README.md

---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-14B-Instruct
datasets:
- ChatTSRepo/ChatTS-Training-Dataset
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
---

# [VLDB' 25] ChatTS-14B Model

<div style="display:flex;justify-content: center">
<a href="https://github.com/NetmanAIOps/ChatTS"><img alt="github" src="https://img.shields.io/badge/Code-GitHub-blue"></a>
<a href="https://arxiv.org/abs/2412.03104"><img alt="preprint" src="https://img.shields.io/static/v1?label=arXiv&amp;message=2412.03104&amp;color=B31B1B&amp;logo=arXiv"></a>
</div>

**[VLDB' 25] ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning**

`ChatTS` focuses on **Understanding and Reasoning** about time series, much like what vision/video/audio-MLLMs do.
This repo provides code, datasets and model for `ChatTS`: [ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning](https://arxiv.org/pdf/2412.03104).

`ChatTS` features native support for multi-variate time series data with any length and range of values. With `ChatTS`, you can easily understand and reason about both the **shape** features and **value** features in the time series.
`ChatTS` can also be integrated into existing LLM pipelines for more time series-related applications, leveraging existing inference frameworks such as `vLLMs`.

Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data:
![Chat](figures/chat_example.png)

[Link to the paper](https://arxiv.org/pdf/2412.03104)

[Link to the Github repository](https://github.com/NetManAIOps/ChatTS)

## Usage
- This model is fine-tuned on the QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) model. For more usage details, please refer to the `README.md` in the ChatTS repository.
- An example usage of ChatTS (with `HuggingFace`):
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, AutoProcessor
import torch
import numpy as np

# Load the model, tokenizer and processor
model = AutoModelForCausalLM.from_pretrained("./ckpt", trust_remote_code=True, device_map="auto", torch_dtype='float16')
tokenizer = AutoTokenizer.from_pretrained("./ckpt", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("./ckpt", trust_remote_code=True, tokenizer=tokenizer)
# Create time series and prompts
timeseries = np.sin(np.arange(256) / 10) * 5.0
timeseries[100:] -= 10.0
prompt = f"I have a time series length of 256: <ts><ts/>. Please analyze the local changes in this time series."
# Apply Chat Template
prompt = f"<|im_start|>system
You are a helpful assistant.<|im_end|><|im_start|>user
{prompt}<|im_end|><|im_start|>assistant
"
# Convert to tensor
inputs = processor(text=[prompt], timeseries=[timeseries], padding=True, return_tensors="pt")
# Model Generate
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0][len(inputs['input_ids'][0]):], skip_special_tokens=True))
```

## Reference
- QWen2.5-14B-Instruct (https://huggingface.co/Qwen/Qwen2.5-14B-Instruct)
- transformers (https://github.com/huggingface/transformers.git)
- [ChatTS Paper](https://arxiv.org/pdf/2412.03104)


## License
This model is licensed under the [Apache License 2.0](LICENSE).

## Cite
```
@article{xie2024chatts,
  title={ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning},
  author={Xie, Zhe and Li, Zeyan and He, Xiao and Xu, Longlong and Wen, Xidao and Zhang, Tieying and Chen, Jianjun and Shi, Rui and Pei, Dan},
  journal={arXiv preprint arXiv:2412.03104},
  year={2024}
}
```