Instructions to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Code 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-Code 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-Code")
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-Code")
model = AutoModelForCausalLM.from_pretrained("BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Code", 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-Code 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-Code"
# 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-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

Open-MOPD-SmolLM3-3B-RL-Code

This is the code-domain teacher in the Open-MOPD pipeline. It starts from BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT and is trained only on code prompts with verifiable rewards using GRPO. This release corresponds to training step 180.

Training uses global batch size 128, mini-batch size 32, learning rate 1e-6, rollout group size 16, a 30,000-token response limit, no KL penalty, and accuracy-based group filtering.

Results

Model	LiveCodeBench v5	LiveCodeBench v6	Code average
RL-Code teacher	22.16	21.31	21.73
MixSFT starting point	15.99	19.20	17.60

Results use avg@10 rather than best@10, with temperature 1.0, max_model_len=32768, top_p=0.95, top_k=-1, and stop_token_ids=[128012].

The code portion of the RL prompt mixture explicitly excludes LiveCodeBench. The decontamination record is available in BytedTsinghua-SIA/Open-MOPD-Data under rl_prompt_mix/manifest.json.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-RL-Code"
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 code and can perform worse than MixSFT on other domains.