| --- |
| license: other |
| task_categories: |
| - text-generation |
| language: |
| - ko |
| - en |
| tags: |
| - keural |
| - moe |
| - pretraining |
| - binary-dataset |
| - korean |
| - multilingual |
| - 69.5B-tokens |
| size_categories: |
| - 10B<n<100B |
| --- |
| |
| # Keural Stage 1 — 69.5 Billion Tokens Binary Dataset |
|
|
| Pre-tokenized binary dataset for the **Keural MoE Foundation Model**. |
| Ready to use directly for training — no tokenization step needed. |
|
|
| ## Dataset Overview |
|
|
| | Property | Value | |
| |---|---| |
| | **Total Tokens** | **69.5 billion** (69,496,921,399) | |
| | **Format** | Binary (.bin) + Index (.idx) + Metadata (.meta) | |
| | **Total Sequences** | 15,761,448 | |
| | **Sequence Length** | 4,096 tokens | |
| | **Shards** | 158 shards | |
| | **Archive Size** | ~242GB (binary_69B_tokens.tar) | |
| | **Tokenizer** | Keural SentencePiece Unigram, vocab=131,072 | |
| | **Last Updated** | 2026-04-03 | |
|
|
| ## Data Sources |
|
|
| | Source | Language | Tokens (approx) | |
| |---|---|---| |
| | FineWeb | English | ~20B | |
| | WanJuan Korean | Korean | ~5B | |
| | Korean WebText | Korean | ~4B | |
| | ArXiv | English Science | ~4B | |
| | CC100 Korean | Korean | ~3B | |
| | PubMed | English Medical | ~3B | |
| | The Stack v1 | Code | ~8B | |
| | Wikipedia Korean | Korean | ~1B | |
| | PG19 Literature | English | ~1B | |
| | Other sources | Mixed | ~20.5B | |
|
|
| ## Archive Contents |
|
|
| The tar file contains a `binary/` folder with: |
| - **158 .bin files**: Pre-tokenized binary data (keural_000.bin to keural_157.bin) |
| - **158 .idx files**: Index files for fast random access |
| - **158 .meta files**: Metadata JSON for each shard |
| - **build_stats.json**: Complete build statistics |
| |
| ## Binary Format Specification |
| |
| ``` |
| File: keural_NNN.bin |
| ───────────────────────────────────────── |
| HEADER (36 bytes): |
| [0:8] magic = b"KEURAL\x00\x00" (8 bytes) |
| [8:12] version = 1 (uint32 LE) |
| [12:20] num_seq (uint64 LE) |
| [20:28] seq_len = 4096 (uint64 LE) |
| [28:36] padding = 0 (uint64 LE) |
| |
| BODY: |
| num_seq × 4096 × 4 bytes (uint32 LE tokens) |
| |
| File: keural_NNN.idx |
| ───────────────────────────────────────── |
| [0:4] num_seq (uint32) |
| [4:8] seq_len (uint32) |
| per sequence: 8-byte offset + 4-byte length |
| |
| File: keural_NNN.meta (JSON) |
| ───────────────────────────────────────── |
| {"num_sequences": N, "seq_length": 4096, "source": "keural_NNN"} |
| ``` |
| |
| ## How to Extract |
| |
| ```bash |
| # Download the tar file, then extract: |
| tar -xf binary_69B_tokens.tar |
| |
| # This creates: binary/ directory with 158 shards |
| ``` |
| |
| ## How to Use |
| |
| ```python |
| import struct, mmap, torch |
| |
| HEADER_FMT = "<8sIQQQ" |
| HEADER_SIZE = struct.calcsize(HEADER_FMT) # 36 bytes |
| |
| with open("binary/keural_001.bin", "rb") as f: |
| raw = f.read(HEADER_SIZE) |
| magic, ver, num_seqs, seq_len, _ = struct.unpack(HEADER_FMT, raw) |
| print(f"Sequences: {num_seqs}, Length: {seq_len}") |
| |
| # Or use directly with training scripts: |
| # torchrun --nproc_per_node=2 train_keural_v2.py --data_dir ./binary |
| ``` |
| |
| ## Build Statistics |
| |
| ```json |
| { |
| "documents_processed": 553,711,744, |
| "tokens_processed": 69,496,921,399, |
| "sequences_written": 15,761,448, |
| "padding_added": 3,143,055,066, |
| "shards_created": 158, |
| "sequence_utilization": "95.13%" |
| } |
| ``` |
| |
| ## Related Resources |
| |
| - Model Training: [github.com/mkd-hossain/Keural-Model-Training](https://github.com/mkd-hossain/Keural-Model-Training) |
| - Tokenizer: [huggingface.co/mkd-ai/keural-tokenizer](https://huggingface.co/mkd-ai/keural-tokenizer) |
| - Organization: [huggingface.co/mkd-ai](https://huggingface.co/mkd-ai) |
| |
| ## Author |
| |
| **Md Najmul Hossain** / MKD CO., LTD. |
| Keural Foundation Model — Stage 1 pretraining dataset, 2026 |
|
|
