Instructions to use ulab-ai/Time-R1-S1P1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ulab-ai/Time-R1-S1P1 with Transformers:

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

pipe = pipeline("text-generation", model="ulab-ai/Time-R1-S1P1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ulab-ai/Time-R1-S1P1")
model = AutoModelForCausalLM.from_pretrained("ulab-ai/Time-R1-S1P1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ulab-ai/Time-R1-S1P1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ulab-ai/Time-R1-S1P1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ulab-ai/Time-R1-S1P1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ulab-ai/Time-R1-S1P1

SGLang

How to use ulab-ai/Time-R1-S1P1 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 "ulab-ai/Time-R1-S1P1" \
    --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": "ulab-ai/Time-R1-S1P1",
		"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 "ulab-ai/Time-R1-S1P1" \
        --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": "ulab-ai/Time-R1-S1P1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ulab-ai/Time-R1-S1P1 with Docker Model Runner:
```
docker model run hf.co/ulab-ai/Time-R1-S1P1
```

nielsr HF Staff commited on Jun 3, 2025

Commit

01315bf

verified ·

1 Parent(s): d261c4a

Improve model card: add project page link

Browse files

This PR adds a link to the project page and ensures consistent formatting across links and emojis in the model card.

Files changed (1) hide show

README.md +10 -9

README.md CHANGED Viewed

@@ -3,23 +3,23 @@ base_model:
 - Qwen/Qwen2.5-3B-Instruct
 datasets:
 - ulab-ai/Time-Bench
 license: apache-2.0
 tags:
 - temporal-reasoning
 - reinforcement-learning
 - large-language-models
 paperswithcode:
   arxiv_id: 2505.13508
-library_name: transformers
-pipeline_tag: text-generation
 ---
 <center>
-    <img src="https://cdn-uploads.huggingface.co/production/uploads/65d188a4aa309d842e438ef1/d6YiWBndm7WzANfl3e1qi.png" alt="Output Examples" width="600">
 </center>
 <div align="center">
-<a href="https://huggingface.co/datasets/ulab-ai/Time-Bench"> 📊 <strong>Dataset</strong></a> | <a href="https://github.com/ulab-uiuc/Time-R1">🚀 <strong>Code</strong></a> | <a href="https://arxiv.org/abs/2505.13508">📖 <strong>Paper</strong></a>
 </div>
 # Time-R1 Model Series
@@ -74,8 +74,9 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
 ## Citations
 ```bibtex
 @article{liu2025time,
-  title={Time-R1: Towards Comprehensive Temporal Reasoning in LLMs},
-  author={Liu, Zijia and Han, Peixuan and Yu, Haofei and Li, Haoru and You, Jiaxuan},
-  journal={arXiv preprint arXiv:2505.13508},
-  year={2025}
-}

 - Qwen/Qwen2.5-3B-Instruct
 datasets:
 - ulab-ai/Time-Bench
+library_name: transformers
 license: apache-2.0
+pipeline_tag: text-generation
 tags:
 - temporal-reasoning
 - reinforcement-learning
 - large-language-models
 paperswithcode:
   arxiv_id: 2505.13508
 ---
 <center>
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/65d188a4aa309d842e438ef1/d6YiWBndm7WzANfl3e1qi.png" alt="Output Examples" width="600">
 </center>
 <div align="center">
+<a href="https://huggingface.co/datasets/ulab-ai/Time-Bench"> 📊 <strong>Dataset</strong></a> | <a href="https://github.com/ulab-uiuc/Time-R1">🚀 <strong>Code</strong></a> | <a href="https://arxiv.org/abs/2505.13508">📖 <strong>Paper</strong></a> | <a href="https://sites.google.com/view/eagle-llm">🌐 <strong>Project Page</strong></a>
 </div>
 # Time-R1 Model Series
 ## Citations
 ```bibtex
 @article{liu2025time,
+  title={Time-R1: Towards Comprehensive Temporal Reasoning in LLMs},
+  author={Liu, Zijia and Han, Peixuan and Yu, Haofei and Li, Haoru and You, Jiaxuan},
+  journal={arXiv preprint arXiv:2505.13508},
+  year={2025}
+}
+```