================================ Training ================================ Once you have :doc:`preprocessed ` the dataset, you can train the model. Small Model ================================ Here is an example to train the model with the default parameters which results in the small Bloodhound model. .. code-block:: bash LAYERS=6 bloodhound-tools train \ --memmap preprocessed/esm${LAYERS}.npy \ --memmap-index preprocessed/esm${LAYERS}.txt \ --seqtree preprocessed/esm${LAYERS}.st \ --max-learning-rate 0.0002 \ --max-epochs 70 \ --train-all \ --embedding-model ESM${LAYERS} \ --run-name "Bloodhound-ESM${LAYERS}-small" This will create a model in the ``logs/Bloodhound-ESM6-small`` directory. The checkpoint with the weights will be saved in the directory called: ``logs/Bloodhound-ESM6-small/version_0/checkpoints/``. Use the smaller checkpoint with the ``weights`` prefix. The larger checkpoint with the ``checkpoint`` prefix includes optimizer state and you can delete this file once the training is finished. If you want to use Weights and Biases for logging, you can add the ``--wandb`` option to the command. Large Model ================================ If you want to train the large Bloodhound model, you can use the following command: .. code-block:: bash LAYERS=6 bloodhound-tools train \ --memmap preprocessed/esm${LAYERS}.npy \ --memmap-index preprocessed/esm${LAYERS}.txt \ --seqtree preprocessed/esm${LAYERS}.st \ --features 1536 \ --max-learning-rate 0.0002 \ --max-epochs 70 \ --train-all \ --embedding-model ESM${LAYERS} \ --run-name "Bloodhound-ESM${LAYERS}-large" Advanced Training ================================ See more options for training with the command: .. code-block:: bash bloodhound-tools train --help