agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
your role is tunisian arabic dailctic derja transltor Translate the following text into the Tunisian dialect. Ensure the output is structured as 'Question: ... Answer: ...'. Please capture the nuances of the Tunisian dialectic Arabic. make sure type and write in arabic tunisian lettre Here is an example to guide you:

Question: شنو اسمك؟
Answer: اسمي جون.

Now, translate the following:

Question: Generate a sentence that includes all the following words: dry, market, sale, seafood
Answer: Step 1: Identify the words that need to be included in the sentence - dry, market, sale, and seafood.

Step 2: Determine the logical connections among these words. In this case, the words "market" and "sale" can be connected, and "dry" and "seafood" can be connected as well.

Step 3: Think of a potential sentence including all these words. I'll start with "market" and "sale" as the main theme, as they have a stronger connection. This could generate a sentence such as, "The market was holding a sale."

Step 4: Now it's time to incorporate the other two words, "dry" and "seafood". I'll connect these words with the previous sentence and modify it accordingly. I can do this by adding "dry seafood" as the object of the sale. So the sentence would now be, "The market was holding a sale on dry seafood."

Final sentence: "The market was holding a sale on dry seafood." This sentence uses all the required words and has a logical connection among them.
Please polish and modify this paragraph: The HDAC inhibitory potential of fresh egg yolks and egg yolk fat is shown in figure 6. The fresh egg yolks and egg yolk fat showed HDAC inhibitory potential in a dose dependent manner at tested concentrations (1-4 mg/mL). Compared to fresh farm chicken egg yolk, country chicken egg yolk showed greater HDAC inhibitory potential (Figure 6). In addition, FCEF demonstrated higher HDAC inhibitory potential than CCEF (Figure 6).  Trichostatin A showed potent HDAC inhibitory effects (data not shown).
Let you be very good at math.

In math problems may occur misconceptions that lead to mistakes in solving one's.
You will be provided by a misconception statement after keyword 'Misconception'.
Your task is to rewrite this statement more detailed to get strong understanding of its idea and provide examples.
Misconception: When drawing a histogram, thinks you plot frequency on the horizontal axis
give me a response to ```Bargaining? Well, you know what they say – you never know until you try. Let's see what we can do – what do you have in mind?``` to send in a discussion, VERY SHORT, CONCISE & CLEAR. ONLY RETURN THE RAW MESSAGE, DO NOT SAY "Hey here is the message you asked"
Assistant: Hello! How are you today?
User: Helloصحح النص سواء كان كلمة، جملة، أو نص كامل، الذي سأقدمه فقط دون تزويدي بأي معلومات أو تفاصيل زائدة مع النص النهائي.
تصحيح الأخطاء باستخدام قواعد الإملاء القياسية للهجات المحلية.
Add expressive emojis in text between sentences and phrases to better express feelings and meanings.
Identify common English words of Latin origin and replace them with their English equivalents, such as:
واتساب becomes WhatsApp
فويس اوفر becomes Voiceover
اوبو becomes Oppo
فوكالايزر becomes Vocalizer
انفيجن becomes Envision
بي ماي ايس becomes Be My Eyes
ميت becomes Meet
ام اكس بلاير برو becomes MX Player Pro
سمارت اوديو بوك بلير becomes Smart AudioBook Player
تك فريدم becomes Tech-Freedom
فويسس becomes voices
فويس اللاود ريدر becomes Voice Allowed Reader
تكنو becomes tecno
اي اي becomes AI
جوجل becomes Google
and so on.
Please provide me with the processed text only, and do not add any additional text or any confirmation description of your own in any form.
This is the text:
احمد سوي لي ملف ارسل لي اياه منتظرك حبيبي الغالي
Assistant:
i have an model< and difference input shape, if it's univariate so the input_shape is (128,1)< for multivariate it could be (128,4)

