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Download the games from Lichess between January 2017 and November 2019 to data/lichess_raw.And that move_prediction/replication-move_training_set.py is where the main shuffling and games selection logic is. This is a similar format to the general training instructions just with our specified data, so you will need to have trainingdata-tool and pgn-extract on your PATH.ДЄlso note that running the scripts manually line by line might be necessary as they do not have any flow control logic. To train the models we present in the paper you need to download the raw files from Lichess then cut them into the training sets and process them into the training data format. It will be the one with the largest number Once complete the final model will be in models/CONFIG_BASENAME/ directory.(optional) You can use tensorboard to watch the training progress, the logs are in runs/CONFIG_BASENAME/.Run the training script move_prediction/train_maia.py PATH_TO_CONFIG.(optional) You can also change all the other training parameters here, like the number of layers.(optional) If you have multiple GPUS change the gpu filed to the one you are using.Add OUTPUT_PATH/validation/*/* to input_test.Add OUTPUT_PATH/training/*/* to input_train.The script will create a training and validation set, if you wish to train on the whole set copy the files from OUTPUT_PATH/validation to OUTPUT_PATH/training.Wait a bit as the processing is both IO and CPU intense.Run move_prediction/pgn_to_trainingdata.sh PGN_FILE_PATH OUTPUT_PATH.Convert the PGN into the training format.Make sure all the required packages are installed from requirements.txt.

#Anilam series 1100 single bo install

(optional) Install the conda environment, maia_env.yml.

To create your own maia from a set of chess games in the PGN format:

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These can be found here Code Move Prediction DatasetsДЄs part of our analysis all the game on Lichess with stockfish analysis were processed into csv files. Move_prediction/maia_chess_backend also has the LeelaEngine class that uses the config files move_prediction/model_files/*/config.yaml to wrap python-chess and allow the models to be used in Python. Info depth 1 seldepth 1 time 831 nodes 1 score cp 6 tbhits 0 pv e2e4

Loading weights file from: model_files/maia-1100.pb.gz :~/maia-chess$ lc0 -weights=model_files/maia-1100.pb.gz When running the models on the command line it should look like this:

We also have all the models in the maia_weights folder of the repo. The bots on Lichess use opening books that are still in development, since the models play the same move every time. The links to download the models directly are: Models with Lichess Bots Targeted Rating Note also, the models are also stronger than the rating they are trained on since they make the average move of a player at that rating. Then unlike most other engines you want to disable searching, a nodes limit of 1 is what we use. So you need to load them with lc0 and follow the instructions here.

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The Maias are not a full chess framework chess engines, they are just brains (weights) and require a body to work. We also have a Lichess team, maia-bots, that we will add more bots to.

MaiaMystery is for testing new versions of Maia.

You can also play against three of of our models on Lichess: Our website has information about the project and team. In this repo is our 9 final maia models saved as Leela Chess neural networks, and the code to create more and reproduce our results. Aligning Superhuman AI with Human Behavior: Chess as a Model System website/ paper/ code/ lichessДЄ collection of chess engines that play like humans, from ELO 1100 to 1900.