---
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
base_model:
- Qwen/Qwen2.5-14B-Instruct
datasets:
- ChatTSRepo/ChatTS-Training-Dataset
language:
- eng
---
# [VLDB' 25] ChatTS-14B-0801 Model
**[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).
## Web Demo
The Web Demo of ChatTS-14B-0801 is available at HuggingFace Spaces: [](https://huggingface.co/spaces/xiezhe22/ChatTS)
## Key Features
ChatTS is a Multimodal LLM built natively for time series as a core modality：
- ✅ **Native support for multivariate time series**
- ✅ **Flexible input**: Supports multivariate time series with **different lengths** and **flexible dimensionality**
- ✅ **Conversational understanding + reasoning**:
Enables interactive dialogue over time series to explore insights about time series
- ✅ **Preserves raw numerical values**:
Can answer **statistical questions**, such as _"How large is the spike at timestamp t?"_
- ✅ **Easy integration with existing LLM pipelines**, including support for **vLLM**.
### Example Application
Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data:
[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
hf_model = "bytedance-research/ChatTS-14B"
# Load the model, tokenizer and processor
# For pre-Ampere GPUs (like V100) use `_attn_implementation='eager'`
model = AutoModelForCausalLM.from_pretrained(hf_model, trust_remote_code=True, device_map="auto", torch_dtype='float16')
tokenizer = AutoTokenizer.from_pretrained(hf_model, trust_remote_code=True)
processor = AutoProcessor.from_pretrained(hf_model, 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: . 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))
```
## Reproduction of Paper Results
Please download the [legacy ChatTS-14B model](https://huggingface.co/bytedance-research/ChatTS-14B/tree/fea24f221dd13ad310b68cc5470f575647b838c6) to reproduce the results in the paper.
## 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}
}
```