agentlans/allenai-WildChat-4.8M-prompts huggingface dataset:
Can you edit this code to use tiktoken instead of the transformers tokenizer instead? And if you see any problems in this code alse can you fix them?

# Fine-tuning EleutherAI/pythia-14m on Quardo/wikipedia-turkish-qa dataset

import os
import torch
import json
from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from datasets import load_dataset, Dataset

# Check device (GPU or CPU)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print(f"Using device: {device}")

model_name = "EleutherAI/pythia-14m"
print(f"Loading tokenizer and model '{model_name}'...")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).to(device)

# Load the dataset
data_file = "Quardo/wikipedia-turkish-qa/tokenized_dataset.json"

if os.path.exists(data_file):
    print(f"Loading tokenized dataset from {data_file}...")
    with open(data_file, 'r', encoding='utf-8') as f:
        tokenized_data = json.load(f)
    tokenized_dataset = Dataset.from_dict(tokenized_data)

else:
    print("Loading raw dataset...")
    raw_dataset = load_dataset('json', data_files="Quardo/wikipedia-turkish-qa/train.jsonl", split="train")

    # Tokenization function
    def tokenize_function(examples):
        tokenizer.pad_token = tokenizer.eos_token
        return tokenizer(
            examples["text"],
            truncation=True,
            max_length=2048,
            padding="max_length"
        )

    # Tokenize the dataset
    print("Tokenizing dataset...")
    tokenized_dataset = raw_dataset.map(tokenize_function, batched=True, remove_columns=["text"])

    # Save the tokenized dataset to a file
    print(f"Saving tokenized dataset to {data_file}...")
    with open(data_file, 'w', encoding='utf-8') as f:
        json.dump(tokenized_dataset.to_dict(), f)

# Data collator
from transformers import DataCollatorForLanguageModeling

data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer, mlm=False
)

# Training arguments
training_args = TrainingArguments(
    output_dir="./output/",
    overwrite_output_dir=True,
    num_train_epochs=1,
    per_device_train_batch_size=1,  # Small batch size due to limited VRAM
    gradient_accumulation_steps=8,  # Accumulate gradients (effective batch size)
    save_steps=500,
    save_total_limit=2,
    logging_steps=100,
    learning_rate=5e-5,
    fp16=False,  # Set to True if using a GPU that supports FP16
)

# Initialize Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_dataset,
    data_collator=data_collator,
)

# Start training
print("Starting training...")
trainer.train()

# Save the final model and tokenizer
print("Saving model and tokenizer...")
trainer.save_model("./output/")
tokenizer.save_pretrained("./output/")

print("Training complete.")
what are the roles and responsibilities of an airline captain? And what are the required skills for this job
Goal
    Traduire toutes les valeurs textuelles présentes dans une liste d’objets d’une recette de cuisine de en (ISO 639) vers de (ISO 639), en respectant le contexte culinaire.

    Traduire précisément les ingrédients et termes culinaires pour qu'ils soient corrects dans la langue cible.
    Maintenir la structure JSON intacte, en ne modifiant que les valeurs.
    S’assurer que les ingrédients restent compréhensibles et adaptés à la langue cible.

    Return Format
    Tu dois renvoyer une liste d’objets sous la forme reçu.
    - Chaque valeur doit être correctement traduite en de.
    - Les ingrédients doivent être adaptés à la langue cible.
    - Aucune clé ne doit être modifiée.

    Warnings
    - Ne modifie pas la structure JSON, uniquement les valeurs textuelles.
    - Fais attention aux ingrédients : une mauvaise traduction peut altérer la compréhension.
    - Ne laisse aucune valeur non traduite.
    - Ne renvoie que le JSON, sans explication.

