File size: 2,511 Bytes
b3f4ed7 27166aa 330f9e7 cd7ba99 f6abe6d cd7ba99 f6abe6d cd7ba99 82bd166 f6abe6d 82bd166 3907677 82bd166 315b9b8 82bd166 315b9b8 82bd166 315b9b8 82bd166 315b9b8 82bd166 315b9b8 82bd166 315b9b8 82bd166 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
---
license: mit
datasets:
- Xmaster6y/stockfish-debug
name: Xmaster6y/gpt2-stockfish-debug
results:
- task: train
metrics:
- name: train-loss
type: loss
value: 0.151
verified: false
- name: eval-loss
type: loss
value: 0.138
verified: false
widget:
- text: "FEN: rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1\nMOVE:"
example_title: "Init board"
- text: "FEN: r2qr1k1/1p3ppp/2n1bb2/p2p4/P2N4/1B1P2B1/1PP2PPP/R2QR1K1 b - - 0 16\nMOVE:"
example_title: "Board with legal completion"
- text: "FEN: 6k1/1p3ppp/2b2b2/p2p4/P2Q4/1B1P2B1/1PP2PPP/4q1K1 w - - 2 24\nMOVE:"
example_title: "Board with illegal completion"
---
# Model Card for gpt2-stockfish-debug
## Training Details
The model was trained during 1 epoch on the `Xmaster6y/stockfish-debug` dataset (no hyperparameter tuning done). The samples are:
```json
{"prompt":"FEN: {fen}\nMOVE:", "completion": " {move}"}
```
Two possible simple extensions:
- Expand the FEN string: `r2qk3/...` -> `r11qk111/...` or equivalent
- Condition with the result (ELO not available in the dataset):
```json
{"prompt":"RES: {res}\nFEN: {fen}\nMOVE:", "completion": " {move}"}
```
## Use the Model
The following code requires `python-chess` (in addition to `transformers`) which you can install using `pip install python-chess`.
```python
import chess
from transformers import AutoModelForCausalLM, AutoTokenizer
def next_move(model, tokenizer, fen):
input_ids = tokenizer(f"FEN: {fen}\nMOVE:", return_tensors="pt")
input_ids = {k: v.to(model.device) for k, v in input_ids.items()}
out = model.generate(
**input_ids,
max_new_tokens=10,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.1,
)
out_str = tokenizer.batch_decode(out)[0]
return out_str.split("MOVE:")[-1].replace("<|endoftext|>", "").strip()
board = chess.Board()
model = AutoModelForCausalLM.from_pretrained("Xmaster6y/gpt2-stockfish-debug")
tokenizer = AutoTokenizer.from_pretrained("Xmaster6y/gpt2-stockfish-debug") # or "gpt2"
tokenizer.pad_token = tokenizer.eos_token
for i in range(100):
fen = board.fen()
move_uci = next_move(model, tokenizer, fen)
try:
print(move_uci)
move = chess.Move.from_uci(move_uci)
if move not in board.legal_moves:
raise chess.IllegalMoveError
board.push(move)
except chess.IllegalMoveError:
print(board)
print("Illegal move", i)
break
```
|