Update README.md

README.md

@@ -1,3 +1,84 @@
----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+library_name: YingLong
+tags:
+- time-series
+- forecasting
+- foundation-models
+- pretrained-models
+- time-series-foundation-models
+- large-time-series-models
+---
+
+
+# YingLong
+
+YingLong model is introduced in this [paper](xxxxxxxx) (coming soon). This version is pre-trained on **78B** time points. More details can be found at our [github](https://github.com/wxie9/YingLong/).
+
+
+ 
+## Quickstart
+
+```bash
+pip install xformers transformers
+pip install flash-attn --no-build-isolation
+git clone https://github.com/Dao-AILab/flash-attention && cd flash-attention
+cd csrc/rotary && pip install .
+cd ../layer_norm && pip install .
+```
+The flash attention is not required. If you use V100 or other GPU doesn't support flash attention, just change the FlashAttention2Available = RequirementCache("flash-attn>=2.0.0.post1") to
+FlashAttention2Available = False in the model.py file. It should be able to run. 
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+
+# load pretrain model
+model = AutoModelForCausalLM.from_pretrained('qcw2333/YingLong_110m', trust_remote_code=True,torch_dtype=torch.bfloat16).cuda()
+
+# prepare input
+batch_size, lookback_length = 1, 2880
+seqs = torch.randn(batch_size, lookback_length).bfloat16().cuda()
+
+# generate forecast
+prediction_length = 96
+output = model.generate(seqs, future_token=prediction_length)
+
+print(output.shape)
+```
+
+A notebook example is also provided [here](https://github.com/wxie9/YingLong/blob/main/quickstart_zero_shot.ipynb). The sample codes for long-term forecasting tasks and gift-eval tasks are provided at [link](https://github.com/wxie9/YingLong/tree/main).
+
+
+<!-- ## Specification -->
+
+
+## Citation
+Coming soon...
+<!-- ```
+@inproceedings{liutimer,
+  title={Timer: Generative Pre-trained Transformers Are Large Time Series Models},
+  author={Liu, Yong and Zhang, Haoran and Li, Chenyu and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng},
+  booktitle={Forty-first International Conference on Machine Learning}
+}
+
+@article{liu2024timer,
+  title={Timer-XL: Long-Context Transformers for Unified Time Series Forecasting},
+  author={Liu, Yong and Qin, Guo and Huang, Xiangdong and Wang, Jianmin and Long, Mingsheng},
+  journal={arXiv preprint arXiv:2410.04803},
+  year={2024}
+}
+``` -->
+
+## Contact
+
+If you have any questions or want to use the code, feel free to contact:
+
+Xue Wang (xue.w@alibaba-inc.com)
+
+Tian Zhou (an.zt@alibaba-inc.com)
+
+ 
+## License
+
+This model is licensed under the cc-by-4.0 License.