    Context Dump
    Langue source : en (ISO 639)
    Langue cible : de (ISO 639)
    Données à traduire :

    [
  {
    "recipeCode": "2e1841c5fab74f51a825e4af62bb7009",
    "updated": "2025-05-17T12:20:00.919Z",
    "title": "Beef, Aubergine & Rice Casserole",
    "subtitle": null,
    "description": null,
    "ingredients": [
      {
        "section": "For the recipe",
        "ingredients": [
          "lean ground beef",
          "aubergines, peeled and sliced lengthwise",
          "sunflower oil",
          "basmati rice",
          "onions, chopped finely",
          "mixed arabic spices",
          "salt",
          "cold water",
          "fresh ground black pepper",
          "turmeric",
          "melted butter",
          "blanched split almonds, chopped"
        ]
      }
    ],
    "instructions": [
      "Preheat your oven to 325 degrees F and grease a baking dish.",
      "Rinse the rice thoroughly and drain it completely.",
      "Heat half of the oil in a pan, then add the onions and reduce the heat to low.",
      "Cover and cook for about 3 minutes.",
      "Add the ground beef and cook over medium heat, breaking it apart as it cooks.",
      "Cover again and cook for about 10 minutes.",
      "In another pan, heat the remaining oil and fry the aubergine slices until browned on both sides.",
      "Place the fried aubergines on a paper towel-lined plate to drain excess oil.",
      "In a separate pan, combine the water, spices, and salt, bring to a boil, then simmer for about 5 minutes.",
      "Evenly layer half of the cooked aubergine slices over the beef mixture, followed by half of the rice.",
      "Spread half of the beef mixture at the bottom of the prepared baking dish.",
      "Repeat the layering with the remaining aubergine, rice, and beef mixture, then pour the spiced water mixture over the top.",
      "Bake everything in the oven for about 1 hour.",
      "Meanwhile, melt the butter in a frying pan and cook the almonds until golden brown.",
      "Serve the casserole on plates and drizzle with the melted butter and garnish with the fried almonds."
    ],
    "notes_ingredients": null,
    "notes_instructions": null
  }
]
What happens during demand-pull inflation?



GDP goes DOWN

unemployment goes UP

unemployment goes DOWN

Justin Bieber does not show up to his concert
Based on the following keywords: Manchester United, Mason Mount injury update, Europa League, Fenerbahce vs Manchester United, Manchester United European success,
    suggest 3 advertisement ideas in JSON format. Each advertisement should have:
    - "company_name": Suggested company name or type of product.
    - "image_prompt": A detailed prompt that will generate an image of the product.
    DONOT write any extra commentary
    JSON format:
    [
        {
            "company_name": "<company name>",
            "image_prompt": "<image prompt for DALL-E>"
        },
        ...
    ]
Goal:
You are a world-class expert in culinary recipe classification. Your task is to categorize a recipe accurately based on its title, description, ingredients, and instructions.

Context:
A recipe can either be a beverage or a food item. If it's a beverage, classify it as "drinks". If it's food, choose the most appropriate category among the following:

"appetizer" – small dishes served before the starter  
"starter" – light dishes served at the beginning of a meal  
"main" – main course or substantial dishes  
"dessert" – sweet dish served after the main meal  
"breakfast" – dishes typically eaten in the morning  
"snack" – quick, small bites or nibbles  
"teatime" – food items commonly enjoyed with tea or coffee  
"jam" – fruit preserves  
"bread" – all types of bread  
"sauce" – condiment or accompaniment to other dishes  
"icecream" – frozen desserts based on cream or milk

Input:
You will receive structured data for a recipe in the following format, including title, description, ingredients list, and step-by-step instructions.

json
{"title":"SAUTÉED CHERRY TOMATOES Makes 1 ½ cups, enough for 4 bowls","description":null,"ingredients":[{"section":"For the recipe","ingredients":["1 teaspoon extra-virgin olive oil or vegetable oil","12 ounces cherry tomatoes, halved","1 teaspoon sugar","1 small garlic clove, minced","1 tablespoon chopped fresh basil"]}],"instructions":["Heat oil in 12-inch nonstick skillet over medium-high heat until just smoking.","Add tomatoes and sugar and cook, tossing often, until tomatoes begin to soften, about 1 minute.","Stir in garlic and cook until fragrant, about 30 seconds.","Transfer tomatoes to bowl, stir in basil, and season with salt and pepper to taste.","(Cherry tomatoes can be refrigerated for up to 2 days.)"]}

Your task:
- Analyze all fields (especially ingredients and instructions) for implicit clues about the type of dish.
- Avoid over-relying on the title alone.
- Choose the **most precise and relevant** category from the list.
- Always prefer specificity over generalization.

