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Supports fine-tuning training with custom CSV data using configuration files Supports fine-tuning training with custom CSV data using configuration files
## Quick Start ## 1. Quick Start
### 1. Configuration Setup ### Configuration Setup
First edit the `config.yaml` file to set the correct paths and parameters: First edit the `config.yaml` file to set the correct paths and parameters:
@ -24,7 +24,7 @@ model_paths:
# ... other paths # ... other paths
``` ```
### 2. Run Training ### Run Training
Using train_sequential Using train_sequential
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DIST_BACKEND=nccl \ DIST_BACKEND=nccl \
torchrun --standalone --nproc_per_node=8 train_sequential.py --config configs/config_ali09988_candle-5min.yaml torchrun --standalone --nproc_per_node=8 train_sequential.py --config configs/config_ali09988_candle-5min.yaml
``` ```
## 2. Training Results
## Configuration Description ![HK_ali_09988_kline_5min_all_historical_20250919_073929](examples/HK_ali_09988_kline_5min_all_historical_20250919_073929.png)
### Main Configuration Items ![HK_ali_09988_kline_5min_all_historical_20250919_073944](examples/HK_ali_09988_kline_5min_all_historical_20250919_073944.png)
- **data**: Data-related configuration ![HK_ali_09988_kline_5min_all_historical_20250919_074012](examples/HK_ali_09988_kline_5min_all_historical_20250919_074012.png)
- `data_path`: CSV data file path
- `lookback_window`: Lookback window size
- `predict_window`: Prediction window size
- `train_ratio/val_ratio/test_ratio`: Dataset split ratios
- **training**: Training-related configuration ![HK_ali_09988_kline_5min_all_historical_20250919_074042](examples/HK_ali_09988_kline_5min_all_historical_20250919_074042.png)
- `epochs`: Number of training epochs
- `batch_size`: Batch size
- `tokenizer_learning_rate`: Tokenizer learning rate
- `predictor_learning_rate`: Predictor learning rate
- **model_paths**: Model path configuration ![HK_ali_09988_kline_5min_all_historical_20250919_074251](examples/HK_ali_09988_kline_5min_all_historical_20250919_074251.png)
- `pretrained_tokenizer`: Pre-trained tokenizer path
- `pretrained_predictor`: Pre-trained predictor path
- `base_save_path`: Model save root directory
- `finetuned_tokenizer`: Fine-tuned tokenizer path (for basemodel training)
- **experiment**: Experiment control
- `train_tokenizer`: Whether to train tokenizer
- `train_basemodel`: Whether to train basemodel
- `skip_existing`: Whether to skip existing models
## Training Process
1. **Tokenizer Fine-tuning Stage**
- Load pre-trained tokenizer
- Fine-tune on custom data
- Save fine-tuned tokenizer to `{base_save_path}/tokenizer/best_model/`
2. **Basemodel Fine-tuning Stage**
- Load fine-tuned tokenizer and pre-trained predictor
- Fine-tune on custom data
- Save fine-tuned basemodel to `{base_save_path}/basemodel/best_model/`
**Data Format**: Ensure CSV file contains the following columns: `timestamps`, `open`, `high`, `low`, `close`, `volume`, `amount` **Data Format**: Ensure CSV file contains the following columns: `timestamps`, `open`, `high`, `low`, `close`, `volume`, `amount`