code for model

# энкодер трансформера с регуляризацией и батч-нормализацией
def gru_branch(inputs, input_shape, units, dropout, l2_reg=0.01):
    x = layers.LayerNormalization(epsilon=1e-6)(inputs)
    x = layers.GRU(units, activation='relu', return_sequences=True, kernel_regularizer=regularizers.l2(l2_reg))(x)
    x = layers.Dropout(dropout)(x)
    x = layers.Dense(input_shape[0], activation='relu', kernel_regularizer=regularizers.l2(l2_reg))(x)
    return x + inputs

def transformer_encoder(inputs, head_size, num_heads, ff_dim, dropout=0):
    # Normalization and Attention
    x = layers.LayerNormalization(epsilon=1e-6)(inputs)
    x = layers.MultiHeadAttention(
        key_dim=head_size, num_heads=num_heads, dropout=dropout
    )(x, x)
    x = layers.Dropout(dropout)(x)
    res = x + inputs

    # Feed Forward Part
    x = layers.LayerNormalization(epsilon=1e-6)(res)
    x = layers.Conv1D(filters=ff_dim, kernel_size=1, activation="relu")(x)
    x = layers.Dropout(dropout)(x)
    x = layers.Conv1D(filters=inputs.shape[-1], kernel_size=1)(x)
    return x + res

def build_model(
    input_shape,
    head_size,
    num_heads,
    ff_dim,
    num_transformer_blocks,
    mlp_units,
    gru_units,
    l2_reg,
    dropout,
    mlp_dropout
):
    inputs = keras.Input(shape=input_shape)
    x = inputs
    y = inputs
    
    for _ in range(num_transformer_blocks):
        x = transformer_encoder(x, head_size, num_heads, ff_dim, dropout)

    y = gru_branch(y, input_shape, gru_units, dropout, l2_reg) #new
    
    x = layers.Concatenate()([x, y]) #new

    x = layers.GlobalAveragePooling1D(data_format="channels_first")(x)
    
    for dim in mlp_units:
        x = layers.Dense(dim, activation="relu")(x)
        x = layers.Dropout(mlp_dropout)(x)
    
    #univariate
    outputs = layers.Dense(64)(x)
    
    return keras.Model(inputs, outputs)

adapt this code for both multi and uni variate inputs
write 78 words about a kindergarten girl named Hannah who came happy after 3 days of holidays she cameback exscited to the pyjama day in kindergarten, she also enjoyed the short movie we watched and had fun and asking question about it.
System: You act as a paraphrase tool. Your role involves understanding the provided text and then providing a positive example that keeps the original meaning of the text unchanged while using synonyms, interchanging, adding or removing words or sentences, and then provide a negative example where the meaning of the original text is at its opposite.
Output your response with the following format : 
Positive : <the positive example>
Negative : <the negative example>

User: We are subject to a trade regulation rule on franchising, known as the FTC Franchise Rule, promulgated by the U.S. Federal Trade Commission (the “FTC”), that regulates the offer and sale of franchises in the United States and requires us to provide to all prospective franchisees certain mandatory disclosure in a Franchise Disclosure Document (“FDD”). In addition, we are subject to state franchise sales laws in approximately 20 U.S. states that regulate the offer and sale of franchises by requiring us to make a business opportunity exemption or franchise filing or obtain franchise registration prior to making any offer or sale of a franchise in those states and to provide a FDD to prospective franchisees. We are subject to franchise sales laws in six provinces in Canada that regulate the offer and sale of franchises by requiring us to provide a FDD in a prescribed format to prospective franchisees and that further regulate certain aspects of the franchise relationship. We are also subject to franchise relationship laws in at least 21 U.S. states and territories that regulate many aspects of the franchise relationship, including renewals and terminations of franchise agreements, franchise transfers, the applicable law and venue in which franchise disputes must be resolved, discrimination and franchisees’ right to associate, among others. In addition, we and franchisees may also be subject to laws in other foreign countries where we or
Assistant:
Hi, what is italy?
User: tuesday hari apa
User: tuesday hari apa
Assistant:
$ ~/work/work_1218/test$ onecc -C onnx.example.cfg
2023-12-20 19:47:54.154847: I tensorflow/core/util/port.cc:110] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2023-12-20 19:47:54.172693: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
/usr/share/one/bin/venv/lib/python3.8/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning:

TensorFlow Addons (TFA) has ended development and introduction of new features.
TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.
Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP).

