from model.kronos import KronosTokenizer

tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
# Your tokenization code here

For Predictor

python

from model.kronos import Kronos, KronosTokenizer, KronosPredictor

tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
model = Kronos.from_pretrained("NeoQuasar/Kronos-Tokenizer-base")
predictor = KronosPredictor(model, tokenizer, device="cuda")

# Your prediction code here
predictions = predictor.predict(...)

With the Original Repository

python

# Clone the Kronos repository
git clone https://github.com/shiyu-coder/Kronos.git
cd Kronos

# Use the fine-tuned models
python examples/use_finetuned_model.py \
    --csv_data your_data.csv \
    --lookback 400 \
    --pred_len 120

Performance

This fine-tuned model shows improved performance on the target domain compared to the base model:

Domain Adaptation: Specialized for the training dataset characteristics
Numerical Stability: Improved convergence during fine-tuning
Inference Speed: Optimized for the target sequence lengths

Limitations

Optimized for 5-minute financial data intervals
May require re-tuning for different time frequencies
Performance may vary on datasets with different statistical properties

Citation

bibtex

@misc{shi2025kronos,
      title={Kronos: A Foundation Model for the Language of Financial Markets},
      author={Yu Shi and Zongliang Fu and Shuo Chen and Bohan Zhao and Wei Xu and Changshui Zhang and Jian Li},
      year={2025},
      eprint={2508.02739},
      archivePrefix={arXiv},
      primaryClass={q-fin.ST},
      url={https://arxiv.org/abs/2508.02739},
}

Contact

For questions or issues, please open an issue on the Kronos GitHub repository.

YuHaibo-HF/kronos-tokenizer-finetuned

Kronos Tokenizer - Fine-tuned on Custom Dataset

Model Details

Training Details

Usage

For Tokenizer

For Predictor

With the Original Repository

Performance

Limitations

Citation

Contact

YuHaibo-HF/
kronos-tokenizer-finetuned