---
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 hf**
```bash
pip install huggingface_hub
hf 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}
}
```