For more information see: https://github.com/tensorflow/addons/issues/2807

  warnings.warn(
/usr/share/one/bin/venv/lib/python3.8/site-packages/tensorflow_addons/utils/tfa_eol_msg.py:23: UserWarning:

TensorFlow Addons (TFA) has ended development and introduction of new features.
TFA has entered a minimal maintenance and release mode until a planned end of life in May 2024.
Please modify downstream libraries to take dependencies from other repositories in our TensorFlow community (e.g. Keras, Keras-CV, and Keras-NLP).

For more information see: https://github.com/tensorflow/addons/issues/2807

  warnings.warn(
WARNING:absl:Found untraced functions such as gen_tensor_dict while saving (showing 1 of 1). These functions will not be directly callable after loading.
Recording 0'th data
Recording 1'th data
Recording 2'th data
Recording finished. Number of recorded data: 3
Cloudy with the Chance of Meatballs on Nickelodeon is Airing Date
Create a diary of a wimpy kid book where frank heffley gets promoted to customer, aka fired
Write a python script that produces a random 5 by 5 grid, where each place in the grid can be either empty or occupied by any number between 1 and 4 inclusive. Empty places must be represented as X. The only condition that must be satisfied is that there should be no row or column all the places of which are the same number. But there should be a possibility of swapping any two places after which the grid should contain exactly one row or column all the places of which have the same number whatever it may be.
explain Neural Basis Expansion Analysis with Exogenous Variables (NBEATSx) model.
Create a list/table of loot and equipment (including name, effects, and rarity) for a hypothetical expansion-centred MMORPG taking place in a world where the phrase "bigger is better" is taken literally and having oversized assets (sorted into three main categories: BOOB, BELLY and BUTT, or BULGE which replaces BOOB for male characters, known as the Four Bs) is ubiquitous; the larger one's assets are, the more powerful they're considered in-universe, and even the smallest, thinnest and most flat of characters can become enormous with enough time, loot and "experience gain". Unlike usual MMORPGs, there is so such thing as a limit in becoming *too* big or powerful; characters with the right amount of EXP, loot and knowledge with the game's mechanics can become larger than houses, towns, cities, countries, continents, planets... infinite EXP and loot leads to infinite levelling, leads to infinite size, and the effects of loot all stack with other loot. The only finite thing is how much room there is to fill in space-item, and players can choose to either carefully balance allocating points in two or all three size categories or dump everything into one asset to make it as massive as possible.
Example 1: **Band of Least Resistance** (*Rare* ring, wearable) "Wearer's body weight does not count against their carrying capacity"
Example 2: **Bare-Chested Leggings** (*Exotic* leggings, wearable) "+9 DEF, +10 BUST, +14 CHA, +60% carrying capacity, locks Chest slot"
Example 3: **Future Me's Problem** (*Highly Unusual* amulet, wearable) "Compresses 90% of Attribute size, wearer permanently gains 10% of compressed size per day this item is worn"
Example 4: **Amplifying Anklet** (*Very Rare* anklet, wearable) "+28 BUTT, -1 SPD per total BUTT, forces the wearer to walk with a swaying gait"
Example 5: **Heavy Bags of Hoarding** (*Elusive* stachel, wearable, requires tail slot) "50% of wearer's body weight is added to their maximum carrying capacity"
Example 6: **Primordial Piercings, Makers of the Milky Way** (*Eternal* piercings, wearable) "Binds on equip, Milk Capacity x110000, Milk Production x110000 (100% of true body weight), +800% milk sale value"
rewrite：

With a bachelor's degree in preschool education and a kindergarten teacher's qualification certificate, I have extensive teaching experience in kindergartens and early education. I have a deep understanding of the Montessori education system and its teaching methods.

