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
```

Add library name and pipeline tag

by nielsr HF Staff - opened Jun 2, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

nielsr

Jun 2, 2025

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.

Add library name and pipeline taga65a6717

m-serious changed pull request status to merged Jun 2, 2025

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