Upload fine-tuned Kronos predictor model

README.md

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+---
+language: zh
+license: mit
+tags:
+- kronos
+- financial-modeling
+- time-series
+- cryptocurrency
+- stock-prediction
+- pytorch
+- transformers
+datasets:
+- custom
+metrics:
+- mse
+- mae
+widget:
+- text: "Financial time series prediction"
+---
+
+# Kronos Predictor - Fine-tuned on Custom Dataset
+
+This is a fine-tuned version of [Kronos](https://huggingface.co/NeoQuasar/Kronos-small) predictor,
+adapted for better performance on custom financial datasets.
+
+## Model Details
+
+- **Model Type**: Predictor
+- **Base Model**: NeoQuasar/Kronos-small
+- **Fine-tuned For**: Financial Time Series Prediction
+- **Architecture**: Transformer-based with custom tokenization
+- **Input**: OHLCV (Open, High, Low, Close, Volume, Amount) data
+- **Output**: Multi-step time series predictions
+
+## Training Details
+
+- **Training Data**: Crypto Dataset (BTC, ETH, SOL, XAU)
+- **Time Range**: 2022-01-21 to 2025-09-16
+- **Frequency**: 5-minute intervals
+- **Sequence Length**: 90 historical points
+- **Prediction Horizon**: 10 future points
+
+## Usage
+
+### For Tokenizer
+```python
+from model.kronos import KronosTokenizer
+
+tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-small")
+# Your tokenization code here
+```
+
+### For Predictor
+```python
+from model.kronos import Kronos, KronosTokenizer, KronosPredictor
+
+tokenizer = KronosTokenizer.from_pretrained("NeoQuasar/Kronos-small")
+model = Kronos.from_pretrained("NeoQuasar/Kronos-small")
+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](https://github.com/shiyu-coder/Kronos).

config.json

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+{
+  "attn_dropout_p": 0.1,
+  "d_model": 512,
+  "ff_dim": 1024,
+  "ffn_dropout_p": 0.25,
+  "learn_te": true,
+  "n_heads": 8,
+  "n_layers": 8,
+  "resid_dropout_p": 0.25,
+  "s1_bits": 10,
+  "s2_bits": 10,
+  "token_dropout_p": 0.1
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:103c3a3b479f3bfe7d2254c0aa56f56920f4c786325f0a2203a9ee1c2275b5ec
+size 98980656