73 lines
2.2 KiB
YAML
73 lines
2.2 KiB
YAML
#This is a template config for custom finetuning kronos on csv data
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#这是一份模板config，用于kronos的csv自定义数据微调
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data:
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data_path: "/xxxx/Kronos/finetune_csv2/data/HK_ali_09988_kline_5min_all.csv"
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lookback_window: 512
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predict_window: 48
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max_context: 512
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clip: 5.0
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# dataset split ratio
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train_ratio: 0.9
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val_ratio: 0.1
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test_ratio: 0.0
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training:
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# control the training epochs of tokenizer and basemodel
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tokenizer_epochs: 30
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basemodel_epochs: 20
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batch_size: 32
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log_interval: 50
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num_workers: 6
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seed: 42
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tokenizer_learning_rate: 0.0002
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predictor_learning_rate: 0.000001
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adam_beta1: 0.9
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adam_beta2: 0.95
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adam_weight_decay: 0.1
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# gradient accumulation steps for tokenizer training
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accumulation_steps: 1
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# model path configuration
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model_paths:
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# pretrained model path
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pretrained_tokenizer: "/mnt/DigitalHuman2D/boyuzhang/quant/Kronos/pretrained/Kronos-Tokenizer-base"
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pretrained_predictor: "/mnt/DigitalHuman2D/boyuzhang/quant/Kronos/pretrained/Kronos-base"
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# experiment name - other paths will be generated based on this
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exp_name: "HK_ali_09988_kline_5min_all"
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base_path: "/mnt/DigitalHuman2D/boyuzhang/quant/Kronos/finetune_csv/finetuned/"
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# the following paths will be generated based on exp_name, no need to modify manually
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# way 1: leave empty string, the system will generate the full path
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base_save_path: "" # /xxxx/Kronos/finetune_csv/finetuned/{exp_name}
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finetuned_tokenizer: "" # /xxxx/quant/Kronos/finetune_csv/finetuned/{exp_name}/tokenizer/best_model
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# way 2: use template string, {exp_name} will be replaced with the actual experiment name
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# base_save_path: "/xxxx/Kronos/finetune_csv/finetuned/{exp_name}"
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# finetuned_tokenizer: "/xxxx/quant/Kronos/finetune_csv/finetuned/{exp_name}/tokenizer/best_model"
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tokenizer_save_name: "tokenizer"
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basemodel_save_name: "basemodel"
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experiment:
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name: "kronos_custom_finetune"
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description: "Custom finetune for HK stock data"
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use_comet: false
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# control the training phase
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train_tokenizer: true
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train_basemodel: true
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# if true, skip the existing model training
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skip_existing: false
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# device configuration
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device:
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use_cuda: true
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device_id: 0
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