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 Settings

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 2, 2025

Commit

a65a671

verified ·

1 Parent(s): f8acc2f

Add library name and pipeline tag

Browse files

This PR improves the model card by adding the library name and pipeline tag.
This ensures the "how to use" button is displayed at the top right of the page and that the model is discoverable at https://huggingface.co/models?pipeline_tag=text-generation.

Files changed (1) hide show

README.md +7 -4

README.md CHANGED Viewed

@@ -1,18 +1,21 @@
 ---
-license: apache-2.0
-datasets:
-- ulab-ai/Time-Bench
 base_model:
 - Qwen/Qwen2.5-3B-Instruct
 tags:
 - temporal-reasoning
 - reinforcement-learning
 - large-language-models
 paperswithcode:
-  arxiv_id: 2505.13508
 model_index:
 - name: Time-R1-S1P1
 ---
 <center>
     <img src="https://cdn-uploads.huggingface.co/production/uploads/65d188a4aa309d842e438ef1/d6YiWBndm7WzANfl3e1qi.png" alt="Output Examples" width="600">
 </center>

 ---
 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
 model_index:
 - name: Time-R1-S1P1
+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>