Instructions to use TimeOmni-1/TimeOmni-1-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TimeOmni-1/TimeOmni-1-9B with Transformers:

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

pipe = pipeline("text-generation", model="TimeOmni-1/TimeOmni-1-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("TimeOmni-1/TimeOmni-1-9B")
model = AutoModelForImageTextToText.from_pretrained("TimeOmni-1/TimeOmni-1-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TimeOmni-1/TimeOmni-1-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TimeOmni-1/TimeOmni-1-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TimeOmni-1/TimeOmni-1-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TimeOmni-1/TimeOmni-1-9B

SGLang

How to use TimeOmni-1/TimeOmni-1-9B 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 "TimeOmni-1/TimeOmni-1-9B" \
    --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": "TimeOmni-1/TimeOmni-1-9B",
		"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 "TimeOmni-1/TimeOmni-1-9B" \
        --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": "TimeOmni-1/TimeOmni-1-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TimeOmni-1/TimeOmni-1-9B with Docker Model Runner:
```
docker model run hf.co/TimeOmni-1/TimeOmni-1-9B
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

🐏 TimeOmni-1-9B: Generalized Time Series Reasoning Model

We present TimeOmni-1, the first generalized, unified model for time series reasoning. It first injects temporal priors through supervised fine-tuning. Then, reinforcement learning with task-grounded rewards guides the model beyond mimicking priors toward robust reasoning. Experiments show that TimeOmni-1 achieves top-tier performance while preserving the general reasoning ability of the base model. Finally, we demonstrate that joint training across diverse reasoning tasks yields mutual gains, supporting a “train-once, use-across-tasks” paradigm for future time series reasoning models.

🎨 Task Illustration

🧠 Method

TimeOmni-1 is a generalized reasoning model for time series. Pretrained LLMs often lack temporal priors because they are rarely exposed to time series during pretraining. To address this, we use a two-stage training pipeline: (1) supervised fine-tuning (SFT) to inject temporal priors and anchor the model in a temporal knowledge space, and (2) reinforcement learning (RL) with task-grounded rewards (see Reward Evaluation in the figure above) to improve robustness and reasoning quality.

📊 Benchmarks

Table 1. Overall Benchmark Comparison

* Note: All metrics below are computed only on valid responses. “–” indicates a success rate (SR) below 10%; in such cases, results are omitted due to insufficient statistical significance, and we therefore do not report them. For ACC, higher is better; for MAE, lower is better.

	Task1 ID (ACC↑/SR)	Task1 OOD (ACC↑/SR)	Task2 ID (ACC↑/SR)	Task2 OOD (ACC↑/SR)	Task3 ID (MAE↓/SR)	Task3 OOD (MAE↓/SR)	Task4 ID (ACC↑/SR)	Task4 OOD (ACC↑/SR)
Time Series Language Model
Time-MQA Llama3-8B	32.2/29.5	25.1/32.6	30.1/44.3	31.2/37.2	-/1.4	-/0.4	12.0/13.3	11.6/15.8
Time-MQA Mistral-7B-v0.3	15.1/21.5	27.8/22.1	8.4/50.2	4.0/52.2	-/0.2	-/0.0	5.4/36.1	10.0/47.3
Time-MQA Qwen2.5-7B	25.0/14.0	37.5/22.7	29.5/33.0	30.5/32.0	19.76/12.2	-/6.5	23.8/58.0	26.4/44.3
ChatTS	-/6.0	-/6.9	18.2/30.1	18.6/26.7	-/0.0	-/0.0	5.8/27.1	11.1/27.1
ChatTime-7B-Chat	18.2/11.0	29.8/12.7	-/-	-/-	14.47/100.0	154.55/100.0	-/0.0	-/0.0
ITFormer-7B	43.8/100.0	47.5/100.0	15.0/47.0	14.6/42.0	29.55/96.0	230.04/100.0	25.0/100.0	41.7/100.0
OpenTSLM-llama-3.2-3b-ecg-flamingo	-/5.0	-/3.2	1.6/23.0	3.3/26.5	-/0.2	-/0.0	17.8/98.4	16.2/98.9
Time Series Reasoning Model
Time-R1	30.9/94.0	34.0/92.5	30.2/53.8	31.4/48.9	17.61/38.7	-/6.3	27.8/95.7	32.2/93.1
Ours
TimeOmni-1-9B	93.5/100.0	92.8/99.8	70.9/100.0	66.2/100.0	13.54/97.8	140.06/95.6	59.6/100.0	75.6/99.6

Table 2. Model Size Scaling Comparison

	Task1 ID (ACC↑/SR)	Task1 OOD (ACC↑/SR)	Task2 ID (ACC↑/SR)	Task2 OOD (ACC↑/SR)	Task3 ID (MAE↓/SR)	Task3 OOD (MAE↓/SR)	Task4 ID (ACC↑/SR)	Task4 OOD (ACC↑/SR)
7B (Qwen2.5-Instruct)
Qwen2.5-Instruct-7B	48.5/100.0	42.8/100.0	21.6/99.8	26.3/100.0	23.28/53.1	146.12/55.5	25.5/100.0	24.9/100.0
TimeOmni-1-7B	90.7/97.5	87.7/98.3	69.3/99.8	64.0/99.8	14.30/93.8	145.53/82.3	47.9/100.0	58.9/100.0
4B (Qwen3.5)
Qwen-3.5-4B	0.0/16.5	5.9/17.0	28.3/12.4	35.4/12.0	-/2.2	-/9.0	-/8.5	-/9.2
TimeOmni-1-4B	91.5/99.5	91.2/98.4	71.1/100.0	66.1/99.9	13.68/97.6	170.41/86.1	58.5/100.0	72.0/100.0
9B (Qwen3.5)
Qwen-3.5-9B	91.2/51.0	93.5/46.1	43.3/12.1	36.3/12.8	17.56/14.1	-/0.8	64.2/28.2	72.0/32.2
TimeOmni-1-9B	93.5/100.0	92.8/99.8	70.9/100.0	66.2/100.0	13.54/97.8	140.06/95.6	59.6/100.0	75.6/99.6

🚀 Usage

This repository hosts the model weights for TimeOmni-1. For installation, usage instructions, and further documentation, please visit our GitHub repository.

License

TimeOmni-1 is licensed under the Apache 2.0 license.

✍️ Citation

@inproceedings{
guan2026timeomni,
title={TimeOmni-1: Incentivizing Complex Reasoning with Time Series in Large Language Models},
author={Tong Guan and Zijie Meng and Dianqi Li and Shiyu Wang and Chao-Han Huck Yang and Qingsong Wen and Zuozhu Liu and Sabato Marco Siniscalchi and Ming Jin and Shirui Pan},
booktitle={The Fourteenth International Conference on Learning Representations},
year={2026},
url={https://openreview.net/forum?id=kOIclg7muL}
}