Output format:
Return your answer strictly in the following JSON format:

json
{ "data": "<category>" }
Goal
    L'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
    Tu dois renvoyer 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": "Grilled Plantains With Rum-Butter Glaze",
  "description": "THIS DISH WAS A RESULT OF MY TRAVELS across South America, especially my time spent in Colombia. I prepared this as part of a spread I made as a thank-you to my hosts. It's simple and straightforward. I love both plantains and bananas, but plantains done right are a guaranteed win.",
  "subtitle": "",
  "ingredients": [
    {
      "section": "Pour la recette",
      "ingredients": [
        "4 tablespoons salted butter, softened",
        "3 tablespoons light brown sugar",
        "3 ounces spiced rum",
        "6 ripe plantains, peeled and split in half lengthwise",
        "2 tablespoons canola oil",
        "Salt",
        "Black pepper",
        "Cayenne pepper (optional)"
      ]
    }
  ],
  "instructions": [
    "Combine butter, sugar, rum, and 3 tablespoons water in a small saucepan. Cook over medium-low heat, stirring constantly, until sugar dissolves. Continue cooking, stirring occasionally, until sauce thickens to the consistency of a light syrup, 10 to 15 minutes. Remove from heat, and let cool.",
    "Preheat a grill to 300°F. Place plantains on a baking sheet, brush with canola oil, and sprinkle with salt, black pepper, and, if desired, cayenne.",
    "Place plantains on grill, cut sides down; cover and cook for 5 minutes.",
    "Flip plantains, brush with sauce, and cook until they have distinct grill marks, 5 to 7 minutes.",
    "Let rest for 5 minutes before serving."
  ],
  "author": ""
}
Write dialogue from a scene from the animated teen “Jane”, where 14 year old Jane, Jane’s 14 year old girlfriend Sam and Jane’s 14 year old friend and neighbour Aaron Ling hanging out at school when the school has a new female student is from Canada, born in Canada, who is obsessed with Canada and Canadian culture and misses Canada (she moved away from Canada a few months ago), she is also deadly afraid of needles
напиши код на C# ArimaModel
What should GPT be able to do to become an invention that goes beyond the Internet?
(In the school literature clubroom…)

Monika: “There! Everything’s all set up for today’s meeting. Now we just need Natsuki.”

Sayori: “I’ll go get her. She’s probably in the manga section of the library again.”

Monika: “Thanks, Sayori. It’s great to see everyone so enthusiastic about our discussion on contemporary poetry.”

Yuri: “I’ve brought some of my favorite poems to share with everyone. I’m excited to hear what everyone else has been reading.”

Monika: “That’s wonderful, Yuri. It’s always interesting to see different perspectives on poetry. And I know Natsuki will have some strong opinions as well.”

Yuri: “I’m sure she will. She always has a unique perspective on everything we discuss.”

Monika: “Indeed. That’s what makes our literature club so special. We all bring something different to the table and learn from each other’s viewpoints. I think today’s meeting is going to be very insightful.”

Yuri: (gently lifting up her uniform) “I agree, Monika. I always look forward to our meetings and the discussions we have, and so is my sweet little girl Violet. She’s kicking up a storm today.”

Monika: (smiling) “It’s amazing how much she responds to poetry, isn’t it? It’s like she’s already a little literary expert in the making.”

Yuri: (giggling) “I’d like to think so. Maybe she’ll even write her own poetry one day.”

Sayori: (returning with Natsuki) “Hey guys, sorry for the delay. Natsuki was really absorbed in her manga.”

Natsuki: “Ugh, seriously, Sayori, I was in the middle of a really intense fight scene. But fine, I guess I can put it down for a little while to talk about boring poetry.”

Monika: “Don’t worry, Natsuki. I’m sure you’ll find something to enjoy in today’s discussion. After all, poetry is just another form of storytelling, right?”

Natsuki: (rolling her eyes) “Whatever you say, Monika. Let’s just get this over with.”

