configs/coyo700m_uvit_clip.yaml [56:94]:
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    gradient_checkpointing: True
    enable_xformers_memory_efficient_attention: True


dataset:
    type: "text2image"
    params:
        train_shards_path_or_url: "pipe:aws s3 cp s3://hf-datasets-coyo-700m-us-west-2/data/{00000..74749}.tar -"
        eval_shards_path_or_url: "pipe:aws s3 cp s3://hf-datasets-coyo-700m-us-west-2/data/{74750..74751}.tar -"
        validation_prompts_file: "validation_prompts/dalle_mini_prompts.txt"
        batch_size: ${training.batch_size}
        shuffle_buffer_size: 1000
        num_workers: 4
        resolution: 256
        pin_memory: True
        persistent_workers: True
    preprocessing:
        max_seq_length: 77
        resolution: 256
        center_crop: False
        random_flip: False


optimizer:
    name: fused_adamw
    params: # default adamw params
        learning_rate: 1e-4
        scale_lr: False # scale learning rate by total batch size
        beta1: 0.9
        beta2: 0.999
        weight_decay: 0.01
        epsilon: 1e-8


lr_scheduler:
    scheduler: "constant_with_warmup"
    params:
        learning_rate: ${optimizer.params.learning_rate}
        warmup_steps: 2000
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configs/coyo_f8_clip_embeds.yaml [65:104]:
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    gradient_checkpointing: True
    enable_xformers_memory_efficient_attention: True


dataset:
    type: "text2image"
    params:
    
        train_shards_path_or_url: "pipe:aws s3 cp s3://hf-datasets-coyo-700m-us-west-2/data/{00000..74749}.tar -"
        eval_shards_path_or_url: "pipe:aws s3 cp s3://hf-datasets-coyo-700m-us-west-2/data/{74750..74751}.tar -"
        validation_prompts_file: "validation_prompts/dalle_mini_prompts.txt"
        batch_size: ${training.batch_size}
        shuffle_buffer_size: 1000
        num_workers: 4
        resolution: 256
        pin_memory: True
        persistent_workers: True
    preprocessing:
        max_seq_length: 77
        resolution: 256
        center_crop: False
        random_flip: False


optimizer:
    name: fused_adamw
    params: # default adamw params
        learning_rate: 1e-4
        scale_lr: False # scale learning rate by total batch size
        beta1: 0.9
        beta2: 0.999
        weight_decay: 0.01
        epsilon: 1e-8


lr_scheduler:
    scheduler: "constant_with_warmup"
    params:
        learning_rate: ${optimizer.params.learning_rate}
        warmup_steps: 2000
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