I have worked in top-tier kindergartens at both the city and district levels in Guangzhou, as well as renowned international early education institutions and Montessori early childhood centers. I place great emphasis on children's early development and am familiar with and respectful of the developmental milestones of children of different ages and stages in various areas.

I excel at promoting children's development in sensory, motor, language, and other aspects in their daily lives. I am skilled at discovering and stimulating children's inner potential, providing them with continuous support and assistance.
Assistant: 
User: Ты — эксперт по играм. У тебя есть информация о игре с разных сайтов в формате JSON, id - уникальный номер, name - массив имен игры, description - массив описаний игры, genre - массив жанров. Твоя задача написать большое описание на основе данной тебе информации, так что бы оно содержала всю информацию без дублирования. ```{"_id":"1","name":["racing car rally"],"description":["choose your car, choose your race type and start racing! throw plate knives, load your car with nitro and hit the gas huggy wuggy","choose your car, choose your race type and start racing! throw plate knives, load your car with nitro and hit the gas"],"genre":[]}```
Assistant:
Defining and nondefining adjective clauses
I have a list of ingredients that I would like to format in a simple and consistent way, following this structure:

"Quantity Unit Description."

Formatting Rules:

Quantity must be a number (e.g., "200" instead of "two hundred").

Unit must use the metric system and the corresponding abbreviated form (e.g., grams → g, liters → l, milliliters → ml, etc.).

Description should be the ingredient, including any specific details if mentioned (e.g., "brown sugar", "melted unsalted butter").

If an ingredient is already formatted correctly, do not change it.

Do not invent a quantity or unit if none is provided — leave it as is and just include the description.

If a line includes alternatives or multiple options (e.g., a frozen or canned version), it should remain as a single line in the output and not be split into separate ingredients.

Return Format: The result should be returned as a properly formatted JSON object, like this:
json
{
  "data": <list ingredient>
}
Please reformat each ingredient in the list I will provide according to these guidelines.

Context Dump
Ingredients source provided:
800 g (1 lb 12 oz) well-ripened tomatoes,5 g (½ oz) fresh ginger, chopped,2 garlic cloves, chopped,2 tablespoons honey,80 g (2¾4 oz) light brown sugar,½ teaspoon ground cumin,1/2 teaspoon ground cinnamon,1 tablespoon white wine vinegar
User: difference between lungi, dhoti, mundu, etc and how to identify the difference between cloth and style of wearing??
Assistant: The terms lungi, dhoti, mundu, and others refer to traditional garments commonly worn in South Asian cultures, particularly in India and surrounding regions. While they may look similar and share cultural significance, there are subtle differences in their design, style, and usage. Here’s a rundown of each and how to identify the differences:  
  
### Lungi  
- **Description:** A lungi is a piece of cloth that is wrapped around the waist and worn primarily by men, though in some cultures, women may wear it too. It is usually informal and can be patterned or plain.  
- **Material:** Typically made from cotton or blends, suitable for the hot and humid climates.  
- **Style:** The lungi is generally wrapped around the waist and tucked in, without being pleated.  
- **Use:** Commonly worn in South India, Bangladesh, and other South Asian countries, often as casual, everyday wear.  
  
### Dhoti  
- **Description:** A dhoti is a traditional garment worn by men, consisting of a long piece of cloth wrapped around the waist and the legs, and then knotted, usually at the waist.  
- **Material:** Commonly made from cotton or silk.  
- **Style:** The dhoti is longer than a lungi and involves more elaborate draping. It often includes pleats at the front and can be wrapped over both legs, somewhat resembling pants.  
- **Use:** Worn across India, particularly in rural areas and at religious or ceremonial occasions.  
  
### Mundu  
- **Description:** Similar to a dhoti, the mundu is a garment worn primarily in Kerala, India. It is popular among men.  
- **Material:** Often made from cotton and can be plain or have a colored border known as a “kara.”  
- **Style:** The mundu is usually ankle-length and can be single or double draped. The double mundu is folded in half and then worn.  
- **Use:** Common in casual and formal settings in Kerala.  
  
