Datasets:

fs90
/

nano-start-data-bin

annotations_creators:
  - machine-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - mit
multilinguality:
  - monolingual
size_categories:
  - n<1K
source_datasets:
  - fs90/nano-start-data
task_categories:
  - text-generation
pretty_name: Nano-Start Tokenized Dataset
tags:
  - educational
  - llm-training
  - tokenized
  - binary
  - oxidizr

Nano-Start Tokenized Dataset

Pre-tokenized binary files ready for training with oxidizr. This is the tokenized version of fs90/nano-start-data.

What is Tokenization?

Language models don't process text directly - they work with numbers called tokens. Tokenization converts text into token IDs:

"Hello world" → [9906, 1917]

This dataset is pre-tokenized for simplicity - download and start training immediately. To learn how tokenization works and create your own datasets, see the splintr project.

Quick Start

Option A: Using hf

pip install huggingface_hub
hf download fs90/nano-start-data-bin --local-dir data/nano-start/tokenized --repo-type dataset

Option B: Direct download

Download combined.bin from the Files tab and place it in your project.

Train with oxidizr:

cargo run --release -- \
    --config models/nano-start.yaml \
    --data data/nano-start/tokenized/combined.bin

Files

Download combined.bin for training - it contains all data merged together:

File	Size	Tokens	Description
`combined.bin`	25,516 bytes	6,379	All data merged (recommended)

Individual Files (Optional)

You can also train on individual subsets. Training on different data produces different model behavior:

File	Size	Tokens	Description
`completions.bin`	8,788 bytes	2,197	Factual statements only
`qa.bin`	11,036 bytes	2,759	Q&A pairs only
`chat.bin`	5,692 bytes	1,423	Multi-turn conversations only

Experiment with different files to see how the training data affects model behavior!

Binary Format

Each .bin file contains raw token IDs:

Encoding: u32 (32-bit unsigned integer)
Byte order: Little-endian
Headers: None (raw token stream)
Tokenizer: cl100k_base (OpenAI, vocab size: 100,331)

Reading the Data

import struct

def read_tokens(path):
    with open(path, "rb") as f:
        data = f.read()
    return list(struct.unpack(f"<{len(data)//4}I", data))

tokens = read_tokens("combined.bin")
print(f"Total tokens: {len(tokens)}")

Tokenizer Details

Property	Value
Tokenizer	`cl100k_base` (OpenAI GPT-4/GPT-3.5)
Vocab size	100,331
EOS token	`<\|endoftext\|>` (ID: 100257)

Special Tokens

Token	ID	Purpose
`<\|endoftext\|>`	100257	Separates examples
`<\|system\|>`	100277	System instructions
`<\|user\|>`	100278	User input
`<\|assistant\|>`	100279	Model response

Source Data

To see the human-readable text before tokenization: fs90/nano-start-data

Related Resources

Raw data: fs90/nano-start-data
Training framework: oxidizr
Tokenization: splintr - Learn how to tokenize your own data

License

MIT License

Citation

@dataset{nano_start_bin_2024,
  title={Nano-Start Tokenized Dataset},
  author={fs90},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/fs90/nano-start-data-bin}
}