| # ChatTS-14B Model | |
| `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). | |
| Here is an example of a ChatTS application, which allows users to interact with a LLM to understand and reason about time series data: | |
|  | |
| ## 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=0, 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\nYou are a helpful assistant.<|im_end|><|im_start|>user\n{prompt}<|im_end|><|im_start|>assistant\n" | |
| # 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} | |
| } | |
| ``` | |