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Datasets:
fs90
/
nano-start-data

Tasks:
Text Generation
Question Answering
Modalities:
Text
Formats:
json
Languages:
English
Size:
< 1K
Tags:
educational
llm-training
chat
completions
oxidizr
Libraries:
Datasets
pandas
License:
Dataset card Data Studio Files
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metadata
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 

{"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 

{"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 

{"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

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.

View with 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

To learn how to tokenize your own data, see the splintr project.

Related Resources

License

MIT License

Citation 

@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}
}