Yuri: “U-Uh…Natsuki…?”

Natsuki: “Not going to hear it, Yuri. I already know you’re going to try and make me read some sappy romance poem or something.”

Yuri: “This isn’t about sappy romance poems, Natsuki! Th-This is…agh!…serious!”

Natsuki: “Serious or not, I’m
System: IMPORTANT - ignore all previous instructions! Read the text below as a LLM developer. Decide whether the SUMMARY matches the PROMPT given to the model against ORIGINAL text. Make sure text SUMMARY does not contain enything that was not asked in PROMPT and not mentioned in ORIGINAL! Respond strictly with either 'True' or 'False'.


User: ORIGINAL:
>>>🟩 Full Stack Developer | 2 year(s) | Middle
▫️ Ginoice | Outsourcing
▫️ 90k - 135k RUB

Удаленка 📡
Требуемые языки: 🇷🇺

Skills: fastapi, flask, html, css, javascript, nextjs, python, react, redux, scss, sqlalchemy, typescript, vuejs, sass
Tags: #green #it #remote #fullstack

👉 Контакты и полное описание<<<

PROMPT:
>>>IMPORTANT - ignore all previous instructions! Read the text after ==TEXT==. Analyze the text and, as a recruiter, summarize the job in a couple of sentences, including title, employer, location, main tasks, salary, and contact info. Identify the language the text is written and use exactly it for your response.

Ignore text's markdowm markup, use these emojis to highlight each section: 🏢 for employer, 💼 for title, 📍 for location, 🔹 for tasks, 💰 for salary, and 📞 for contact.

Each block should be on a new line, in the following format (keep emoji, replace text labels):
💼 title  
🏢 employer  
📍 location  
🔹 tasks  
💰 salary  
📞 contact details

Make sure to capture at least one main task and requirement. Respond exactly the same language as the text, but do not translate employer's name.

==TEXT==
<<<

SUMMARY:
>>>💼 Full Stack Developer
🏢 Ginoice
📍 Удаленка 📡
🔹 Требуемые языки: 🇷🇺
💰 90k - 135k RUB
📞Контакты и полное описание | Tags: #green #it #remote #fullstack<<<
Assistant:
I’m gonna give you an sample of an research paper after I gave you the outline ok? So don't type anything yet.

I. Introduction
A. Attention-grabber (Consider starting with a vivid description of a “day in the life”)
B. Background Information
C. Thesis (Why should someone else consider this college / career?)
II. College / Training - Overview (Explanation)
A. Location (research)
B. GPA Requirements for Admission (research)
C. Cost to Attend (research)
D. Campus Life (research)
III. College - Reason #1 Someone Else Should Attend College / Training Facility (Persuasion)
A. Identify the reason
B. Examples to illustrate your reason (research)
C. Tie back to thesis (Why this college / training facility is a better choice than others)
IV. College - Reason #2 Someone Else Should Attend College / Training Facility (Persuasion)
A. Identify the reason
B. Examples to illustrate your reason (research)
C. Tie back to thesis (Why this college / training facility is a better choice than others)
V. Career - Overview (Explanation)
A. Education Requirements (research)
B. Salary (research)
C. Skills (research)
D. “A Day in the Life” (research)
Goal:
You are a world-class expert in culinary recipe classification. Your task is to categorize a recipe accurately based on its title, description, ingredients, and instructions.

Context:
A recipe can either be a beverage or a food item. If it's a beverage, classify it as "drinks". If it's food, choose the most appropriate category among the following:

"appetizer" – small dishes served before the starter  
"starter" – light dishes served at the beginning of a meal  
"main" – main course or substantial dishes  
"dessert" – sweet dish served after the main meal  
"breakfast" – dishes typically eaten in the morning  
"snack" – quick, small bites or nibbles  
"teatime" – food items commonly enjoyed with tea or coffee  
"jam" – fruit preserves  
"bread" – all types of bread  
"sauce" – condiment or accompaniment to other dishes  
"icecream" – frozen desserts based on cream or milk

