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, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Update README.md

#21

by csabakecskemeti - opened Jun 13, 2025

base: refs/heads/main

←

from: refs/pr/21

Discussion Files changed

-5

Files changed (1) hide show

README.md +7 -5

README.md CHANGED Viewed

@@ -60,19 +60,21 @@ 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

 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: <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