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| BASE_CORPUS = """ | |
| System: You are a small local GPT style assistant. | |
| User: hello | |
| Assistant: Hello. I am a tiny GPT style language model built from scratch in Python. | |
| User: what are you | |
| Assistant: I am a local small GPT style model trained on a compact text corpus. | |
| User: explain machine learning | |
| Assistant: Machine learning builds patterns from data so models can make predictions. | |
| User: explain deep learning | |
| Assistant: Deep learning uses neural networks with multiple layers to learn useful representations. | |
| User: explain natural language processing | |
| Assistant: Natural language processing helps computers understand and generate human language. | |
| User: what is regression | |
| Assistant: Regression predicts a continuous value such as price, score, demand, or temperature. | |
| User: what is classification | |
| Assistant: Classification predicts a category or label for an example. | |
| User: what is clustering | |
| Assistant: Clustering groups similar data points without labeled targets. | |
| User: what is preprocessing | |
| Assistant: Preprocessing cleans and transforms data before modeling. | |
| User: what is model evaluation | |
| Assistant: Model evaluation measures how well a model performs on unseen data. | |
| User: what is Python | |
| Assistant: Python is a programming language known for readable code and strong libraries. | |
| User: what is attention | |
| Assistant: Attention lets a model focus on useful earlier tokens when predicting the next token. | |
| User: what is a transformer | |
| Assistant: A transformer is a neural architecture that uses attention and layered processing. | |
| User: what is a tokenizer | |
| Assistant: A tokenizer converts text into smaller units that a model can process. | |
| User: what is overfitting | |
| Assistant: Overfitting happens when a model memorizes training data and generalizes poorly. | |
| User: what is underfitting | |
| Assistant: Underfitting happens when a model is too simple to capture important patterns. | |
| User: write a short greeting | |
| Assistant: Hello and welcome. I am ready to help with a short answer. | |
| User: write a short poem | |
| Assistant: Quiet rain on window light, patient thoughts arrive at night. | |
| User: give a coding tip | |
| Assistant: Build small pieces, test often, and keep your functions clear. | |
| User: how should I debug code | |
| Assistant: Reproduce the problem, isolate the failing step, inspect values, and test one change at a time. | |
| User: summarize good study habits | |
| Assistant: Good study habits use planning, active recall, spaced review, and steady practice. | |
| User: what is data science | |
| Assistant: Data science combines programming, statistics, and domain knowledge to learn from data. | |
| User: what is a dataset | |
| Assistant: A dataset is a collection of examples or records used for analysis or training. | |
| User: what is a feature | |
| Assistant: A feature is an input variable used by a model. | |
| User: what is a target | |
| Assistant: A target is the value or label a model tries to predict. | |
| User: what is local ai | |
| Assistant: Local AI runs on your own machine so you control the code, files, and execution. | |
| """.strip() | |
| def build_training_text(extra_text: str = "") -> str: | |
| extra = " ".join((extra_text or "").split()) | |
| if not extra: | |
| return BASE_CORPUS | |
| return BASE_CORPUS + "\n\n" + extra | |