training_data.py

data = [
    {"question": "What is a large language model (LLM)?", "answer": "An LLM is an artificial intelligence model trained on large volumes of text to understand and generate natural language."},
    {"question": "What are some examples of famous LLMs?", "answer": "Examples of famous LLMs include GPT, BERT, T5, and ChatGPT."},
    {"question": "What are LLMs used for?", "answer": "LLMs are used in tasks such as text generation, machine translation, text summarization, question answering, and more."},
    {"question": "What does the term 'fine-tuning' mean?", "answer": "Fine-tuning is the process of adjusting a pre-trained LLM on a specific dataset for a particular application or domain."},
    {"question": "What is 'tokenization'?", "answer": "Tokenization is the process of breaking text into smaller units, called tokens, which can be words, subwords, or characters."},
    {"question": "How do LLMs learn to generate text?", "answer": "LLMs learn to generate text by predicting the next word or token in a sequence based on patterns in the training text."},
    {"question": "What is the main challenge in training?", "answer": "The main challenges include the high computational cost, the need for large datasets, and the difficulty of avoiding biases in the models."},
    {"question": "What are parameters?", "answer": "Parameters are adjustable values in the model that determine how it processes and generates text based on input data."},
    {"question": "What is a transformer?", "answer": "Transformers are a neural network architecture that uses attention mechanisms to process data sequences, such as text."},
    {"question": "What is the role of 'pre-training'?", "answer": "'Pre-training' involves training the model on a large corpus of text to learn general language patterns before fine-tuning it for specific tasks."},
]