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 / README.md

c7w

Update English model card

5e901bb verified 2 days ago

preview code

Raw

History Blame Contribute Delete

1.92 kB

	---
	license: apache-2.0
	base_model: BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT
	datasets:
	- BytedTsinghua-SIA/Open-MOPD-Data
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- smollm3
	- open-mopd
	- reinforcement-learning
	- 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

	```python
	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.

	## Model specifications

	- Architecture: `SmolLM3ForCausalLM`
	- Parameters: approximately 3B
	- Layers: 36
	- Vocabulary size: 128,256
	- Weights: BF16, approximately 6.2 GB
	- Includes tokenizer and chat template