Model Card for gpt2-stockfish-debug
See my blog post for additional details.
Training Details
The model was trained during 1 epoch on the yp-edu/stockfish-debug dataset (no hyperparameter tuning done). The samples are:
{"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):
{"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
.
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("yp-edu/gpt2-stockfish-debug")
tokenizer = AutoTokenizer.from_pretrained("yp-edu/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)
outcome = board.outcome()
if outcome is not None:
print(board)
print(outcome.result())
break
except chess.IllegalMoveError:
print(board)
print("Illegal move", i)
break
else:
print(board)
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