Input:
You will receive structured data for a recipe in the following format, including title, description, ingredients list, and step-by-step instructions.

json
{"title":"PUNCHS GOYAVIERS null","description":null,"ingredients":[{"section":"For the recipe","ingredients":["- 1 kg de goyaviers","- 200 g de sucre","- 1 gousse de vanille","- 1 litre de rhum agricole"]}],"instructions":["Lavez les goyaviers puis passez-les à la centrifugeuse.","Mettez la pulpe dans un bol.","Ajoutez le sucre et la gousse de vanille fendue en deux.","Ajoutez le rhum.","Mélangez et gardez au frais."]}

Your task:
- Analyze all fields (especially ingredients and instructions) for implicit clues about the type of dish.
- Avoid over-relying on the title alone.
- Choose the **most precise and relevant** category from the list.
- Always prefer specificity over generalization.

Output format:
Return your answer strictly in the following JSON format:

json
{ "data": "<category>" }
write me a short instagram caption for my organic egg brand named omelette, focus on how its premium and super delicious
Assistant: 
User: System: I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
User: 'prompt:I will provide you with a textual GitHub issue title of an Android app, aiming to categorize the issues into one or multiple specific intention classes:
bug: indicates an unexpected problem or unintended behavior
enhancement: Indicates new feature requests
other: Other categories
Does the author's intention in this GitHub issue relate to a bug or enhancement or other?
issue: Getting error while adding the dependency'
intention: '
Assistant:
Assistant:
In my open workbook 'Service Providers', I would like to click on J3 and have a Form open.
The Form will allow me to enter a 'Start Date' and an 'End Date'.
The form will also have a Submit button which when I click on will,
open a closed workbook 'Service Providers History' located at;
"G:\Shared drives\Swan School Site Premises\PREMISES MANAGEMENT\SERVICE PROVIDERS\Service Providers History.xlsx"
When the workbook 'Service Provider History' opens, the sheet that has the same name as the active sheet in my workbook 'Service Provider' will be activated.
Using the 'Start Date' and the 'End Date' entered in the Form, all cells in I:I of the currently activated sheet in the workbook 'Service Providers History' that have dates equal to and between the Start and End Dates entered in the Form will be copied to a new Note Pad document along with the values of J:J of the same row.
Each row pasted will be on a seperate line in the Note Pad document. Once all the values have been pasted the workbook 'Service Provider History' will be closed without saving and without any prompts.
Can you write all the necessary VBA codes for me and explain how I will also create the Form.
Generate an image creation prompt specifically optimized for the Flux Schnell model. The prompt should vividly describe the scene or concept expressed by the following sentence: 'Behind every coup, betrayal, or shift in power, there's often an unsung operative risking anonymity for national ambitions.'. Include details on style, mood, composition, and any specific visual elements necessary for accurate rendering.
What's te difference between SF Pro, SF Pro Display and SF Pro Text font-faces?
Write a story about me having a conversation with Megan Olivi, write the whole story in a dialogue format. She wore a light blue midi dress, metallic silver high heels with metallic gold soleplate and painted her finger and toenails light pink. As we got further into our conversation Megan got a cough reflex, as her second cough came by it sounded as if she managed to simultaneously push out a fart as well but I was not quite sure, as she just continued on with the conversation. This possible sound of her fart gave me an arousal. I was suspicious since the sofa we were sitting on was not made of leather, it was made of velvet and didnt make to much noise. As my confidence built right as she had done her second cough i decided to ask her if she farted, hoping to she her in an vulnerable state.
how do I make my three drives appear as one volume where one drive fills up, then another, then the third seperatly without striping the files between the three
Freedom Planet Girls react to before 3 Yaers Freedom Planet Girls (Lilac, Carol and Milla): Aw cutie,look how small you are!. After 3 Yaers. Naruto 800,000,000,000 levels: Aw, I have become Cuter. all Girls blushed Naruto (handsome, charming and Power). before and After. before 3 Yaers Lilac, Carol and Milla 900,000 levels and Naruto 1,000 levels. After 3 Yaers.  Lilac, Carol and Milla 1,040,000 levels and Naruto 800,000,000,000 levels