Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,80 @@
+---
+license: cc0-1.0
+task_categories:
+  - other
+tags:
+  - chess
+  - nnue
+  - self-play
+  - reinforcement-learning
+pretty_name: Zevra Self-Play Training Data
+size_categories:
+  - 1B<n<10B
+---
+
+# Zevra Self-Play Training Data
+
+Raw self-play positions generated by the [Zevra 2](https://github.com/sovaz1997/Zevra2)
+chess engine, used to train its NNUE evaluation network.
+
+**Provenance — trained from scratch on own play only.** Every position here was
+produced by Zevra playing itself. No hand-crafted-evaluation labels and no games
+from any other engine were used. The data is the product of a reinforcement loop
+seeded by the Zevra 2.6 network: each generation's champion generates self-play
+games, a fresh network is trained from scratch on them, and the stronger network
+then generates the next, better games (g0 → g1 → g2 → g3).
+
+## Contents
+
+- `zevra-selfplay-raw.tar.zst` — the full cumulative corpus, zstd-compressed
+  (~20 GB compressed, ~110 GB / ~1.8 billion positions uncompressed).
+- `SHA256SUMS.txt` — checksum for integrity.
+
+The public **Zevra 2.7** network was trained on the ~1.5B-position subset
+available at the g3 generation; this archive is the full cumulative set.
+
+## Format
+
+Plain text, one position per line:
+
+```
+<FEN> | <eval_cp> | <result>
+rn1qkbnr/pb1pp1pp/2p5/1p3p2/2P5/1Q5N/PP1PPPPP/RNB1KBR1 w KQkq - 6 7 | 123 | 0.0
+```
+
+- `FEN` — the position.
+- `eval_cp` — the engine's search evaluation in centipawns.
+- `result` — the game's final result (`1.0` win / `0.5` draw / `0.0` loss).
+
+Positions were generated by fixed-node self-play with light opening randomness.
+For the exact sign/perspective conventions see the datagen code in the engine
+repository.
+
+## Usage
+
+Decompress:
+
+```bash
+# modern GNU tar auto-detects zstd:
+tar xf zevra-selfplay-raw.tar.zst
+# or explicitly:
+zstd -d --long=27 zevra-selfplay-raw.tar.zst && tar xf zevra-selfplay-raw.tar
+```
+
+This unpacks a `collected/` tree of `.txt` shards in the format above. To train
+an NNUE the same way Zevra does, convert the text to the
+[bullet](https://github.com/jw1912/bullet) `bulletformat` and train with bullet:
+
+```bash
+# using bullet-utils (from the bullet repo)
+bullet-utils convert --from text --input <shards.txt> --output data.bin
+```
+
+## License
+
+Released under **CC0-1.0** (public domain dedication) — use it for anything.
+
+## Citation
+
+If you use this data, a link back to the engine is appreciated:
+<https://github.com/sovaz1997/Zevra2>