Instructions to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math with Transformers:

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

pipe = pipeline("text-generation", model="BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math")
model = AutoModelForCausalLM.from_pretrained("BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math", device_map="auto")
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 BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math

SGLang

How to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math 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 "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math" \
    --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": "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math",
		"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 "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math" \
        --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": "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math with Docker Model Runner:
```
docker model run hf.co/BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math
```

Open-MOPD-SmolLM3-3B-RL-Math

This is the math-domain teacher in the Open-MOPD pipeline. It starts from BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT and is trained only on math prompts with verifiable rewards using GRPO. This release corresponds to training step 100.

Training uses global batch size 128, mini-batch size 32, constant learning rate 1e-6 with 10 warmup steps, clipping at 0.2/0.25, rollout group size 16, temperature 1.0, a 30,000-token response limit, and no KL penalty. Groups with all-correct or all-incorrect generations are filtered, with up to eight resampling attempts.

Results

Model	AIME24	AIME25	Math average
RL-Math teacher	23.65	24.84	24.24
MixSFT starting point	15.63	20.26	17.95

Math results use avg@64 with temperature 0.6. The broader evaluation setup uses max_model_len=32768, top_p=0.95, top_k=-1, and stop_token_ids=[128012].

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Math"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")

Intended use and limitations

This is a domain teacher intended for distillation, not a general-purpose assistant. It was optimized only on math and can perform worse than MixSFT on other domains.