Instructions to use bytedance-research/ChatTS-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bytedance-research/ChatTS-14B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bytedance-research/ChatTS-14B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("bytedance-research/ChatTS-14B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bytedance-research/ChatTS-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bytedance-research/ChatTS-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bytedance-research/ChatTS-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bytedance-research/ChatTS-14B

SGLang

How to use bytedance-research/ChatTS-14B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bytedance-research/ChatTS-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bytedance-research/ChatTS-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bytedance-research/ChatTS-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bytedance-research/ChatTS-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bytedance-research/ChatTS-14B with Docker Model Runner:
```
docker model run hf.co/bytedance-research/ChatTS-14B
```

xiezhe22 commited on Aug 2, 2025

Commit

5c7016a

verified ·

1 Parent(s): 761befa

Update README.md

Browse files

Files changed (1) hide show

README.md +5 -14

README.md CHANGED Viewed

@@ -7,22 +7,10 @@ base_model:
 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>
@@ -35,7 +23,7 @@ language:
 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 is available at HuggingFace Spaces: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20ChatTS-Web%20Demo-blue)](https://huggingface.co/spaces/xiezhe22/ChatTS)
 ## Key Features
 ChatTS is a Multimodal LLM built natively for time series as a core modality：
@@ -85,6 +73,9 @@ 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)

 datasets:
 - ChatTSRepo/ChatTS-Training-Dataset
 language:
 - eng
 ---
+# [VLDB' 25] ChatTS-14B-0801 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>
 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: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20ChatTS-Web%20Demo-blue)](https://huggingface.co/spaces/xiezhe22/ChatTS)
 ## Key Features
 ChatTS is a Multimodal LLM built natively for time series as a core modality：
 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)