metadata
license: mit
datasets:
- nsarrazin/lichess-games-2023-01
pipeline_tag: text-generation
tags:
- chess
A 231M parameter base model trained on 4.4B tokens of lichess games from January 2023 that ended in checkmate (filtered out games that were won because of time).
Inference
from transformers import GPT2LMHeadModel, AutoTokenizer
model = GPT2LMHeadModel.from_pretrained("nsarrazin/chessformer").eval()
tokenizer = AutoTokenizer.from_pretrained("nsarrazin/chessformer")
moves = " ".join(["e2e4", "e7e5", "d2d4", "d7d5"])
model_inputs = tokenizer(moves, return_tensors="pt")
gen_tokens = model.generate(**model_inputs, max_new_tokens=1)[0]
next_move = tokenizer.decode(gen_tokens[-1])
print(next_move) #d4e5
End of game detection
The model also has three special tokens for end game detection <BLACK_WIN>, <WHITE_WIN> and <DRAW>. This can be useful for implementing beam search strategies.
moves = " ".join(["f2f3", "e7e5", "g2g4", "d8h4"])
model_inputs = tokenizer(moves, return_tensors="pt")
gen_tokens = model.generate(**model_inputs, max_new_tokens=1)[0]
next_move = tokenizer.decode(gen_tokens[-1])
print(next_move) # <BLACK_WIN>