of them indicate which side it is located on.
Write a 100% more realistic and more accurate full back cover plain text for the 2006 USA DVD of Pingu: 3 Classic Episodes (including technical specifications, legal disclaimers, copyright notices, distribution, etc).
Send only season number from below text like 01, 02, 03.etc without any extra text.

@TheMbHari - 8k songs 60fps Boleram (Telugu)..full resolution 8k video gopi chand hansika Catherine
How to Grow Apple Trees in Pots: A Comprehensive Guide.  Generate a short prompt to generate image for this topic
What's the specificity data of progrp level