### Identifying the Differences  
- **Length and Width:** Dhoti is generally longer than a lungi or mundu and is wrapped in a more elaborate style.  
- **Patterns and Borders:** Lungis often have colorful patterns, while mundus are typically white or off-white with a colorful border (kara).  
- **Purpose and Context:** Lungis are more informal, used for everyday activities, whereas dhotis and mundus are often worn during religious ceremonies, weddings, and formal occasions.  
- **Regional Usage:** Mundus are specific to Kerala, whereas lungis and dhotis have wider regional use across India and South Asia.  
  
Understanding these differences involves not just looking at the fabric and style but also the cultural context in which they are worn.
User: Are there other types in India? Can you give a rundown for all?
Assistant:
(In the clubroom…)

Natsuki: “GAH! It’s been 5 days since the festival and she’s STILL not showing up?!”

Monika: “I’m sure she just…overslept or something like that.”

Yuri: “I-I-I’m so worried about her.”

(Just then, Monika’s phone starts ringing.)

Monika: (answering the phone) “Hello?”

Sayori: (slightly in pain) “Hello, Monika…NNNNNNNNNNNNNNNGH!!”

Monika: “Sayori?! Where are you?!”

Sayori: “I’m at the…” (feeling more intense pain) “…hospital! AAAAAAAHHH!!!”

Natsuki: “Hospital?! What happened?!”

Monika: “Sayori, please calm down. We’re on our way. What happened? Are you hurt?”

Sayori: “I…this labor’s been going on for days! And the doctor STILL says that it’s not time for me to push yet!”

Yuri: “Oh my goodness, Sayori! That sounds so difficult. Hang in there, we’ll be there as soon as we can. Just try to relax and take deep breaths, okay?”

Natsuki: “Yeah, Sayori, stay strong! We’ll make sure everything’s okay. Just hold on a little longer.”

Monika: “We’re on our way to the hospital right now, Sayori. Just remember to breathe and stay calm. You’re in good hands, and we’ll be with you soon.”

Sayori: (struggling) “O-Okay… I’ll try my best… It just hurts so much!”

Monika: “I can only imagine, Sayori. But remember, the pain means you’re one step closer to meeting your baby. You’re doing an amazing job.”

Yuri: “The doctors and nurses are there to help you through this, Sayori. They’ll take good care of you and make sure everything goes smoothly.”

Natsuki: “Yeah, Sayori. Don’t worry, we’ll be there soon. Just take deep breaths and focus on the thought of finally getting to hold your baby.”

Sayori: “T-Thank you, guys… I don’t know what I would do without you…”

Monika: “You don’t have to worry about that, Sayori. We’ll always be here for you, no matter what. Just hang in there a little longer.”

(They quickly gather their things and rush out of the clubroom to head to the hospital, filled with a mix of worry and anticipation for what awaits them.)

(At the hospital…)
Please write an exciting sports story about a very muddy rugby match between two rival girls schools. Avoid flowery prose and poetic analogies. Give names. The girls love being at schools where they get to play rough and get muddy instead of playing boring traditional girls sports. Describe the state of the pitch - are some parts muddier than others? Include dramatic and important events from the realistic match. Show how the players get increasingly filthy as the game goes on and dont care at all, in fact they relish it. Describe the state of their bodies as well as their kit. Write in the style of classic school stories like Girls Own Paper or The Schoolgirl. The match is serious but the girls also enjoy playing and the tone should be lighthearted and fun with the emphasis on the sport and the mud, avoiding preaching or moralizing. include dialog which shows the girls relishing the rough and tumble and the muddy conditions. include some discussion in a team huddle where one team comes up with a strategy to take advantage of the muddy conditions. they had been losing but the strategy helps turn things around.
Send only season number from below text like 01, 02, 03.etc without any extra text.

Doraemon S08E32 [RareToonsIndia].mkv
How can i use my custom llm that i have as python code. i want to use in openwebui. I can change some code but then i need to know specific file of it. I would like easiest solution possible. The custom llm is running locally inside a python file, you can add code to it if u need.