Instructions to use ulab-ai/Time-R1-Theta1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ulab-ai/Time-R1-Theta1 with Transformers:

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

pipe = pipeline("text-generation", model="ulab-ai/Time-R1-Theta1")
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-Theta1")
model = AutoModelForCausalLM.from_pretrained("ulab-ai/Time-R1-Theta1")
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-Theta1 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-Theta1"
# 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-Theta1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ulab-ai/Time-R1-Theta1

SGLang

How to use ulab-ai/Time-R1-Theta1 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-Theta1" \
    --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-Theta1",
		"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-Theta1" \
        --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-Theta1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ulab-ai/Time-R1-Theta1 with Docker Model Runner:
```
docker model run hf.co/ulab-ai/Time-R1-Theta1
```

Replace Arxiv paper link with Hugging Face paper link

by nielsr HF Staff - opened Jun 3, 2025

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

-6

Files changed (1) hide show

README.md +7 -6

README.md CHANGED Viewed

@@ -3,15 +3,15 @@ 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>
@@ -19,12 +19,12 @@ pipeline_tag: text-generation
 </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
-This collection hosts the official checkpoints for the **Time-R1** model, as described in the paper "Time-R1: Towards Comprehensive Temporal Reasoning in LLMs". Time-R1 is a 3B parameter Large Language Model trained with a novel three-stage reinforcement learning curriculum to endow it with comprehensive temporal abilities: understanding, prediction, and creative generation.
 These models are trained using the [Time-Bench dataset](https://huggingface.co/datasets/ulab-ai/Time-Bench).
@@ -52,7 +52,7 @@ This model builds upon Stage 1 capabilities to predict future event timings.
 * **[Time-R1-Theta2](https://huggingface.co/ulab-ai/Time-R1-Theta2):** Checkpoint θ₂, after Stage 2 training.
    * *Focus: Predicting the timing of future events occurring after its initial knowledge cutoff.*
-Please refer to the [main paper](https://arxiv.org/abs/2505.13508) for detailed discussions on the architecture, training methodology, and comprehensive evaluations.
 ## How to Use
@@ -76,4 +76,5 @@ model = AutoModelForCausalLM.from_pretrained(model_name)
   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>
 </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://huggingface.co/papers/2505.13508">📖 <strong>Paper</strong></a>
 </div>
 # Time-R1 Model Series
+This collection hosts the official checkpoints for the **Time-R1** model, as described in the paper [Time-R1: Towards Comprehensive Temporal Reasoning in LLMs](https://huggingface.co/papers/2505.13508). Time-R1 is a 3B parameter Large Language Model trained with a novel three-stage reinforcement learning curriculum to endow it with comprehensive temporal abilities: understanding, prediction, and creative generation.
 These models are trained using the [Time-Bench dataset](https://huggingface.co/datasets/ulab-ai/Time-Bench).
 * **[Time-R1-Theta2](https://huggingface.co/ulab-ai/Time-R1-Theta2):** Checkpoint θ₂, after Stage 2 training.
    * *Focus: Predicting the timing of future events occurring after its initial knowledge cutoff.*
+Please refer to the [main paper](https://huggingface.co/papers/2505.13508) for detailed discussions on the architecture, training methodology, and comprehensive evaluations.
 ## How to Use
   author={Liu, Zijia and Han, Peixuan and Yu, Haofei and Li, Haoru and You, Jiaxuan},
   journal={arXiv preprint arXiv:2505.13508},
   year={2025}
+}
+```