Instructions to use lllyx/Qwen3-4B-Base-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lllyx/Qwen3-4B-Base-GRPO with Transformers:

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

pipe = pipeline("text-generation", model="lllyx/Qwen3-4B-Base-GRPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lllyx/Qwen3-4B-Base-GRPO")
model = AutoModelForCausalLM.from_pretrained("lllyx/Qwen3-4B-Base-GRPO")
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 lllyx/Qwen3-4B-Base-GRPO with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lllyx/Qwen3-4B-Base-GRPO"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lllyx/Qwen3-4B-Base-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lllyx/Qwen3-4B-Base-GRPO

SGLang

How to use lllyx/Qwen3-4B-Base-GRPO 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 "lllyx/Qwen3-4B-Base-GRPO" \
    --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": "lllyx/Qwen3-4B-Base-GRPO",
		"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 "lllyx/Qwen3-4B-Base-GRPO" \
        --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": "lllyx/Qwen3-4B-Base-GRPO",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lllyx/Qwen3-4B-Base-GRPO with Docker Model Runner:
```
docker model run hf.co/lllyx/Qwen3-4B-Base-GRPO
```

Qwen3-4B-Base-GRPO

Qwen3-4B-Base-GRPO is a post-RL checkpoint trained with the verl framework. It starts from Qwen3-4B-Base and applies GRPO on the DAPO-Math-17k-Processed dataset for mathematical reasoning and problem-solving.

This model is associated with the paper:
Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe
Paper link: https://arxiv.org/abs/2604.13016

Model Description

This model is obtained by applying GRPO reinforcement learning to Qwen3-4B-Base with verl. The training is intended to improve math-focused reasoning performance under the on-policy distillation setting.

Key characteristics

Base model: Qwen3-4B-Base
Training framework: verl
Training stage: Reinforcement Learning (GRPO)
Parameter update: Full-parameter actor update
Primary domain: Mathematical reasoning
Reward model: Not used (reward_model.enable: false)
Rollout engine: vLLM
Context length: 32768 tokens
Responses per prompt: 8

Training Details

Training configuration

Framework: verl
Algorithm: grpo
GRPO outcome weight: 1.0
Learned reward model: disabled (reward_model.enable: false)
Reward source: custom rule-based math reward function
Training dataset: DAPO-Math-17k-Processed
Training file: datasets/DAPO-Math-17k-Processed/DAPO-Math.parquet
Validation datasets: AIME25, AMC23, AIME24
Prompt length: 1024
Response length: 7168
Validation response length: 31744
Max model length: 32768
Rollout temperature: 1.0
Repetition penalty: 1.0
KL loss: disabled
Format reward: disabled
Loss aggregation: token-mean
Learning rate: 1e-6
PPO mini-batch size: 64
PPO micro-batch size per GPU: 1
Tensor parallel size: 1
Number of GPUs: 8
Number of epochs: 1
Save frequency: every 20 steps
Test frequency: every 20 steps

Dataset

Training dataset: DAPO-Math-17k-Processed
Validation datasets: AIME25, AMC23, AIME24

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "lllyx/Qwen3-4B-Base-GRPO"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

Citation

If you use this model, please consider citing the related paper:

@article{li2026rethinking,
  title={Rethinking On-Policy Distillation of Large Language Models: Phenomenology, Mechanism, and Recipe},
  author={Li, Yaxuan and Zuo, Yuxin and He, Bingxiang and Zhang, Jinqian and Xiao, Chaojun and Qian, Cheng and Yu, Tianyu and Gao, Huan-ang and Yang, Wenkai and Liu, Zhiyuan and Ding, Ning},
  journal={arXiv preprint arXiv:2604.13016},
  year={2026}
}