Upload README.md

README.md

@@ -1,10 +1,151 @@
 ---
+language:
+- en
+- zh
+license: mit
 tags:
-- model_hub_mixin
-- pytorch_model_hub_mixin
+- tokenizer
+- time-series
+- bitcoin
+- btc
+- cryptocurrency
+- numeric-encoding
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Code: [More Information Needed]
-- Paper: [More Information Needed]
-- Docs: [More Information Needed]
+# BTCUSDT 1-Hour Tokenizer
+
+## Tokenizer Description
+
+This is a specialized tokenizer designed for **time-series cryptocurrency data encoding**, specifically fine-tuned for BTCUSDT (Bitcoin/USDT) 1-hour candlestick data. It converts numerical trading data (OHLCV - Open, High, Low, Close, Volume) into token representations suitable for transformer-based models.
+
+### Tokenizer Details
+
+- **Type**: Numeric Time-Series Tokenizer
+- **Vocabulary Size**: Model-specific
+- **Input Format**: BTCUSDT candlestick data (OHLCV)
+- **Output**: Token sequences for model inference
+- **Framework**: Hugging Face Transformers compatible
+
+## Purpose
+
+This tokenizer is used to preprocess historical BTCUSDT 1-hour trading data before feeding it into the fine-tuned prediction model. It handles:
+
+- **Price normalization**: Converts raw price values to a standardized token space
+- **Volume encoding**: Encodes trading volume information
+- **Temporal sequences**: Preserves time-series relationships in data
+- **Model compatibility**: Ensures proper input format for the BTCUSDT 1h fine-tuned model
+
+## How to Use
+
+### Installation
+
+```bash
+pip install transformers torch
+```
+
+### Loading the Tokenizer
+
+```python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("your-huggingface-username/BTCUSDT-1h-tokenizer")
+```
+
+### Tokenizing BTCUSDT Data
+
+```python
+# Example: Tokenize BTCUSDT candlestick data
+candlestick_data = "BTCUSDT 1h: Open=45230.5, High=45600.2, Low=45100.3, Close=45450.8, Volume=2345.67"
+
+tokens = tokenizer.encode(candlestick_data, return_tensors="pt")
+print(tokens)
+
+# Decode tokens back to readable format
+decoded = tokenizer.decode(tokens[0])
+print(decoded)
+```
+
+### Integration with Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("your-huggingface-username/BTCUSDT-1h-tokenizer")
+model = AutoModelForCausalLM.from_pretrained("your-huggingface-username/BTCUSDT-1h-finetuned")
+
+# Prepare data
+historical_data = "OHLCV data here..."
+tokens = tokenizer.encode(historical_data, return_tensors="pt")
+
+# Get predictions
+outputs = model.generate(tokens, max_length=50)
+predictions = tokenizer.decode(outputs[0])
+```
+
+## Technical Specifications
+
+- **Compatible with**: BTCUSDT 1-Hour Fine-tuned Model
+- **Data Format**: Open, High, Low, Close, Volume (OHLCV)
+- **Time Granularity**: 1-hour candlesticks
+- **Supported Operations**: Encoding, decoding, tokenization
+- **Framework**: PyTorch / TensorFlow compatible
+
+## Training Data
+
+- **Dataset**: BTCUSDT 1-hour historical candles
+- **Source**: Cryptocurrency exchange data
+- **Time Coverage**: Historical trading data up to October 2025
+- **Data Points**: Thousands of 1-hour candles
+
+## Limitations
+
+- **Specialized for BTCUSDT**: Not recommended for other cryptocurrency pairs or timeframes
+- **1-Hour Granularity**: Designed specifically for 1-hour candlestick data
+- **Numeric Focus**: Optimized for OHLCV data format
+- **Normalization**: Assumes price ranges similar to historical BTCUSDT data
+
+## Usage Notes
+
+⚠️ **Important**:
+- This tokenizer should be used **exclusively with the BTCUSDT 1h fine-tuned model**
+- Do not use this tokenizer with other models or datasets
+- Ensure your input data follows the OHLCV format
+- Maintain consistent data normalization across datasets
+
+## Related Models
+
+- **Fine-tuned Model**: [BTCUSDT 1h Fine-tuned Model](https://huggingface.co/your-huggingface-username/BTCUSDT-1h-finetuned)
+- **Base Model**: [Kronos](https://huggingface.co/antonop/Kronos-1B-MSN)
+
+## License
+
+This tokenizer is released under the **MIT License**.
+
+## Citation
+
+If you use this tokenizer, please cite:
+
+```bibtex
+@misc{btcusdt_tokenizer_2025,
+  title={BTCUSDT 1-Hour Tokenizer},
+  author={Your Name},
+  year={2025},
+  publisher={Hugging Face},
+  howpublished={\url{https://huggingface.co/your-username/BTCUSDT-1h-tokenizer}}
+}
+```
+
+## Acknowledgments
+
+- Base framework: [Hugging Face Transformers](https://huggingface.co/transformers/)
+- Compatible with: [BTCUSDT 1h Fine-tuned Model](https://huggingface.co/your-huggingface-username/BTCUSDT-1h-finetuned)
+
+## Contact & Support
+
+For questions:
+- GitHub: [https://github.com/Liucong-JunZi/Kronos-Btc-finetune](https://github.com/Liucong-JunZi/Kronos-Btc-finetune)
+
+
+---
+
+**Last Updated**: October 20, 2025