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README.md

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+---
+annotations_creators:
+- expert-generated
+language_creators:
+- expert-generated
+language:
+- en
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- n<1K
+source_datasets:
+- original
+task_categories:
+- text-generation
+- question-answering
+pretty_name: Nano-Start Learning Dataset
+tags:
+- educational
+- llm-training
+- chat
+- completions
+- oxidizr
+configs:
+- config_name: completions
+  data_files:
+  - split: train
+    path: completions.jsonl
+- config_name: qa
+  data_files:
+  - split: train
+    path: qa.jsonl
+- config_name: chat
+  data_files:
+  - split: train
+    path: chat.jsonl
+---
+
+# Nano-Start Learning Dataset
+
+A small educational dataset for learning how to train language models from scratch.
+
+## Dataset Description
+
+This dataset contains simple, factual examples designed to demonstrate LLM training concepts:
+
+- **Completions**: Factual statements the model learns to continue
+- **Q&A**: Question-answer pairs using chat special tokens
+- **Chat**: Multi-turn conversations with system prompts
+
+The dataset is intentionally small (~276 examples) so models can be trained quickly on CPU. The goal is education, not production-quality models.
+
+## Dataset Statistics
+
+| Split | Examples | Description |
+|-------|----------|-------------|
+| completions | 129 | Factual statements about geography, math, science, etc. |
+| qa | 96 | Q&A pairs with `<\|user\|>` and `<\|assistant\|>` tokens |
+| chat | 51 | Multi-turn conversations with `<\|system\|>` prompts |
+
+## Data Format
+
+All files are JSONL (JSON Lines) with a single `text` field:
+
+### Completions
+```json
+{"text": "The capital of France is Paris. Paris is known for the Eiffel Tower."}
+{"text": "1 + 1 = 2. This is the most basic addition problem in mathematics."}
+{"text": "Water boils at 100 degrees Celsius at sea level."}
+```
+
+### Q&A
+```json
+{"text": "<|user|>What is 1+1?<|assistant|>1+1 equals 2."}
+{"text": "<|user|>What is the capital of France?<|assistant|>The capital of France is Paris."}
+```
+
+### Chat
+```json
+{"text": "<|system|>You are a helpful assistant.<|user|>Hello!<|assistant|>Hello! How can I help you today?"}
+{"text": "<|system|>You are a math tutor.<|user|>What is 5x5?<|assistant|>5x5 equals 25."}
+```
+
+## Special Tokens
+
+The dataset uses OpenAI-compatible special tokens from the `cl100k_base` vocabulary:
+
+| Token | ID | Purpose |
+|-------|------|---------|
+| `<\|endoftext\|>` | 100257 | End of document (added during tokenization) |
+| `<\|system\|>` | 100277 | System instructions |
+| `<\|user\|>` | 100278 | User input |
+| `<\|assistant\|>` | 100279 | Model response |
+
+## Usage
+
+### Download
+
+**Option A: Using huggingface-cli**
+```bash
+pip install huggingface_hub
+huggingface-cli download fs90/nano-start-data --local-dir raw --repo-type dataset
+```
+
+**Option B: Direct download**
+
+Download files from the [Files tab](https://huggingface.co/datasets/fs90/nano-start-data/tree/main).
+
+### View with Python
+```python
+from datasets import load_dataset
+
+ds = load_dataset("fs90/nano-start-data", "completions")
+for example in ds["train"][:3]:
+    print(example["text"])
+```
+
+### For Training
+
+This raw data shows what the text looks like **before tokenization**. For training, use the pre-tokenized version: [fs90/nano-start-data-bin](https://huggingface.co/datasets/fs90/nano-start-data-bin)
+
+To learn how to tokenize your own data, see the [splintr](https://github.com/farhan-syah/splintr) project.
+
+## Related Resources
+
+- **Pre-tokenized data**: [fs90/nano-start-data-bin](https://huggingface.co/datasets/fs90/nano-start-data-bin)
+- **Training framework**: [oxidizr](https://github.com/farhan-syah/oxidizr)
+- **Tokenization**: [splintr](https://github.com/farhan-syah/splintr) - Learn how to tokenize your own data
+
+## License
+
+MIT License
+
+## Citation
+
+```bibtex
+@dataset{nano_start_2024,
+  title={Nano-Start: Educational Dataset for LLM Training},
+  author={fs90},
+  year={2024},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/fs90/nano-start-data}
+}
+```