Improve language tag (#11)

- Improve language tag (a0b6e3219754a89bcbeff49effa5aece89e465dd)


Co-authored-by: Loïck BOURDOIS <[email protected]>

README.md

@@ -1,78 +1,92 @@
----
-license: apache-2.0
-library_name: transformers
-pipeline_tag: text-generation
-base_model:
-- Qwen/Qwen2.5-14B-Instruct
-datasets:
-- ChatTSRepo/ChatTS-Training-Dataset
----
-
-# [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}
-}
+---
+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}
+}
 ```