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
```

lllyx commited on 7 days ago

Commit

e59bf75

verified ·

1 Parent(s): 513e04f

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README.md +18 -1

README.md CHANGED Viewed

@@ -24,6 +24,23 @@ base_model_relation: finetune
 # 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.
@@ -109,4 +126,4 @@ If you use this model, please consider citing the related paper:
   journal={arXiv preprint arXiv:2604.13016},
   year={2026}
 }
-```

 # Qwen3-4B-Base-GRPO
+<div align="center" style="line-height: 1;">
+  <a href="https://arxiv.org/abs/2604.13016" style="margin: 2px;">
+    <img alt="Paper" src="https://img.shields.io/badge/paper-A42C25?style=for-the-badge&logo=arxiv&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://github.com/thunlp/OPD" style="margin: 2px;">
+    <img alt="Github" src="https://img.shields.io/badge/OPD-000000?style=for-the-badge&logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://huggingface.co/papers/2604.13016" style="margin: 2px;">
+    <img alt="HF Papers" src="https://img.shields.io/badge/HF--Paper-%23FFD14D?style=for-the-badge&logo=huggingface&logoColor=black" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+  <a href="https://x.com/HBX_hbx/status/2044464414829777354" style="margin: 2px;">
+    <img alt="Twitter" src="https://img.shields.io/badge/Twitter-%23000000.svg?style=for-the-badge&logo=x&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
+  </a>
+</div>
+<br>
 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.
   journal={arXiv preprint arXiv:2604.13016},
   year={2026}
 }
+```