Instructions to use divelab/DAPO_E2H-countdown-gaussian_0p5_0p5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use divelab/DAPO_E2H-countdown-gaussian_0p5_0p5 with Transformers:

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

pipe = pipeline("text-generation", model="divelab/DAPO_E2H-countdown-gaussian_0p5_0p5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("divelab/DAPO_E2H-countdown-gaussian_0p5_0p5")
model = AutoModelForCausalLM.from_pretrained("divelab/DAPO_E2H-countdown-gaussian_0p5_0p5")
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 divelab/DAPO_E2H-countdown-gaussian_0p5_0p5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "divelab/DAPO_E2H-countdown-gaussian_0p5_0p5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "divelab/DAPO_E2H-countdown-gaussian_0p5_0p5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/divelab/DAPO_E2H-countdown-gaussian_0p5_0p5

SGLang

How to use divelab/DAPO_E2H-countdown-gaussian_0p5_0p5 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 "divelab/DAPO_E2H-countdown-gaussian_0p5_0p5" \
    --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": "divelab/DAPO_E2H-countdown-gaussian_0p5_0p5",
		"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 "divelab/DAPO_E2H-countdown-gaussian_0p5_0p5" \
        --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": "divelab/DAPO_E2H-countdown-gaussian_0p5_0p5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use divelab/DAPO_E2H-countdown-gaussian_0p5_0p5 with Docker Model Runner:
```
docker model run hf.co/divelab/DAPO_E2H-countdown-gaussian_0p5_0p5
```

Model Card for Qwen2.5-1.5B-Instruct_math_grpo_cosine_0.5_0.5_SEC0.3DRO1.0G0.0_minpTrue_1600

This model is a fine-tuned version of Qwen/Qwen2.5-1.5B-Instruct on the Countdown dataset. It has been trained using E2H on the top of TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shubhamprshr/Qwen2.5-1.5B-Instruct_math_grpo_cosine_0.5_0.5_SEC0.3DRO1.0G0.0_minpTrue_1600", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with GRPO, a method introduced in DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

Framework versions

TRL: 0.19.1
Transformers: 4.53.1
Pytorch: 2.7.0
Datasets: 3.6.0
Tokenizers: 0.21.4

Citations

Cite E2H as:

@inproceedings{parashar2026curriculum,
  title     = {Curriculum Reinforcement Learning from Easy to Hard Tasks Improves {LLM} Reasoning},
  author    = {Parashar, Shubham and Gui, Shurui and Li, Xiner and Ling, Hongyi and Vemuri, Sushil and Olson, Blake and Li, Eric and Zhang, Yu and Caverlee, James and Kalathil, Dileep and Ji, Shuiwang},
  booktitle = {The Fourteenth International Conference on Learning Representations},
  year      = {2026},
  url       = {https://openreview.net/forum?id=KJvHnl3kUv}
}