---
license: cc0-1.0
task_categories:
  - other
tags:
  - chess
  - lichess
  - sequence-modeling
  - language-modeling
pretty_name: Lichess 2024–2025 Standard Rated (Binary Move Encoding)
size_categories:
  - 1B<n<10B
configs:
  - config_name: metadata
    data_files:
      - split: train
        path: "*-metadata.parquet"
---

# Lichess Standard Rated Games, 2024-01 → 2025-09 (binary move encoding)

A compact binary re-encoding of the [Lichess monthly database](https://database.lichess.org/) for standard rated games, covering 21 consecutive months (January 2024 through September 2025). Designed for training sequence models on chess games — every move is one 16-bit token, every game is a variable-length sequence of tokens, and per-game metadata lives in Parquet alongside.

Source PGNs were processed with the encoder at https://github.com/Alfredvc/chess-autocomplete (see `rust/src/move_codec.rs` for the authoritative codec implementation).

## File layout

Per month, three files share the prefix `lichess_db_standard_rated_YYYY-MM`:

| File | Format | Purpose |
| --- | --- | --- |
| `*.bin` | Packed little-endian `u16` tokens | Concatenated games. One game = one variable-length run of `u16` move tokens. No in-stream delimiters — use the map file to find game boundaries. |
| `*-map.bin` | Packed little-endian `u64` | Cumulative byte offsets, one per game. Game `i` occupies bytes `[offsets[i-1], offsets[i])` in `*.bin` (with `offsets[-1] = 0`). Number of games = `len(map) / 8`. |
| `*-metadata.parquet` | Parquet | One row per game, in the same order as games appear in `*.bin`. |

All months together: 63 files, ~272 GB. The 21 Parquet files alone are ~13 GB and can be browsed directly on the Hub's Datasets viewer via the `metadata` config.

## Metadata schema

| Column | Type | Notes |
| --- | --- | --- |
| `GameIndex` | `uint64` | 1-based index within the source PGN file. Skipped games (non-standard start positions, etc.) leave gaps. |
| `WhiteRating/16` | `uint8` | Rating quantized to 16-pt buckets — multiply by 16 to recover. `0` = unknown. Saturates at 4080+ (255 × 16). |
| `BlackRating/16` | `uint8` | Same encoding as WhiteRating. |
| `InitialTime` | `uint16` | Initial clock in seconds. `0` = unknown. |
| `Increment` | `uint8` | Increment in seconds. |

## Move token encoding (16 bits per move)

Each `u16`, parsed in little-endian byte order, packs five fields:

```
bits 15-12 : op       (4 bits — piece, castle, promotion, or game-end marker)
bits 11-9  : from_file (3 bits, 0=a … 7=h)
bits  8-6  : from_rank (3 bits, 0=rank 1 … 7=rank 8)
bits  5-3  : to_file   (3 bits)
bits  2-0  : to_rank   (3 bits)
```

`op` codes:

| Code | Meaning |
| --- | --- |
| `0b0000` | Pawn move |
| `0b0001` | Knight move |
| `0b0010` | Bishop move |
| `0b0011` | Rook move |
| `0b0100` | Queen move |
| `0b0101` | King move (non-castle) |
| `0b0110` | White king-side castle |
| `0b0111` | White queen-side castle |
| `0b1000` | Game-end marker (see special tokens below) |
| `0b1001` | Promotion to knight |
| `0b1010` | Promotion to bishop |
| `0b1011` | Promotion to rook |
| `0b1100` | Promotion to queen |
| `0b1101` | Black king-side castle |
| `0b1110` | Black queen-side castle |
| `0b1111` | Reserved / unused |

When `op == 0b1000`, the lower 12 bits encode the termination reason rather than squares:

| Token | Reason |
| --- | --- |
| `0x8000` | Unknown |
| `0x8001` | Checkmate |
| `0x8002` | Stalemate |
| `0x8003` | Insufficient material |
| `0x8004` | Fifty-move rule |
| `0x8005` | Threefold repetition |

Each game's token stream terminates with one of these markers. This representation is **board-free**: a parser does not need to maintain a chess position to decode any single move (unlike SAN or UCI), since the moving piece type, origin square, destination square, and promotion piece are all explicit.

## Decoding example (Python)

Reading game `i` from a single month:

```python
import numpy as np
import pyarrow.parquet as pq

month = "2024-01"
prefix = f"lichess_db_standard_rated_{month}"

# Per-game byte offsets into *.bin (cumulative end positions)
offsets = np.fromfile(f"{prefix}-map.bin", dtype=np.uint64)
num_games = len(offsets)

# Memory-map the moves
moves = np.memmap(f"{prefix}.bin", dtype=np.uint16, mode="r")

def get_game_tokens(i: int) -> np.ndarray:
    start_byte = 0 if i == 0 else offsets[i - 1]
    end_byte = offsets[i]
    # offsets are byte positions; tokens are 2 bytes each
    return moves[start_byte // 2 : end_byte // 2]

# Per-game metadata aligns row-for-row with the games in *.bin
meta = pq.read_table(f"{prefix}-metadata.parquet").to_pandas()
print(meta.iloc[0])
print(get_game_tokens(0))
```

To decode an individual move token:

```python
def decode(token: int):
    op = (token >> 12) & 0xF
    if op == 0b1000:
        return ("game_end", token & 0xFFF)
    from_file = (token >> 9) & 0x7
    from_rank = (token >> 6) & 0x7
    to_file   = (token >> 3) & 0x7
    to_rank   =  token       & 0x7
    return (op, from_file, from_rank, to_file, to_rank)
```

## Coverage

21 months: `2024-01`, `2024-02`, …, `2024-12`, `2025-01`, …, `2025-09`.

Games per month are on the order of 10⁸ (the source Lichess dumps contain ~90–100M standard rated games per month). Games with non-standard starting positions (e.g., Chess960 mixed into the standard archive, or unusual `FEN` tags) are skipped during encoding, so `GameIndex` is not contiguous.

## Filtering / partial downloads

Use `huggingface_hub`'s filtered download to pull only the months or file types you need:

```python
from huggingface_hub import snapshot_download

# Just the metadata Parquet files (~13 GB total)
snapshot_download(
    repo_id="Alfredvc/chess-autocomplete-lichess",
    repo_type="dataset",
    allow_patterns="*-metadata.parquet",
    local_dir="./lichess",
)

# A single month, all three files
snapshot_download(
    repo_id="Alfredvc/chess-autocomplete-lichess",
    repo_type="dataset",
    allow_patterns="lichess_db_standard_rated_2024-01*",
    local_dir="./lichess",
)
```

## License

The underlying Lichess game data is published by Lichess under **CC0 1.0** (public domain dedication). This re-encoded copy is released under the same terms. Please cite the [Lichess database](https://database.lichess.org/) when using this dataset.